Forest Agostinelli
Cited by
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Learning activation functions to improve deep neural networks
F Agostinelli, M Hoffman, P Sadowski, P Baldi
International Conference on Learning Representations Workshop, 2015
Adaptive multi-column deep neural networks with application to robust image denoising
F Agostinelli, MR Anderson, H Lee
Advances in neural information processing systems 26, 2013
Solving the Rubik’s cube with deep reinforcement learning and search
F Agostinelli, S McAleer, A Shmakov, P Baldi
Nature Machine Intelligence 1 (8), 356-363, 2019
What time is it? Deep learning approaches for circadian rhythms
F Agostinelli, N Ceglia, B Shahbaba, P Sassone-Corsi, P Baldi
Bioinformatics 32 (12), i8-i17, 2016
Solving the rubik's cube with approximate policy iteration
S McAleer, F Agostinelli, AK Shmakov, P Baldi
International Conference on Learning Representations, 2019
From reinforcement learning to deep reinforcement learning: An overview
F Agostinelli, G Hocquet, S Singh, P Baldi
Braverman Readings in Machine Learning. Key Ideas from Inception to Current …, 2018
CircadiOmics: circadian omic web portal
N Ceglia, Y Liu, S Chen, F Agostinelli, K Eckel-Mahan, P Sassone-Corsi, ...
Nucleic acids research 46 (W1), W157-W162, 2018
Splash: Learnable activation functions for improving accuracy and adversarial robustness
M Tavakoli, F Agostinelli, P Baldi
Neural Networks 140, 1-12, 2021
Hippocampal ensembles represent sequential relationships among an extended sequence of nonspatial events
B Shahbaba, L Li, F Agostinelli, M Saraf, KW Cooper, D Haghverdian, ...
Nature communications 13 (1), 787, 2022
Q* Search: Heuristic Search with Deep Q-Networks
F Agostinelli, SS Shperberg, A Shmakov, S McAleer, R Fox, P Baldi
ICAPS Workshop on Bridging the Gap between AI Planning and Reinforcement …, 2024
Designing children’s new learning partner: collaborative artificial intelligence for learning to solve the Rubik’s cube
F Agostinelli, M Mavalankar, V Khandelwal, H Tang, D Wu, B Berry, ...
Proceedings of the 20th Annual ACM Interaction Design and Children …, 2021
Obtaining approximately admissible heuristic functions through deep reinforcement learning and A* search
F Agostinelli, S McAleer, A Shmakov, R Fox, M Valtorta, B Srivastava, ...
Bridging the Gap between AI Planning and Reinforcement Learning workshop at …, 2021
Specifying goals to deep neural networks with answer set programming
F Agostinelli, R Panta, V Khandelwal
Proceedings of the International Conference on Automated Planning and …, 2024
CircadiOmics: circadian omic web portal
M Samad, F Agostinelli, T Sato, K Shimaji, P Baldi
Nucleic acids research 50 (W1), W183-W190, 2022
Explainable artificial intelligence (XAI) user interface design for solving a Rubik’s Cube
C Bradley, D Wu, H Tang, I Singh, K Wydant, B Capps, K Wong, ...
International Conference on Human-Computer Interaction, 605-612, 2022
ALLURE: A Multi-Modal Guided Environment for Helping Children Learn to Solve a Rubik’s Cube with Automatic Solving and Interactive Explanations
K Lakkaraju, T Hassan, V Khandelwal, P Singh, C Bradley, R Shah, ...
AAAI Conference on Artificial Intelligence - Demonstration Track, 2022
On Solving the Rubik's Cube with Domain-Independent Planners Using Standard Representations
B Muppasani, V Pallagani, B Srivastava, F Agostinelli
arXiv preprint arXiv:2307.13552, 2023
Synthesis of CdZnTeSe single crystals for room temperature radiation detector fabrication: mitigation of hole trapping effects using a convolutional neural network
SK Chaudhuri, JW Kleppinger, OF Karadavut, R Nag, R Panta, ...
Journal of Materials Science: Materials in Electronics 33 (3), 1452-1463, 2022
Improving survey aggregation with sparsely represented signals
T Shi, F Agostinelli, M Staib, D Wipf, T Moscibroda
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge …, 2016
Deep Learning for Puzzles and Circadian Rhythms
F Agostinelli
University of California, Irvine, 2019
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