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2020 – today
- 2024
[i17]Soumyendu Sarkar, Vineet Gundecha, Sahand Ghorbanpour, Alexander Shmakov, Ashwin Ramesh Babu, Avisek Naug, Alexandre Pichard, Mathieu Cocho:
Function Approximation for Reinforcement Learning Controller for Energy from Spread Waves. CoRR abs/2404.10991 (2024)- [i16]Alexander Shmakov, Kevin Greif, Michael James Fenton, Aishik Ghosh, Pierre Baldi, Daniel Whiteson:
Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion. CoRR abs/2404.14332 (2024) - [i15]Nathan Huetsch, Javier Mariño Villadamigo, Alexander Shmakov, Sascha Diefenbacher, Vinicius Mikuni, Theo Heimel, Michael James Fenton, Kevin Greif, Benjamin Nachman, Daniel Whiteson, Anja Butter, Tilman Plehn:
The Landscape of Unfolding with Machine Learning. CoRR abs/2404.18807 (2024) - [i14]Haoyang Li, Marko Stamenkovic, Alexander Shmakov, Michael Fenton, Darius Shih-Chieh Chao, Kaitlyn Maiya White, Caden Mikkelsen, Jovan Mitic, Cristina Mantilla Suarez, Melissa Quinnan, Greg Landsberg, Harvey B. Newman, Pierre Baldi, Daniel Whiteson, Javier M. Duarte:
Reconstruction of boosted and resolved multi-Higgs-boson events with symmetry-preserving attention networks. CoRR abs/2412.03819 (2024) - 2023
- [c8]Soumyendu Sarkar, Ashwin Ramesh Babu, Sajad Mousavi, Vineet Gundecha, Sahand Ghorbanpour, Alexander Shmakov, Ricardo Luna Gutierrez, Antonio Guillen, Avisek Naug:
Robustness with Black-Box Adversarial Attack using Reinforcement Learning. SafeAI@AAAI 2023 - [c7]Alexander Shmakov, Avisek Naug, Vineet Gundecha, Sahand Ghorbanpour, Ricardo Luna Gutierrez, Ashwin Ramesh Babu, Antonio Guillen, Soumyendu Sarkar:
RTDK-BO: High Dimensional Bayesian Optimization with Reinforced Transformer Deep kernels. CASE 2023: 1-8 - [c6]Soumyendu Sarkar, Vineet Gundecha, Sahand Ghorbanpour, Alexander Shmakov, Ashwin Ramesh Babu, Avisek Naug, Alexandre Pichard, Mathieu Cocho:
Function Approximation for Reinforcement Learning Controller for Energy from Spread Waves. IJCAI 2023: 6201-6209 - [c5]Alexander Shmakov, Kevin Greif, Michael James Fenton, Aishik Ghosh, Pierre Baldi, Daniel Whiteson:
End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics. NeurIPS 2023 - [c4]Mohammadamin Tavakoli, Pierre Baldi, Ann Marie Carlton, Yin Ting T. Chiu, Alexander Shmakov, David Van Vranken:
AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning. NeurIPS 2023 - [i13]Alexander Shmakov, Alejandro Yankelevich, Jianming Bian, Pierre Baldi:
Interpretable Joint Event-Particle Reconstruction for Neutrino Physics at NOvA with Sparse CNNs and Transformers. CoRR abs/2303.06201 (2023) - [i12]Alexander Shmakov, Kevin Greif, Michael James Fenton
, Aishik Ghosh, Pierre Baldi, Daniel Whiteson:
End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics. CoRR abs/2305.10399 (2023) - [i11]Michael James Fenton, Alexander Shmakov, Hideki Okawa, Yuji Li, Ko-Yang Hsiao, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi:
Extended Symmetry Preserving Attention Networks for LHC Analysis. CoRR abs/2309.01886 (2023) - [i10]Alexander Shmakov, Avisek Naug, Vineet Gundecha, Sahand Ghorbanpour, Ricardo Luna Gutierrez, Ashwin Ramesh Babu, Antonio Guillen, Soumyendu Sarkar:
