|
Deep Learning Publications
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
Chemically Informed Deep Learning for Interpretable Radical Reaction Prediction M. Tavakoli, Y. T. Chiu, A. M. Carlton, D. Van Vranken, P. Baldi. Journal of Chemical Information and Modeling., 65, ( 3 ), 1228-1242, 2025.
Evaluating the Intelligence of Large Language Models: A Comparative Study Using Verbal and Visual IQ Tests S. Abdelkarim, S. Jaeggi, and P. Baldi. Computers in Human Behavior: Artificial Humans., 5, 2025.
Optimization of Antenna Array Configurations Using Deep Learning D. Lin, J. Earls, L. Ma, A. Boag, and P. Baldi. IEEE Open Journal of Antennas and Propagation., 1, ( 1 ), 99, 2025.
Precision Polishing of Ablator Capsules via in situ Process Monitoring and Machine Learning–Based Optimization A. Tiwari, S. Jin, S. Galla, B. Botcha, S. Hayes, M. Biener, K. Bhardwaj, S. Bukkapatnam, Y. Ding, A. Antonios, P. Baldi, S. Bhandarkar. Fusion Science and Technology., 81, ( 3 ), 2025.
Improving Deep Learning Speed and Performance through Synaptic Neural Balance. The Thirty-Ninth AAAI Conference on Artificial Intelligence A. Alexos, I. Domingo, P. Baldi. Proceedings of the AAAI Conference on Artificial Intelligence., 2025.
Realizable Continuous-Space Shields for Safe Reinforcement Learning K. Kim, D. Corsi, A. Rodriguez, J.B. Lanier, B. Parellada, P. Baldi, C. Sanchez, R. Fox. 7th Annual Learning for Dynamics & Control Conference ., 2025.
Memorization: A Close Look at Books Iris Ma, Ian Domingo, Alberto Krone-Martins, Pierre Baldi, and Cristina Lopes. First Workshop on Large Language Model Memorization., 2025.
Clinical Knowledge and Reasoning Abilities of AI Large Language Models in Anesthesiology: A Comparative Study on the American Board of Anesthesiology Examination Mirana C Angel, Joseph Rinehart, Maxime Cannesson, and Pierre Baldi. Anesthesia & Analgesia., 139, ( 2 ), 349-356, 2024.
PMechDB: A Public Database of Elementary Polar Reaction Steps Tavakoli, Mohammadamin; Miller, Ryan; Angel, Mirana; Pfeiffer, Michael ; Gutman, Eugene; Mood, Aaron; Van Vranken, David; Baldi, Pierre. . Journal of Chemical Information and Modeling., 64, ( 6 ), 2024.
Reconstruction of Unstable Heavy Particles Using Deep Symmetry-Preserving Attention Networks M. Fenton, A. Schmakov, H. Okawa, Y. Li, K. Hsiao, S. Hsu, D. Whiteson, P. Baldi. Nature Communications Physics., 2024, ( 7 ), 2024.
Nuclear fusion diamond polishing dataset A. Alexos, J. Liu, S. Galla, S. Hayes, K. Bhardwaj, A. Schwartz, M. Biener, P. Baldi, S. Bukkapatnam, S. Bhandarkar. Advances in Neural Information Processing Systems., 2024.
Diamond Polishing Data Set A Alexos, J Liu, S Galla, S Hayes, K Bhardwaj, A Schwartz, M Biener, P Baldi, S Bukkapatnam, S Bhandarkar. The Thirty-Eighth Annual Conference on Neural Information Processing Systems, NeurIPS 2024., 2024.
Consistent and intermittent exercise patterns that facilitate long-term memory formation identify ACVR1C as a bidirectional regulator of memory A. Keiser, T. Dong, H. Shaikh, S. La Tour, D. Matheos, E. Kramar, C. Butler, S. Chen, J. Beardwood, A. Augustynski, A. Al-Shammari, Y. Alaghband, V. Alizo Vera, N. Berchtold, S. Shanur, P. Baldi, C. Cotman, M. Wood. Neuropshychopharmacology., 2024.
Q* search: Heuristic search with deep q-networks F. Agostinelli, S. Shperberg, A. Shmakov, S. McAleer, R. Fox, P. Baldi. ICAPS Workshop on Bridging the Gap between AI Planning and Reinforcement Learning, Banff, Alberta, Canada., 2024.
Toward optimal policy population growth in two-player zero-sum games SM. McAleer, JB. Lanier, K. Wang, P. Baldi, T. Sandholm, R.Fox. 12th International Conference on Learning Representations (ICLR)., 2024.
Fourier Guided Transformer for Diffusion Haodong Zhang, Zhe Yuan, Antonios Alexos, Pierre Baldi. Conference on Computer Vision and Pattern Recognition 2024., 2024.
Deep Learning Overparameterization: the Shallow Fallacy Pierre Baldi. Northern Lights Deep Learning Conference, Tromso, Norway., 2024.
Speech editing by hitch-hiking a pre-trained FastSpeech model Antonios Alexos and Pierre Baldi. Northern Lights Deep Learning Conference, Tromso, Norway., 2024.
Deep learning models of the discrete component of the galactic interstellar gamma-ray emission Alexander Shmakov, Mohammadamin Tavakoli, Pierre Baldi, Christopher M. Karwin, Alex Broughton, Simona Murgia. Physical Review D., 107, ( 6 ), 210, 2023.
RMechDB: A Public Database of Elementary Radical Reaction Steps Tavakoli, Mohammadamin; Chiu, Yin Ting; Baldi, Pierre; Carlton, Annmarie; Van Vranken, David. . Journal of Chemical Information and Modeling., 63, ( 4 ), 114-1123, 2023.
Vitreoretinal surgical instrument tracking in 3-Dimensions using Deep Learning Pierre Baldi, Junze Liu, Sherif Abdelkarim, Josiah K To, Marialejandra Diaz Ibarra and Andrew W. Browne. Translational Vision Science and Technology., 12, ( 20 ), 2023.
Deducing Neutron Star Equation of State Parameters Directly From Telescope Spectra with Uncertainty-Aware Machine Learning Delaney Farrell, Pierre Baldi, Jordan Ott, Aishik Ghosh, Andrew W. Steiner, Atharva Kavitkar, Lee Lindblom, Daniel Whiteson, and Fridolin Weber. Journal of Cosmology and Astroparticle Physics., 16, ( 2 ), 2023.
Deducing the EOS of Dense Neutron Star Matter with Machine Learning Delaney Farrell, Pierre Baldi, Jordan Ott, Aishik Ghosh, Andrew W. Steiner, Atharva Kavitkar, Lee Lindblom, Daniel Whiteson, and Fridolin Weber. Astronomische Nachrichten., ( 344 ), 1-2, 2023.
The Quarks of Attention: Structure and Capacity of Neural Attention Building Blocks P. Baldi and R. Vershynin. Artificial Intelligence., 319, 2023.
Generalizing to new geometries with Geometry-Aware autoregressive Models (GAAMs) for fast calorimeter simulation Junze Liu, Aishik Ghosh, Dylan Smith, Pierre Baldi, and Daniel Whiteson. Journal of Instrumentation (JINST)., 18, 2023.
Deep Learning Assisted Imaging Methods to Facilitate Access to Ophthalmic Telepathology, Andrew W. Browne, Geunwoo Kim, Anderson N. Vu, Josiah K. To, Don S. Minckler, Maria Del Valle Estopina, Narsing A. Rao, Christine A. Curcio, Pierre F. Baldi. Ophthalmology Science., 4, ( 3 ), 2023.
Evaluating the Performance of Large Language Models for Spanish Language in Undergraduate Admissions Exams Sabino Miranda, Obdulia Pichardo-Lagunas, Bella Martinez-Seis, Pierre Baldi. Computacion y Sistemas., 2023.
Recommending Anesthesia Dosages with Neural Networks K. Sarullo, M.Samad, S. Kendale, S. Swamidass, P. Baldi. Conference Anesthesia and Analgesia., 2023.
