Forest Agostinelli
Forest Agostinelli
Assistant Professor at the University of South Carolina
Verified email at - Homepage
Cited by
Cited by
Learning activation functions to improve deep neural networks
F Agostinelli, M Hoffman, P Sadowski, P Baldi
arXiv preprint arXiv:1412.6830, 2014
Adaptive Multi-Column Deep Neural Networks with Application to Robust Image Denoising
F Agostinelli, MR Anderson, H Lee
Neural Information Processing Systems (NIPS), 2013
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, A Shmakov, P Baldi
International Conference on Learning Representations, 2019
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
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
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
Hippocampal ensembles represent sequential relationships among discrete nonspatial events
B Shahbaba, L Li, F Agostinelli, M Saraf, GA Elias, P Baldi, NJ Fortin
bioRxiv, 840199, 2019
Improving Survey Aggregation with Sparsely Represented Signals
T Shi, F Agostinelli, M Staib, D Wipf, T Moscibroda
22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 1845-1854, 2016
SPLASH: Learnable Activation Functions for Improving Accuracy and Adversarial Robustness
M Tavakoli, F Agostinelli, P Baldi
arXiv preprint arXiv:2006.08947, 2020
Deep Learning for Puzzles and Circadian Rhythms
F Agostinelli
UC Irvine, 2019
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