RTDK-BO: High Dimensional Bayesian Optimization with Reinforced Transformer Deep kernels. CoRR abs/2310.03912 (2023) - [i9]Mohammadamin Tavakoli, Yin Ting T. Chiu, Alexander Shmakov, Ann Marie Carlton, David Van Vranken, Pierre Baldi:
AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning. CoRR abs/2311.01118 (2023) - 2022
- [c3]Soumyendu Sarkar, Vineet Gundecha, Alexander Shmakov, Sahand Ghorbanpour, Ashwin Ramesh Babu, Paolo Faraboschi, Mathieu Cocho, Alexandre Pichard, Jonathan Fievez:
Multi-Agent Reinforcement Learning Controller to Maximize Energy Efficiency for Multi-Generator Industrial Wave Energy Converter. AAAI 2022: 12135-12144 - [c2]Soumyendu Sarkar, Vineet Gundecha, Sahand Ghorbanpour, Alexander Shmakov, Ashwin Ramesh Babu, Alexandre Pichard, Mathieu Cocho:
Skip Training for Multi-Agent Reinforcement Learning Controller for Industrial Wave Energy Converters. CASE 2022: 212-219 - [i8]Mohammadamin Tavakoli, Alexander Shmakov, Francesco Ceccarelli, Pierre Baldi:
Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation. CoRR abs/2201.01196 (2022) - [i7]Alexander Shmakov, Mohammadamin Tavakoli, Pierre Baldi, Christopher M. Karwin, Alex Broughton, Simona Murgia:
Deep Learning Models of the Discrete Component of the Galactic Interstellar Gamma-Ray Emission. CoRR abs/2206.02819 (2022) - [i6]Soumyendu Sarkar, Vineet Gundecha, Sahand Ghorbanpour, Alexander Shmakov, Ashwin Ramesh Babu, Alexandre Pichard, Mathieu Cocho:
Skip Training for Multi-Agent Reinforcement Learning Controller for Industrial Wave Energy Converters. CoRR abs/2209.05656 (2022) - 2021
- [i5]Forest Agostinelli, Alexander Shmakov, Stephen McAleer, Roy Fox, Pierre Baldi:
A* Search Without Expansions: Learning Heuristic Functions with Deep Q-Networks. CoRR abs/2102.04518 (2021) - [i4]Alexander Shmakov, Michael James Fenton, Ta-Wei Ho, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi:
SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention. CoRR abs/2106.03898 (2021) - 2020
- [i3]Michael James Fenton, Alexander Shmakov, Ta-Wei Ho, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi:
Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks. CoRR abs/2010.09206 (2020)
2010 – 2019
- 2019
- [j2]Forest Agostinelli, Stephen McAleer, Alexander Shmakov, Pierre Baldi:
Solving the Rubik's cube with deep reinforcement learning and search. Nat. Mach. Intell. 1(8): 356-363 (2019) - [j1]Gregor Urban, Kevin Bache, Duc T. T. Phan, Agua Sobrino, Alexander Shmakov, Stephanie J. Hachey, Christopher C. W. Hughes, Pierre Baldi:
Deep Learning for Drug Discovery and Cancer Research: Automated Analysis of Vascularization Images. IEEE ACM Trans. Comput. Biol. Bioinform. 16(3): 1029-1035 (2019) - [c1]Stephen McAleer, Forest Agostinelli, Alexander Shmakov, Pierre Baldi:
Solving the Rubik's Cube with Approximate Policy Iteration. ICLR (Poster) 2019 - [i2]Alexander Shmakov, John B. Lanier, Stephen McAleer, Rohan Achar, Cristina V. Lopes, Pierre Baldi:
ColosseumRL: A Framework for Multiagent Reinforcement Learning in N-Player Games. CoRR abs/1912.04451 (2019) - 2018
- [i1]Stephen McAleer, Forest Agostinelli, Alexander Shmakov, Pierre Baldi:
Solving the Rubik's Cube Without Human Knowledge. CoRR abs/1805.07470 (2018)
Coauthor Index
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