Machine learning-enhanced prediction of surface smoothness for inertial confinement fusion target polishing using limited data A. Alexos, J. Liu, A. Tiwari, K. Bhardwaj, S. Hayes, P. Baldi, S. Bukkapatnam, S. Bhandarkar. The 4th Workshop on Artificial Intelligence and Machine Learning for Scientific Applications., 2023.
Interpretable Joint Event-Particle Reconstruction for Neutrino Physics at NOvA with Sparse CNNs and Transformers A. Shmakov, A. Yankelevich, J. Bian, P. Baldi. Thirty-seventh Conference on Neural Information Processing Systems, NeurIPS ., 2023.
AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning Mohammadamin Tavakoli, Pierre Baldi, Ann Marie Carlton, Yinting Chiu, Alexander Shmakov, David Van Vranken. Thirty-seventh Conference on Neural Information Processing Systems, NeurIPS., 2023.
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation Sungduk Yu, Walter Hannah, Liran Peng, Jerry Lin, Mohamed Aziz Bhouri, Ritwik Gupta, Bjorn Lutjens, Justus Christopher Will, Gunnar Behrens, Julius Busecke, Nora Loose, Charles I Stern, Tom Beucler, Bryce Harrop, Benjamin R Hillman, Andrea Jenney, Savanna. Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track, NeurIPS., 2023.
End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics Alexander Shmakov, Kevin Greif, Michael James Fenton, Aishik Ghosh, Pierre Baldi, Daniel Whiteson. Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track, NeurIPS., 2023.
Language ModelsCann Solve Computer Tasks Geunwoo Kim, Pierre Baldi, Stephen Marcus McAleer. Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track, NeurIPS., 2023.
Selective Perception: Learning Concise State Descriptions for Language Model Actors Kolby Nottingham, Yasaman Razeghi, Kyungmin Kim, JB Lanier, Pierre Baldi, Roy Fox, Sameer Singh. Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track, NeurIPS., 2023.
AI and Veterinary Medicine: Performance of Large Language Models on the North American Licensing Examination Mirana Angel, Anuj Patel, Haiyi Xing, Dylan Balsz, Cody Arbuckle, David Bruyette and Pierre Baldi. IEEE International Symposium on Foundation and Large Language Models, FLLM 2023., 2023.
Clinical Knowledge and Reasoning Abilities of Large Language Models in Pharmacy: A Comparative Study on the NAPLEX Exam Mirana Angel, Anuj Patel, Amal Alachkar and Pierre Baldi. IEEE International Symposium on Foundation and Large Language Models, FLLM 2023., 2023.
Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural Networks to Predict Chemical Reactivity A. Tavakoli, A. Mood, D. Van Vranken, and P. Baldi. Journal of Chemical Information and Modeling., 62, ( 9 ), 2022.
SSpro/ACCpro 6: Almost Perfect Prediction of Protein Secondary Structure and Relative Solvent Accessibility Using Profiles, Deep Learning, and Structural Similarity G. Urban, C. Magnan, and P. Baldi. Bioinformatics., ( 7 ), 2064-2065, 2022.
Real-time reconstruction of high energy, ultrafast laser pulses using deep learning M. Stanfield, J. Ott, C. Gardner, N. F. Beier, D. Farinella, C. A. Mancuso, P. Baldi, and F. Dollar. Nature Scientific Reports,., 12, 5299, 2022.
Deep Learning to Enable Color Vision in the Dark Andrew W. Browne, Ekaterina Deyneka, Francesco Ceccarelli, Siwei Chen, Josiah K. To, Jianing Tang, Anderson N. Vu, Pierre Baldi. PLOS ONE., 17, ( 4 ), 2022.
Weakly Supervised Polyp Segmentation in Colonoscopy Images using Deep Neural Networks Siwei Chen, Gregor Urban, and Pierre Baldi. Journal of Imaging., 8, ( 5 ), 121, 2022.
Improving sensitivity of the ARIANNA detector by rejecting thermal noise with deep learning A. Anker, P. Baldi, S. W. Barwick, J. Beise, et al. Journal of Instrumentation., 17, ( 3 ), 2022.
Feasible Adversarial Robust Reinforcement Learning for Underspecified Environments J, Lanier, S. McAleer, P. Baldi, and R. Fox. NeurIPS 2022 ., 2022.
Geometry-aware Autoregressive Models for Calorimeter Shower Simulations J. Liu, A. Ghosh, D. Smith, P, Baldi, and D. Whiteson. NeurIPS 2022 ., 2022.
Anytime PSRO for two-player zero-sum gam S. McAleer, K. Wang, J. Lanier, M. Lanctot, P. Baldi, T. Sandholm, R. Fox. The 36th AAAI Conference on Artificial Intelligence Reinforcement Learning in Games Workshop, Vancouver, BC, Canada., 2022.
Foundations of Attention Mechanisms in Deep Neural Network Architectures. P. Baldi and R. Vershynin. Attention Workshop, NeurIPS 2022., 2022.
Anytime Optimal PSRO for Two-Player Zero-Sum Games Stephen McAleer, Kevin Wang, Marc Lanctot, John Lanier, Pierre Baldi, and Roy Fox. AAAI 2022 Workshop on Reinforcement Learning in Games., 2022.
Deep Learning From Four-Vectors P. Baldi, P. Sadowski, and D. Whiteson. Artificial Intelligence Applied to Particle Physics ., 2022.
Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models with Real-Geography Boundary Conditions Griffin Mooers, Michael Pritchard, Tom Beucler, Jordan Ott, Galen Yacalis, Pierre Baldi, Pierre Gentine. Journal of Advances in Modeling Earth Systems., 15, ( 3 ), 2021.
Detecting Pulmonary Coccidioidomycosis with Deep Convolutional Neural Networks J. Ott, D. Bruyetter, C. Arbuckle, D. Balsz, S. Hecth, L. Shubitz, and P. Baldi. . Machine Learning with Applications., ( 5 ), 2021.
Learning to identify electrons Julian Collado, Jessica N. Howard, Taylor Faucett, Tony Tong, Pierre Baldi, and Daniel Whiteson. Physical Review D., 103, ( 11 ), 2021.
Deep Learning–Assisted Multiphoton Microscopy to Reduce Light Exposure and Expedite Imaging in Tissues With High and Low Light Sensitivity Stephen McAleer; Alexander Fast; Yuntian Xue; Magdalene J. Seiler; William C. Tang; Mihaela Balu; Pierre Baldi; Andrew W. Browne. Translational Vision Science & Technology., 10, ( 30 ), 2021.
Learning to Isolate Muons Julian Collado, Kevin Bauer, Edmund Witkowski, Taylor Faucett, Daniel Whiteson, and Pierre Baldi. Journal of High Energy Physics., 2021, ( 200 ), 2021.
Obtaining approximately admissible heuristic functions through deep reinforcement learning and A* search. Bridging the Gap between AI Planning and Reinforcement F. Agostinelli, S. McAleer, A. Shmakov, R. Fox, M. Valtorta, B. Srivastava, P. Baldi. Learning workshop at International Conference on Automated Planning and Scheduling, Aug 2021., 2021.
Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation Mohammadamin Tavakoli, Alexander Shmakov, Francesco Ceccarelli and Pierre Baldi. The Seventh International Workshop on Deep Learning on Graphs: Methods and Applications (AAAI--DLG22)., 2021.
XDO: A Double Oracle Algorithm for Extensive-Form Games S. McAleer, F. Agostinelli, A. Shmakov, R. Fox, and P. Baldi. Proceedings of the NeurIPS 2021 Conference., 2021.
CFR-DO: A double oracle algorithm for extensive-form games S. McAleer, J. Lanier, P. Baldi, R. Fox. The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21) Workshop on Reinforcement Learning in Games., 2021.
Deep Learning in Science P. Baldi. , Cambridge University Press , 2021.
Training and Validation of Deep Neural Networks for the Prediction of 90-Day Post-Liver Transplant Mortality Using UNOS Registry Data Lee C., Wray C., Agopian V., Urban G., Baldi P., Cannesson M., Ershoff B. Transplantation Proceedings., 52, ( 1 ), 246-258, 2020.
Development and Validation of a Deep Neural Network Model to Predict Postoperative Mortality, Acute Kidney Injury, and Reintubation using a single feature set Hofer I.S., Lee C., Gabel E., Baldi P., Cannesson M. npj Digital Medicine., 1, ( 58 ), 2020.
