Felipe Petroski Such
Felipe Petroski Such
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Evaluating large language models trained on code
M Chen, J Tworek, H Jun, Q Yuan, HPO Pinto, J Kaplan, H Edwards, ...
arXiv preprint arXiv:2107.03374, 2021
An intriguing failing of convolutional neural networks and the coordconv solution
R Liu, J Lehman, P Molino, F Petroski Such, E Frank, A Sergeev, ...
Advances in neural information processing systems 31, 2018
Deep neuroevolution: Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning
FP Such, V Madhavan, E Conti, J Lehman, KO Stanley, J Clune
arXiv preprint arXiv:1712.06567, 2017
Gpt-4 technical report
J Achiam, S Adler, S Agarwal, L Ahmad, I Akkaya, FL Aleman, D Almeida, ...
arXiv preprint arXiv:2303.08774, 2023
Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents
E Conti, V Madhavan, F Petroski Such, J Lehman, K Stanley, J Clune
Advances in neural information processing systems 31, 2018
Text and code embeddings by contrastive pre-training
A Neelakantan, T Xu, R Puri, A Radford, JM Han, J Tworek, Q Yuan, ...
arXiv preprint arXiv:2201.10005, 2022
Robust spatial filtering with graph convolutional neural networks
FP Such, S Sah, MA Dominguez, S Pillai, C Zhang, A Michael, ND Cahill, ...
IEEE Journal of Selected Topics in Signal Processing 11 (6), 884-896, 2017
Intelligent character recognition using fully convolutional neural networks
R Ptucha, FP Such, S Pillai, F Brockler, V Singh, P Hutkowski
Pattern recognition 88, 604-613, 2019
Generative teaching networks: Accelerating neural architecture search by learning to generate synthetic training data
FP Such, A Rawal, J Lehman, K Stanley, J Clune
International Conference on Machine Learning, 9206-9216, 2020
An atari model zoo for analyzing, visualizing, and comparing deep reinforcement learning agents
FP Such, V Madhavan, R Liu, R Wang, PS Castro, Y Li, J Zhi, L Schubert, ...
arXiv preprint arXiv:1812.07069, 2018
Generalized hidden parameter mdps: Transferable model-based rl in a handful of trials
C Perez, FP Such, T Karaletsos
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5403-5411, 2020
System and method of character recognition using fully convolutional neural networks
FP Such, R Ptucha, F Brockler, P Hutkowski, V Singh
US Patent 10,936,862, 2021
Fully convolutional networks for handwriting recognition
FP Such, D Peri, F Brockler, H Paul, R Ptucha
2018 16th International Conference on Frontiers in Handwriting Recognition …, 2018
System and method of character recognition using fully convolutional neural networks with attention
FP Such, R Ptucha, F Brockler, P Hutkowski
US Patent 10,846,523, 2020
Efficient transfer learning and online adaptation with latent variable models for continuous control
CF Perez, FP Such, T Karaletsos
arXiv preprint arXiv:1812.03399, 2018
Towards 3d convolutional neural networks with meshes
M Dominguez, FP Such, S Sah, R Ptucha
2017 IEEE international conference on image processing (ICIP), 3929-3933, 2017
Scalable parameter encoding of artificial neural networks obtained via an evolutionary process
FP Such, JM Clune, KO Stanley, E Conti, V Madhavan, JA Lehman
US Patent 10,599,975, 2020
Synthetic petri dish: a novel surrogate model for rapid architecture search
A Rawal, J Lehman, FP Such, J Clune, KO Stanley
arXiv preprint arXiv:2005.13092, 2020
Temporally steered gaussian attention for video understanding
S Sah, T Nguyen, M Dominguez, F Petroski Such, R Ptucha
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017
Training neural networks using evolution based strategies and novelty search
E Conti, V Madhavan, JM Clune, FP Such, JA Lehman, KO Stanley
US Patent 11,068,787, 2021
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