A Fortran-Keras Deep Learning Bridge for Scientific Computing J. Ott, M. Pritchard, N. Best, E. Linstead, M. Curcic, and P. Baldi. Scientific Programming., 2020.
Deep Learning Analysis of Vibrational Spectra of Bacterial Lysate for Rapid Antimicrobial Susceptibility Testing William John Thrift, Sasha Ronaghi, Muntaha Samad, Hong Wei, Dean Gia Nguyen, Antony Superio Cabuslay, Chloe E Groome, Peter Joseph Santiago, Pierre Baldi, Allon I Hochbaum, Regina Ragan
. ACS., 14, ( 11 ), 2020.
Deep-learning-based kinematic reconstruction for DUNE Liu, J. Ott, J. Collado, B. Jargowsky, W. Wu, J. Bian, P. Baldi. Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020)., 2020.
Postoperative In-Hospital Mortality Prediction Using Bayesian Neural Networks for Interpretability M. Samad, C. Lee, I. Hofer, P. Baldi, M. Cannesson. Conference ANESTHESIA AND ANALGESIA., 2020.
An Interpretable Neural Network for Prediction of Postoperative In-hospital Mortality C. Lee, M. Samad, I. Hofer, P. Baldi, M. Cannesson. Conference ANESTHESIA AND ANALGESIA., 2020.
Assessing the potential of deep neural networks for emulating cloud superparameterization in climate models under real geography boundary conditions G. Mooers, M. S. Pritchard, T. Beucler, J. Ott, G. Yacalis, P. Baldi, P. Gentin. The American Geophysical Union (AGU) Fall Meeting 2020., 2020.
Gentin.Assessing the potential of deep neural networks for emulating cloud superparameterization in climate models under real geography boundary conditions J. Ott, M. S. Pritchard, N. Best, E. Linstead, M. Curcic, P. Baldi. The American Geophysical Union (AGU) Fall Meeting 2020., 2020.
Hyperparameter Optimization and a Deep Learning Bridge to Fortran M. S. Pritchard, T. Beucler, G. Mooers, J. Ott, P. Gentine, L. Peng, P. Baldi, S. K. Mukkavilli. The American Geophysical Union (AGU) Fall Meeting 2020., 2020.
Deep Bucket Elimination Y. Razeghi, S. Kask, Y. Lu, P. Baldi, S. Agarwal, R. Dechter. 30th International Joint Conference on Artificial Intelligence., 2020.
Deep-Learning-Based Kinematic Reconstruction for DUNE J. Liu, J. Ott, J. Collado, B. Jargowsky, W. Wu, J. Bian, P. Baldi. 34th Conference on Neural Information Processing Systems (NeurIPS), Machine Learning and the Physical Sciences Workshop, (2020)., 2020.
ColosseumRL: A Framework for Multiagent Reinforcement Learning in N-Player Games. Shmakov, A., Lanier, J., McAleer, S., Achar, R., Lopes, C.V. and Baldi, P. Proceedings of Challenges and Opportunities for Multi-Agent Reinforcement Learning (COMARL AAAI)., 2020.
Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games S. McAleer, J. B. Lanier, R. Fox, and P. Baldi. Proceedings of the NeurIPS 2020 Conference., 2020.
Neural Network Gradient Hamiltonian Monte Carlo Li, L., Holbrook, A.J., Shahbaba, B., Baldi, P. Computational Statistics., 34, 281-299, 2019.
Improved energy reconstruction in NOvA with regression convolutional neural network Baldi, P., Bian, J., Hertel, L., et al. Physical Review D., 99, ( 12011 ), 2019.
First measurement of neutrino oscillation parameters using neutrinos and antineutrinos by NOvA Acero, M.A., et al. Physical Review Letters., 2019.
Solving the Rubik’s cube with deep reinforcement learning and search Agostinelli, F., McAleer, S., Shmakov, A.K., Baldi, P. Nature Machine Intelligence., 1, 356-363, 2019.
Neuronal Capacity Baldi, P., Vershynin, R. Journal of Statistical Mechanics, Theory and Experiment., 2019.
Polynomial threshold functions, hyperplane arrangements, and random tensors Baldi, P., Vershynin, R. SIAM Journal of Mathematics of Data Science., 2019.
The capacity of feedforward neural networks Baldi, P., Vershynin, R. Neural Networks., 116, 288-311, 2019.
Deep Learning in Biomedical Data Science Baldi, P. Annual Review of Biomedical Data Science., 1, 2018.
Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-Hospital Mortality Lee, C. K., Hofer, I., Gabel, E., Baldi, P., Cannesson, M. Anestgesiology., 2018.
Inner and Outer Recursive Neural Networks for Chemoinformatics Applications Urban, G., Subrahmanya, N., Baldi, P. Journal of Chemical Information and Modeling., 58, ( 2 ), 2018.
Deep learning convolutional neural networks accurately classify genetic mutation in gliomas Chang, P., Su, L., Baldi, P., Choi, D. American Journal of Neuroradiolog., 2018.
Learning in the Machine: Random Backpropagation and the Learning Channel Baldi, P., Sadowski, P., Lu, Z. Artifical Intelligence., 2018.
Longitudinal Monitoring of Biofilm Formation via Robust SERS Quantification of Pseudomonas aeruginosa-Produced Metabolite Nguyen, C., Thrift, W. J., Bhattacharjee, A., Ranjbar, S., Gallagher, T., Darvishzadeh-Varcheie, M., Sandersion, R., Capolimo, F., Whiteson, K., Baldi, P., Hochbaum, A., Ragan, R. ACS, Applied Materilas and Interfaces., 2018.
Learning in the Machine: Random Backpropagation and the Deep Learning Channel Baldi, P., Sadowski, P., Lu, Z. Artificial Intelligence., 260, 1-35, 2018.
CircadiOmics: Circadian Omic Data Web Portal Ceglia, N., Liu, Y., Chen, S., Agostinelli, F., Eckel-Mahan, K, Sassone-Corsi, P., Baldi, P. Nuclei Acids Research., 2018.
Deep Learning for Drug Discovery and Cancer Research: Automated Analysis of Vascularization Images Urban, G., Bache, K., Phan, D.T.T., Sorbrino, A., Shmakov, A.K., Hachey, S.J., Hughes, C.W., Baldi, P. IEEE/ACM Transactions on Computational Biology and Bioinformatics., 2018.
Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas Chang, P., Grinband, J., Winberg, B.D., Bardis, M., Khy, M., Cadena, G., Su, M.-Y., Cha, S., Filippi, C.G., Bota, D., Baldi, P., Poisson, L.M., Jain, R., Chow, D. American Journal of Neuroradiology., 2018.
Deep Learning Localizes and Identifies Polyps in Real Time with 96% Accuracy in Screening Colonoscopy Urban, G., Tripathi, P., Alkayali, T., Mittal, M., Jalali, F., Karnes, W., Baldi, P. Gastroenterology,., 155, ( 4 ), 1069-1078, 2018.
Highly-Accurate Machine Fault Diagnosis Using Deep Transfer Learning Shao, S., McAleer, S., Yan, R., Baldi, P. IEEE Transactions on Industrial Informatics., 15, ( 4 ), 2018.
Learning in the machine: Recirculation is random backpropagation Baldi, P., Sadowski, P. Neural Networks., 108, 479-494, 2018.
Deep Learning in the Natural Sciences: Applications to Physics. Sadowski, P., Baldi, P. Key Ideas in Learning Theory from Inception to Current State: Emmanuel Braverman's Legacy., 2018.
From Reinforcement Learning to Deep Reinforcement Learning: An Overview. Agostinelli, F., Hocquet, G., Singh, S., Baldi, P. Key Ideas in Learning Theory from Inception to Current State: Emmanuel Braverman's Legacy., 2018.
A Multi-Resolution Approach to Spinal Metastasis Detection using Dep Siamese Neural Networks Wang, J., Fang, Z., Lang, N., Yuan, H., Su, M., Baldi, P. Computers in Biology and Medicine., 84, 137-146, 2017.
Detecting Cardiovascular Disease from Mammograms with Deep Learning Wang, J., Ding, H., Azamian, F., Zhou, B., Iribarren, C., Molloi, S., Baldi, P. IEEE Transactions onBiomedical Imaging., 36, ( 5 ), 1172-1181, 2017.
Efficient Antihydrogen Detection in Antimater Physics by Deep Learning Sadowski, P., Radics, B., Ananya, A., Yamazaki, Y., Baldi, P. Journal of Physics Communications., 2017.
Mir-132/212 is Required for Maturation of Binocular Matching Orientation Prference and Depth Perception Mazziotti, R., Baroncelli, L., Ceglia, N., Chelini, G., Della Sala, G., Magnan, C., Napoli, D., Putugnano, E., Silingardi, D., Tola, J., Tognini, P., Baldi, P., Pizzorusso, T. Nature Communicaiton., 8, ( 23 ), 2017.
Mutation of neuron-specific chromatin remodeling subunit BAF53b: Rescue of plasticity and memory by manipulating actin remodeling Ciernial, A. V., Kramar, E. A., Matheos, D. P., Havekes, R., Hemstedt, T. J., Magnan, C., Sakata, K., Tran, A., Azzawi, S., Lopez, A., Dang, R., Wang, W., Trieu, B., Tong, J., Barrett, R. M., Post, R.J., Baldi, P., Abel, T., Lynch, G., Wood, M. A. Learning and Memory., 24, 199-209, 2017.
Learning in the Machine: the Symmetries of the Deep Learning Channel Baldi, P., Sadowski, P., Lu, Z. Neural Networks., 95, 110-133, 2017.
The Inner and Outer Approaches for the Design or Recursive Neural Networks Architctures Baldi, P. Data Mining and Knowledg Discovery., 1-13, 2017.
Decorrelated Jet Substructure Tagging using Adversarial Neural Networks Shimmin, C., Sadowski, P., Baldi, P., Weik, E., Whiteson, D., Goul, E., Sogaard, A. Physical Review D., 96, ( 74034 ), 2017.
Deep learning for chemical reaction prediction Fooshee, D., Mood, A., Gutman, E., Tavakoli, A., Urban, G., Liu, F., Huynh, N., Van Vranken, D., Baldi, P. Molecular Systems Design & Engineering., 2018, ( 3 ), 2017.
Deep Learning for Predicitng Postoperative Outcomes: AKI, Reintubation, and In-Hospital Mortality Lee, C., Hofer, I., Baldi, P., Cannesson, M. American Society of Anesthesiologists Conference., 2017.
Deep Learning in Prediciting in Hospital Mortality Lee, C., Hofer, I., Cannesson, M., Baldi, P. Society for Technology in Anesthesia., 2017.
A Theory of Local Learning, the Learning Channel, and the Optimality of Backpropagation Baldi, P., Sadowski, P. Neural Networks., 83, 51-74, 2016.
What Time is it? Deep Learning Approaches for Circadian Rhythms Agostinelli, F., Ceglia, N., Shahbaba, B., Sassone-Corsi, P., Baldi, P. Bioinformatics., 32, ( 12 ), i8-i17, 2016.
Jet Substructure Classification in High-Energy Physics with Deep Neural Networks Baldi, P., Bauer, K., Eng, C., Sadowski, P., Whiteson, D. Physical Review D., 93, ( 94034 ), 2016.
Parameterized Neural Networks for High-Energy Physics Baldi, P., Cranmer, K., Faucett, T., Sadowski, P., Whiteson, D. The European Physical Journal C., 76, ( 235 ), 1-7, 2016.
VIRALpro: a tool to identify viral capsid and tail sequences Galiez, C., Magnan, C., Coste, F., Baldi, P. Bioinformatics., 32, ( 9 ), 1405-1407, 2016.
Jet Flavor Classification in High-Energy Physics with Deep Neural Networks Guest, D., Collado, J., Baldi, P., Whiteson, D. Physical Review D., 94, ( 112002 ), 2016.
Synergies Between Quantum Mechanics and Machine Learning in Reaction Prediction Sadowski, P., Fooshee, D., Subrahmanya, N., Baldi, P. Journal of Chemical Information and Modeling., 56, ( 11 ), 2125-2128, 2016.
Enhanced Higgs to tau+tau- Search with Deep Learning P. Baldi, P. Sadowski, D. Whiteson. Physical Review Letters., 114, 111801-1-111801-5, 2015.
Mitochondrial Mutations in Subjects with Psychiatric Disorders Sequeira, A., Rollins, B., Magnan, C., van Oven, M., Baldi, P., Myers, R. M., Barchas, J. David, Schatzberg, A. F., Watson, S. J., Akil, H., Bunney, W. E., Vawter, M. P. PLOS ONE., 2015.
Deep Learning, Dark Knowledge, and Dark Matter Sadowski, P., Collado, J., Whiteson, D., Baldi, P. Journal of Machine Learning Research., 42, 81-87, 2015.
The Pervasiveness and Plasticity of Circadian Oscillations: The Coupled Circadian-Oscillators Framework Patel, V., Ceglia, N., Zeller, M., Eckel-Mahan, K., Sassone-Corsi, P., Baldi, P. Bioinformatics., 19, ( 31 ), 3181-3188, 2015.
Accurate and Efficient Target Prediction Using a Potency-Sensitive Influence-Relevance Voter Lusci, A., Browning, M., Fooshee, D., Swamidass, S. Josh, Baldi, P. Journal of Cheminformatics., 7, 63-79, 2015.
The Dropout Learning Algorithm P. Baldi, P. Sadowski. Artifical Intelligence., 210, 78-122, 2014.
Incorporating Post-Translational Modifications and Unnatural Amino Acids into High-Throughput Modeling of Protein Structures K. Nagata, A. Randall, P. Baldi. Bioinformatics., 30, ( 12 ), 1681-1689, 2014.
Sspro/ACCpro 5.0: Almost Perfect Prediction of Protein Secondary Structure and Relative Solvent Accessibility. Problem Solved? C. Magnan, P. Baldi. Bioinformatics., 30, ( 18 ), 2592-2597, 2014.
Deep Learning in High-Energy Physics: Improving the Search for Exotic Particles P. Baldi, P. Sadowski, D. Whiteson. Nature Communicaiton., 5, ( 4038 ), 2014.
Enhanced Higgs to Tau-plus Tau-minus Search with Deep Learning Baldi, P., Sadowski, P., Whiteson, D. Pyhsical Review letters., 2014.
Deep Autoencoder Neural Networks for Gene Ontology Annotation Predictions Chicco, D., Sadowski, P., Baldi, P. IEEE/ACM Transactions on Computational Biology and Bioinformatics., 2014.
A Large-Scale Deep Learning Target-Prediction System for Diverse Molecules Lusci, A., Browning, M., Swamidass, S. Josh, Baldi, P. Journal of Chemical Information and Modeling., 2014.
The Pervasiveness and Plasticity of Circadian Oscillations Patel, V., Ceglia, N., Zeller, M., Eckel-Mahan, K., Sassone-Corsi, P., Baldi, P. Bioinformatics., 2014.
Deep Autoencoder Neural Networks for Gene Ontology Annotation Predictions D. Chicco, P. Sadowski, P. Baldi. 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics., 2014.
Searching for Higgs Boson Decay Modes with Deep Learning P. Sadowski, D. Whiteson, P. Baldi. NIPS., 2014.
Deep Architectures and Deep Learning in Chemoinformatics: the Prediction of Aqueous Solubility for Drug-Like Molecules A. Lusci, G. Pollastri, P. Baldi. Journal of Chemical Information and Modeling., 53, ( 7 ), 1563-1575, 2013.
Small-molecule 3D Structure Prediction Using Open Crystallography Data P. Sadowski, P. Baldi. Journal of Chemical Information and Modeling., 53, ( 12 ), 3127-3130, 2013.
Neuron-Specific Nucleosome Remodeling Component BAF53b is Neccessary for Synaptic Plasticity and Memory Vogel-Ciernia, A, Matheos, D. P., Barrett, R. M., Kramar, E., Chen, Y., Magnan, C., Zeller, M., Sylvain, A., Azzawi, S., Haettig, J., Tran, A., Post, R.J., Crabtree, G. R., Baram, T. Z., Baldi, P., Lynch, G., Wood, M. A. Nature Neuroscience., 16, 552-561, 2013.
Understanding Dropout P. Sadowski, P. Baldi. NIPS., 26, 2814-2822, 2013.
Beyond Gradient Diffusion: New Algorithms for Training Deep Layers of Neural Networks J. Yarkony, P. Sadowski, B.S. Manjunath, P. Baldi. NIPS., 2013.
Mitochondrial Mutations and Polymorphisms in Psychiatric Disorders. A. Sequeira, M. V. Martin, B. Rollins, E. A. Moon, W. E. Bunney, F. Macciardi, S. Lupoli, E. Smith, J. Kelsoe, C. Magnan, M. van Oven, P. Baldi, D.C. Wallace, M. P. Vawter. Frontiers in Behavioral and Psychiatric Genetics., 3, 103, 2012.
SIDEpro: a Novel Machine Learning Approach for the Fast and Accurate Prediction of Side-Chain Conformations. K. Nagata, A. Randall, P. Baldi. Protein: Structure, Function, and Bioinformaics., 80, ( 1 ), 142-153, 2012.
Cyber-T Webserver: Differential Analysis of High-Throughput Data. M. Kayala, P. Baldi. Nucleic Acids Research., 1-7, 2012.
Complex-Valued Autoencoders. P. Baldi, Z. Lu. Neural Networks., 33, 136-147, 2012.
Boolean autoencoders and hypercube clustering complexity Baldi, P. Designs, Codes and Cryptography., 1-21, 2012.
Autoencoders, Unsupervised Learning, and Deep Architectures. Baldi, P. Journal of Machine Learning Research., 2012.
CircadiOmics: integrating circadian genomics, transcriptomics, proteomics and metabolomics. Patel, Vishal R., Eckel-Mahan, Kristin, Sassone-Corsi, Paolo, Baldi, Pierre. Nature methods., 9, ( 8 ), 772-773, 2012.
ReactionPredictor: Prediction of Complex Chemical Reactions at the Mechanistic Level Using Machine Learning Kayala, Matthew A., Baldi, Pierre. Journal of Chemical Information and Modeling., 52, ( 10 ), 2526-2540, 2012.
Deep architectures for protein contact map prediction Di Lena, Pietro, Nagata, Ken, Baldi, Pierre. Bioinformatics (Oxford, England)., 28, ( 19 ), 2449-2457, 2012.
Deep Architectures and Learning for Protein Structure Prediction P. Di Lena, K. Nagata, P. Baldi. NIPS., 2012.
Deep Spatial-Temporal Architectures and Learning for Protein Structure Prediction P. Di Lena, K. Nagata, P. Baldi. NIPS., 25, 512-520, 2012.
SIDEpro: a Novel Machine Learning Approach for the Fast and Accurate Prediction of Side-Chain Conformations K. Nagata, A. Randall, P. Baldi. Protein: Structure, Function, and Bioinformaics., 2011.
Learning to Predict Chemical Reactions M. Kayala, C. Azencott, J. Chen, P. Baldi. Journal of Chemical Information and Modeling., 51, ( 9 ), 2209-2222, 2011.
Visual Adaptation and Novelty Responses in the Superior Colliculus. S. Boehnke, D. Berg, R. Marino, P. Baldi, L. Itti, D. Munoz. European Journal of Neuroscience., 34, ( 5 ), 766-779, 2011.
Autoencoders, Unsupervised Learning, and Deep Architectures. Baldi, P. Journal of Machine Learning Research., 2011.
Data-driven High-throughput Prediction of the 3-D Structure of Small Molecules: Review and Progress. Andronico, Alessio, Randall, Arlo, Benz, Ryan W., Baldi, Pierre. Journal of Chemical Information and Modeling., 51, ( 4 ), 760-776, 2011.
Data-driven High-throughput Prediction of the 3-D Structure of Small Molecules: Review and Progress. Andronico, Alessio, Randall, Arlo, Benz, Ryan W., Baldi, Pierre. Journal of Chemical Information and Modeling., 51, ( 4 ), 760-776, 2011.
Data-driven High-throughput Prediction of the 3-D Structure of Small Molecules: Review and Progress. Andronico, Alessio, Randall, Arlo, Benz, Ryan W., Baldi, Pierre. Journal of Chemical Information and Modeling., 51, ( 4 ), 760-776, 2011.
A Machine Learning Approach to Predict Chemical Reactions. M. Kayla, P. Baldi. Neural Information Processing Systems 2011 (NIPS 2011)., 2011.
High-throughput Prediction of Protein Antigenicity Using Protein Microarray Data. Magnan, Christophe N., Zeller, Michael, Kayala, Matthew A., Vigil, Adam, Randall, Arlo, Felgner, Philip L., Baldi, Pierre. Bioinformatics (Oxford, England)., 26, ( 23 ), 2936-2943, 2010.
A CROC Stronger than ROC: Measuring, Visualizing, and Optimizing Early Retrieval. S. J. Swamidass, C. Azencott, K. Daily, P. Baldi. Bioinformatics., 2010.
Of Bits and Wows: A Bayesian Theory of Surprise with Applications to Attention. P. Baldi, L. Itti. Neural Networks., 23, 649-666, 2010.
When is Chemical Similarity Significant? The Statistical Distribution of Chemical Similarity Scores and Its Extreme Values. P. Baldi, R. Nasr. Journal of Chemical Information and Modeling., 2010.
Autoencoders, Unsupervised Learning, and Deep Architectures. P. Baldi. Workshop on Deep Learning and Unsupervised Feature Learning., 2010.
Information-Theoretic Metrics for Project-Level Scattering and Tangling. E. Linstead, L. Hughes, C. Lopes, P. Baldi. International Conference on Software Engineering and Knowledge Engineering (SEKE)., 2010.
Bridging the Gap Between Neural Network and Kernel Methods: Applications to Drug Discovery. P. Baldi, C. Azencott, S. J. Swamidass. WIRN 2010., 2010.
Transmembrane β-Barrel Protein Structure Prediction. A. Randall, P. Baldi. Structural Bioinformatics of Membrane Proteins., 2010.
Reaction Explorer: Towards a Knowledge Map of Organic Chemistry to Support Dynamic Assessment and Personalized Instruction J. Chen, M. A. Kayala, P. Baldi. Enhancing Learning with Online Resources, Social Networking and Digital Libraries., 191-209, 2010.
MotifMap: a human genome-wide map of candidate regulatory motif sites. X. Xie, P. Rigor, P. Baldi. Bioinformatics., 25, ( 2 ), 167-174, 2009.
COBEpro: A Novel System for Predicting Continuous B-cell Epitopes. M. J. Sweredoski, P. Baldi. Protein Engineering Design and Selection., 22, ( 3 ), 113-120, 2009.
Sourcerer: Mining and Searching Internet-Scale Software Repositories. E. Linstead, S. Bajracharya, T. Ngo, P. Rigor, C. Lopes, P. Baldi. Journal of Datamining and Knowledge Discovery., 18, ( 2 ), 300-336, 2009.
The Influence Relevance Voter: An Accurate and Interpretable Virtual High Throughput Screening Method. S. J. Swamidass, C. Azencott, H. Gramajo, S. Tsai, P. Baldi. Journal of Chemical Information and Modeling., 49, ( 4 ), 756-766, 2009.
SOLpro: Accurate Sequence-Based Prediction of Protein Solubility. C. N. Magnan, A. Randall, P. Baldi. Bioinformatics., 25, ( 17 ), 2200-2207, 2009.
Bayesian Surprise Attracts Human Attention. L. Itti, P. Baldi. Vision Research., 49, ( 10 ), 1295-1306, 2009.
Exploring Java Software Vocabulary: A Search and Mining Perspective. E. Linstead, L. Hughes, C. Lopes, P. Baldi. SUITE 2009: Proceedings of the First International Workshop on Search-Driven Development - Users, Tools, and Applications., 2009.
Software Analysis with Unsupervised Topic Models. E. Linstead, L. Hughes, C. Lopes, P. Baldi. Neural Information Processing Systems (NIPS) Conference., 2009.
The Evolution of Concerns, Scattering, and Tangling in Eclipse and ArgoUML. E. Linstead, L. Hughes, P. Baldi. Third International Symposium on Empirical Software Engineering and Measurement (ESEM)., 2009.
Mining the Coherence of GNOME Bug Reports with Statistical Topic Models. E. Linstead, P. Baldi. MSR '09: Proceedings of the Sixth International Working Conference on Mining Software Repositories., 2009.
SELECTpro: Effective Protein Model Selection Using a Structure-Based Energy Function Resistant to Blunders. A. Randall, P. Baldi. BMC Structural Biology., 8, 52, 2008.
Machine Learning Methods for Protein Structure Prediction. J. Cheng, A. N. Tegge, P. Baldi. IEEE Reviews in Biomedical Engineering., 1, 41-49, 2008.
BLASTing Small Molecules—Statistics and Extreme Statistics of Chemical Similarity Scores. P. Baldi, R. W. Benz. Bioinformatics., 24, ( 13 ), i357-i365, 2008.
PEPITO: Improved Discontinuous B-Cell Epitope Prediction Using Multiple Distance Thresholds and Half-Sphere Exposure. M. J. Sweredoski, P. Baldi. Bioinformatics., 24, ( 12 ), 1459-1460, 2008.
Discovery of Power-Laws in Chemical Space. R. W. Benz, S. J. Swamidass, P. Baldi. Journal of Chemical Information and Modeling., 48, ( 6 ), 1138-1151, 2008.
Learning to Play Go Using Recursive Neural Networks. L. Wu, P. Baldi. Neural Networks., ( 21 ), 1392-1400, 2008.
TMBpro: Secondary Structure, β-Contact, and Tertiary Structure Prediction of Transmembrane β-Barrel Proteins. A. Randall, J. Cheng, M. Sweredoski, P. Baldi. Bioinformatics., 24, ( 4 ), 513-520, 2008.
A Theory of Aspects as Latent Topics. P. Baldi, C. Lopes, E. Linstead, S. Bajracharya. 2008 ACM SIGPLAN Conference on Object-Oriented Programming Systems, Languages, and Applications., 2008.
An Application of Latent Dirichlet Allocation to Analyzing Software Evolution. E. Linstead, C. Lopes, P. Baldi. International Conference on Machine Learning and Applications (ICMLA)., 2008.
One- to Four-Dimensional Kernels for Virtual Screening and the Prediction of Physical, Chemical, and Biological Properties. C. Azencott, A. Ksikes, S. Joshua Swamidass, J. Chen, L. Ralaivola, P. Baldi. Journal of Chemical Information and Modeling., 47, ( 3 ), 965-974, 2007.
A bottom-up model of spatial attention predicts human error patterns in rapid scene recognition. W. Einhäuser, T. N. Mundhenk, P. Baldi, C. Koch, L. Itti. Journal of Vision., 7, ( 10 ), 1-13, 2007.
Improved Residue Contact Prediction Using Support Vector Machines and a Large Feature Set. J. Cheng, P. Baldi. BMC Bioinformatics., 8, 113-121, 2007.
Mining Eclipse Developer Contributions via Author-Topic Models. E. Linstead, P. Rigor, S. Bajracharya, C. Lopes, P. Baldi. Proceedings of the MSR 2007: International Workshop on Mining Software Repositories., 30-33, 2007.
Mining Concepts from Code with Probabilistic Topic Models. E. Linstead, P. Rigor, S. Bajracharya, C. Lopes, P. Baldi. Proceedings of the 22nd IEEE/ACM International Conference on Automated Software Engineering., 461-464, 2007.
Mining Internet-Scale Software Repositories. E. Linstead, P. Rigor, S. Bajracharya, C. Lopes, P. Baldi. Advances in Neural Information Processing Systems 20., 929-936, 2007.
Virtual High-Throughput Screening with Two-Dimensional Kernels. A. Azencott, P. Baldi. Hands-On Pattern Recognition. Challenges in Data Representation, Model Selection, and Performance Prediction., 2007.
Functional Census of Mutation Sequence Spaces: The Example of p53 Cancer Rescue Mutants. S. A. Danziger, S. J. Swamidass, J. Zeng, L. R. Dearth, Q. Lu, J. H. Chen, J. Cheng, V. P. Hoang, H. Saigo, R. Luo, P. Baldi, Rainer K. Brachmann, Richard H. Lathrop. IEEE Transactions on Computational Biology and Bioinformatics., 3, ( 2 ), 114-125, 2006.
Large-Scale Prediction of Disulphide Bridges Using Kernel Methods, Two-Dimensional Recursive Neural Networks, and Weighted Graph Matching. J. Cheng, H. Saigo, P. Baldi. Proteins., 62, ( 3 ), 617-629, 2006.
Prediction of Protein Stability Changes for Single Site Mutations Using Support Vector Machines. J. Cheng, A. Randall, P. Baldi. Proteins., 62, ( 4 ), 1125-1132, 2006.
DOMpro: Protein Domain Prediction Using Profiles, Secondary Structure, Relative Solvent Accessibility, and Recursive Neural Networks. J. Cheng, M. J. Sweredoski, P. Baldi. Data Mining and Knowledge Discovery., 13, ( 1 ), 1-10, 2006.
Modular DAG-RNN Architectures for Assembling Coarse Protein Structures. G. Pollastri, A. Vullo, P. Frasconi, P. Baldi. Journal of Computational Biology., 3, 631-650, 2006.
A Machine Learning Information Retrieval Approach to Protein Fold Recognition. J. Cheng, P. Baldi. Bioinformatics., 22, ( 12 ), 1456-1463, 2006.
A Scalable Machine Learning Approach to Go. L. Wu, P. Baldi. Advances in Neural Information Processing Systems 19., 1521-1528, 2006.
Kernels for Small Molecules and the Prediction of Mutagenicity, Toxicity, and Anti-Cancer Activity. S. J. Swamidass, J. Chen, P. Phung, J. Bruand, L. Ralaivola, P. Baldi. Bioinformatics., 21, i359-368, 2005.
Three-Stage Prediction of Protein β-Sheets by Neural Networks, Alignments, and Graph Algorithms. J. Cheng, P. Baldi. Bioinformatics., 21, i75-84, 2005.
Polymodal sensory function of the C. elegans OCR-2 channel arises from distinct intrinsic determinants within the protein and is selectively conserved in human TRPV2. I. Sokolchik, T. Tanabe, P. Baldi, J. Y. Sze. Journal of Neuroscience., 25, ( 4 ), 1015-1023, 2005.
Graph Kernels for Chemical Informatics. L. Ralaivola, J. S. Swamidass, H. Saigo, P. Baldi. Neural Networks., 18, ( 8 ), 1093-1110, 2005.
On the Relationship Between Deterministic and Probabilistic Directed Graphical Models: from Bayesian Networks to Recursive Neural Networks. P. Baldi, M. Rosen-Zvi. Neural Networks., 18, ( 8 ), 1080-1086, 2005.
SCRATCH: a Protein Structure and Structural Feature Prediction Server. J. Cheng, A. Z. Randall, M. Sweredoski, P. Baldi. Nucleic Acids Research., 33, ( 9 ), W72-76, 2005.
Accurate Prediction of Protein Disordered Regions by Mining Protein Structure Data. J. Cheng, M. J. Sweredoski, P. Baldi. Data Mining and Knowledge Discovery., 11, ( 3 ), 213-222, 2005.
SVM and Pattern-Enriched Common Fate Graphs for the Game of GO. L. Ralaivola, L. Wu, P. Baldi. European Symposium on Artificial Neural Networks (ESANN)., 2005.
A Principled Approach to Detecting Surprising Events in Video. L. Itti, P. Baldi. Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR)., 2005.
Kernels for Small Molecules and the Prediction of Mutagenicity, Toxicity, and Anti-Cancer Activity. S. Swamidass, J. Chen, P. Phung, J. Bruand, L. Ralaivola, P. Baldi. 2005 Conference on Intelligent Systems for Molecular Biology (ISMB 05)., 21, i359-368, 2005.
Three-Stage Prediction of Protein β-Sheets by Neural Networks, Alignments, and Graph Algorithms. J. Cheng, P. Baldi. 2005 Conference on Intelligent Systems for Molecular Biology (ISMB 05)., i75-84, 2005.
Bayesian Surprise Attracts Human Attention. L. Itti, P. Baldi. Advances in Neural Information Processing Systems 18, NIPS 2005., 547-554, 2005.
Attention: Bits versus Wows. P. Baldi, L. Itti. Proceedings of the International Conference on Neural Networks and Brain., 1, PL56-PL61, 2005.
Exploring Chemical Space with Computers: Informatics Challenges for AI and Machine Learning. P. Baldi. BIOMAT V Symposium., 2005.
Surprise: A Shortcut for Attention? P. Baldi. Neurobiology of Attention., 24-28, 2005.
Large-Scale Prediction of Disulphide Bond Connectivity. P. Baldi, J. Cheng, A. Vullo. Advances in Neural Information Processing Systems 17 (NIPS 2004)., 2004.
Hidden Markov Models and Neural Networks. S. Kremer, P. Baldi. Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics., 2004.
Combining Protein Secondary Structure Prediction Models With Ensemble Methods of Optimal Complexity. Y. Guermeur, G. Pollastri, A. Elisseeff, D. Zelus, H. Paugam-Moisy, P. Baldi. Neurocomputing., 56, 305-327, 2003.
The Principled Design of Large-Scale Recursive Neural Network Architectures—DAG-RNNs and the Protein Structure Prediction Problem. P. Baldi, G. Pollastri. Journal of Machine Learning Research., 4, 575-602, 2003.
Prediction of Protein Topologies Using Generalized IOHMMs and RNNs. G. Pollastri, P. Baldi, A. Vullo, P. Frasconi. Advances in Neural Information Processing Systems 15., 15, 2003.
Modeling the Internet and the Web: Probabilistic Methods and Algorithms. P. Baldi, P. Frasconi, P. Smyth. , Wiley , 2003.
New Machine Learning Methods for the Prediction of Protein Topologies. P. Baldi, G. Pollastri, P. Frasconi, A. Vullo. Artificial Intelligence and Heuristic Methods in Bioinformatics., 51-73, 2003.
Prediction of Contact Maps by GIOHMMs and Recurrent Neural Networks Using Lateral Propagation From All Four Cardinal Corners. G. Pollastri, P. Baldi. Bioinformatics., 18, S62-S70, 2002.
Improving the Prediction of Protein Secondary Structure in Three and Eight Classes Using Recurrent Neural Networks and Profiles. G. Pollastri, D. Przybylski, B. Rost, P. Baldi. Proteins., 47, 228-235, 2002.
Prediction of Coordination Number and Relative Solvent Accessibility in Proteins. G. Pollastri, P. Baldi, P. Fariselli, R. Casadio. Proteins., 47, 142-153, 2002.
A Machine Learning Strategy for Protein Analysis. P. Baldi, G. Pollastri. IEEE Intelligent Systems (special Issue on "Intelligent Systems in Biology")., 17, ( 2 ), 28-35, 2002.
Modeling and Optimization of UWB Communication Networks Through a Flexible Cost Function. P. Baldi, L. De Nardis, M. G. Di Benedetto. IEEE Journal on Selected Areas in Communications., 20, ( 9 ), 1733-1744, 2002.
Prediction of Contact Maps by GIOHMMs and Recurrent Neural Networks Using Lateral Propagation From All Four Cardinal Corners. G. Pollastri, P. Baldi. Proceedings of the 2002 Conference on Intelligent Systems for Molecular Biology (ISMB 02)., 18, S62-S70, 2002.
A Computational Theory of Surprise. P. Baldi. Information, Coding, and Mathematics., 1-25, 2002.
Improved Prediction of the Number of Residue Contacts in Proteins by Recurrent Neural Networks. G. Pollastri, P. Baldi, P. Fariselli, R. Casadio. Bioinformatics., 17, S234-S242, 2001.
A Bayesian Framework for the Analysis of Microarray Expression Data: Regularized t-Test and Inference of Gene Changes. P. Baldi, A. D. Long. Bioinformatics., 17, ( 6 ), 509-519, 2001.
A Model for Self-Organizing Large Scale Wireless Networks. M. Di Benedetto, P. Baldi. International Symposium on 3G Infrastructure and Services., 2001.
Improved Prediction of the Number of Residue Contacts in Proteins by Recurrent Neural Networks. G. Pollastri, P. Baldi, P. Fariselli, R. Casadio. Proceedings of the 2001 Conference on Intelligent Systems for Molecular Biology, (ISMB01)., 2001.
Probabilistic Graphical Models in Computational Molecular Biology. P. Baldi. Journal of the Italian Association for Artificial Intelligence., 1, 8-12, 2000.
On the Convergence of a Clustering Algorithm for Protein-Coding Regions in Microbial Genomes. P. Baldi. Bioinformatics., 16, ( 4 ), 367-371, 2000.
Assessing the Accuracy of Prediction Algorithms for Classification: An Overview. P. Baldi, S. Brunak, Y. Chauvin, H. Nielsen. Bioinformatics., 16, ( 5 ), 412-424, 2000.
Protein β-Sheet Partner Prediction by Neural Networks. P. Baldi, G. Pollastri, C. A. F. Andersen, S. Brunak. Artifical Neural Networks in Medicine and Biology. Proceedings of the ANNIMAB-1 Conference., 2000.
Matching Protein β-Sheet Partners by Feedforward and Recurrent Neural Networks. P. Baldi, G. Pollastri, C. A. F. Andersen, S. Brunak. Proceedings of the 2000 Conference on Intelligent Systems for Molecular Biology (ISMB00)., 25-36, 2000.
Bidirectional IOHMMs and recurrent neural networks for protein secondary structure prediction. P. Baldi, S. Brunak, G. Pollastri, P. Frasconi. Protein Sequence Analysis in the Genomic Era., 2000.
Bidirectional Dynamics for Protein Secondary Structure Prediction. P. Baldi, S. Brunak, P. Frasconi, G. Pollastri, G. Soda. Sequence Learning: Paradigms, Algorithms, and Applications., 99-120, 2000.
Exploiting the Past and the Future in Protein Secondary Structure Prediction. P. Baldi, S. Brunak, P. Frasconi, G. Soda, G. Pollastri. Bioinformatics., 15, ( 11 ), 937-946, 1999.
On the Use of Bayesian Methods for Evaluating Compartmental Neural Models. P. Baldi, M. C. Vanier, J. M. Bower. Journal of Computational Neuroscience., 5, 285-314, 1998.
Bioinformatics: the Machine Learning Approach. P. Baldi, S. Brunak. , MIT Press , 1998.
Probabilistic Models of Neuronal Spike Trains. P. Baldi. Adaptive Processing of Temporal Information., 198-228, 1998.
Software Foundation Libraries for the Design of Intelligent Systems. P. Baldi, Y. Chauvin, V. Mittal Henkle. Neural Nets WIRN Vietri 98. Proceedings of the 10-th Italian Workshop on Neural Nets., 17-39, 1998.
Hybrid Modeling, HMM/NN Architectures, and Protein Applications. P. Baldi, Y. Chauvin. Neural Computation., 2, ( 3 ), 497-501, 1996.
Universal Approximation and Learning of Trajectories Using Oscillators. P. Baldi, K. Hornik. Advances in Neural Information Processing Systems 8., 451-457, 1996.
Hidden Markov Models for Human Genes: Periodic Patterns in Exon Sequences. P. Baldi, S. Brunak, Y. Chauvin, A. Krogh. Theoretical and Computational Methods in Genome Research., 1996.
Gradient Descent Learning Algorithms Overview: A General Dynamical Systems Perspective. P. Baldi. IEEE Transactions on Neural Networks., 6, ( 1 ), 182-195, 1995.
Learning in Linear Networks: a Survey. P. Baldi, K. Hornik. IEEE Transactions on Neural Networks., 6, ( 4 ), 837-858, 1995.
Substitution Matrices and Hidden Markov Models. P. Baldi. Journal of Computational Biology., 2, ( 3 ), 497-501, 1995.
Inferring Ground Truth from Subjective Labelling of Venus Images. P. Smyth, U. Fayadd, P. Perona, M. Burl, P. Baldi. Advances in Neural Information Processing Systems., 1085-1092, 1995.
Protein Modeling with Hybrid Hidden Markov Model/Neural Network Architectures. P. Baldi, Y. Chauvin. Proceedings of the 1995 Conference on Intelligent Systems for Molecular Biology (ISMB95)., 39-47, 1995.
When Neural Networks Play Sherlock Holmes. P. Baldi, Y. Chauvin. Back-Propagation: Theory, Architectures and Applications., ( 487 ), 1995.
Gradient Descent Learning Algorithms: A Unified Perspective. P. Baldi. Back-Propagation: Theory, Architectures and Applications., 509-541, 1995.
Back-propagation and unsupervised learning in linear networks. P. Baldi, Y. Chauvin, K. Hornik. Back-Propagation: Theory, Architectures and Applications., 389-432, 1995.
How Delays Affect Neural Dynamics and Learning. P. Baldi, A. Atiya. IEEE Transactions on Neural Networks., 5, ( 4 ), 626-635, 1994.
Smooth On-Line Learning Algorithms for Hidden Markov Models. P. Baldi, Y. Chauvin. Neural Computation., 6, ( 2 ), 305-316, 1994.
Hidden Markov Models of Biological Primary Sequence Information. P. Baldi, Y. Chauvin, T. Hunkapiller, M. A. McClure. PNAS USA., 91, ( 3 ), 1059-1063, 1994.
Hidden Markov Models of the G-Protein Coupled Receptor Family. P. Baldi, Y. Chauvin. Journal of Computational Biology., 1, ( 4 ), 311-335, 1994.
Trading Decision Learning: from Theory to Personal Traders. P. Baldi, Y. Chauvin. Proceedings of the Second International Conference on Neural Networks in the Capital Markets., 1994.
Modeling Protein Families and Human Genes: Hidden Markov Models and a Little Beyond. P. Baldi, Y. Chauvin. Proceedings of the 1994 Fifth Generation Computing Symposium, Workshop on Fusion of Molecular Biology and Knowledge Processing., 1994.
Discrimination of Tyrosine and Serine/Threonine Kinase Sub-Families by Hidden Markov Models. P. Baldi, Y. Chauvin. Proceedings of The Third International Conference on Bioinformatics and Genome Research., 1994.
Hidden Markov Models of Human Genes. P. Baldi, S. Brunak, Y. Chauvin, J. Engelbrecht, A. Krogh. Advances in Neural Information Processing Systems., 761-768, 1994.
Statistical Models of Proteins: an Application to the G-Protein-Coupled Receptor Family. P. Baldi, Y. Chauvin. Modern Approaches in Molecular Bioinformatics., 53-102, 1994.
Random Interactions in Higher-Orders Neural Networks. P. Baldi, S.S. Venkatesh. IEEE Transactions on Information Theory., 39, ( 1 ), 274-283, 1993.
Neural Networks for Fingerprint Recognition. P. Baldi, Y. Chauvin. Neural Computation., 5, ( 3 ), 402-418, 1993.
Learning Trajectories with a Hierarchy of Oscillatory Modules. P. Baldi, Nikzad Toomarian. Proceedings of the 1993 IEEE International Conference on Neural Networks., 1172-1176, 1993.
Hidden Markov Models in Molecular Biology: New Algorithms and Applications. P. Baldi, Y. Chauvin, T. Hunkapiller, M. A. McClure. Advances in Neural Information Processing Systems., 747-754, 1993.
A Modular Hierarchical Approach to Learning. P. Baldi. Proceedings of the 2nd International Conference on Fuzzy Logic and Neural Networks., 2, 985-988, 1992.
Trading Decision Learning. P. Baldi, Y. Chauvin. Neural Networks for Computing Conference (abstract)., 1992.
Programmed Interactions in Higher-Orders Neural Networks: I. Maximal Capacity. S.S. Venkatesh, P. Baldi. Journal of Complexity., 7, 316-337, 1991.
Programmed Interactions in Higher-Orders Neural Networks: II. The Outer-Product Algorithm. S. S. Venkatesh, P. Baldi. Journal of Complexity., 7, 443-479, 1991.
Temporal Evolution of Generalization during Learning in Linear Networks. P. Baldi, Y. Chauvin. Neural Computation., 3, ( 4 ), 589-603, 1991.
Contrastive Learning and Neural Oscillations. P. Baldi, F. Pineda. Neural Computation., 3, ( 4 ), 526-545, 1991.
Computing with Arrays of Coupled Oscillators: an Application to Preattentive Texture Discrimination. P. Baldi, R. Meir. Neural Computation., 2, ( 4 ), 458-471, 1990.
Computing with Arrays of Bell-Shaped and Sigmoid Functions. P. Baldi. Proceedings of the 1990 conference on Advances in neural information processing systems 3., 735-742, 1990.
Asymptotic Normality of Some Graph Related Statistics. P. Baldi, Y. Rinott. Journal of Applied Probability., 26, 171-175, 1989.
On Normal Approximations of Distributions in Terms of Dependency Graphs. P. Baldi, Y. Rinott. Annals of Probability., 17, ( 4 ), 1646-1650, 1989.
Oscillations and Synchronizations in Neural Networks: an Exploration of the Labeling Hypothesis. P. Baldi, A. Atiya. International Journal of Neural Systems., 1, ( 2 ), 103-124, 1989.
On the Distribution of the Number of Local Minima of a Random Function on a Graph. P. Baldi, Y. Rinott, C. Stein. Proceedings of the 1989 Conference on Neural Information Processing Systems., 727-732, 1989.
A Normal Approximation for the Number of Local Maxima of a Random Function on a Graph. P. Baldi, Y. Rinott, C. Stein. Probability, Statistics and Mathematics: Papers in Honor of Samuel Karlin., 1989.
Neural Networks and Principal Component Analysis: Learning from Examples without Local Minima. P. Baldi, K. Hornik. Neural Networks., 2, ( 1 ), 53-58, 1988.
Neural Networks, Orientations of the Hypercube and Algebraic Threshold Functions. P. Baldi. IEEE Transactions on Information Theory., 34, ( 3 ), 523-530, 1988.
Group Actions and Learning for a Family of Automata. P. Baldi. Journal of Computer and System Sciences., 36, ( 2 ), 1-15, 1988.
Neural Networks, Acyclic Orientations of the Hypercube and Sets of Orthogonal Vectors. P. Baldi. SIAM Journal of Discrete Mathematics., 1, ( 1 ), 1-13, 1988.
How Sensory Maps Could Enhance Resolution Through Ordered Arrangements of Broadly Tuned Receivers. P. Baldi, W. Heiligenberg. Biological Cybernetics., 59, ( 4 ), 313-318, 1988.
Neural Networks and Principal Component Analysis. P. Baldi. Proceedings of the 1988 Conference on Neural Information Processing., 65-72, 1988.
Number of Stable Points for Spin Glasses and Neural Networks of Higher Orders. P. Baldi, S.S. Venkatesh. Physical Review Letters., 58, ( 9 ), 913-916, 1987.
Symmetries and Learning in Neural Network Models. P. Baldi. Physical Review Letters., 59, ( 17 ), 1976-1978, 1987.
Embeddings of Ultrametric Spaces in Finite Dimensional Structures. M. Aschbacher, P. Baldi, E. B. Baum, R.M. Wilson. SIAM Journal of Algebraic and Discrete Methods., 8, ( 4 ), 564-577, 1987.
On Properties of Networks of Neuron-Like Elements: Complexity and Capacity. P. Baldi, S.S. Venkatesh. Proceedings of the IEEE Conference on Neural Information Processing Systems., 1987.
Bounds on the Size of Ultrametric Structures. P. Baldi, E.B. Baum. Physical Review Letters., 56, ( 15 ), 1598-1600, 1986.
Caging and Exhibiting Ultrametric Structures. P. Baldi, E.B. Baum. Proceedings of the Conference on Neural Networks for Computing., 37, 232-246, 1986.
|
|