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Filipe de Avila Belbute Peres |
Member of Technical Staff at OpenAI |
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GPT-4 Technical Report \ OpenAI \ arXiv, 2023\ [paper]
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Simple initialization and parametrization of sinusoidal networks via their kernel bandwidth \ Filipe de Avila Belbute-Peres, J. Zico Kolter \ ICLR 2023\ [paper]
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HyperPINN: Learning parameterized differential equations with physics-informed hypernetworks \ Filipe de Avila Belbute-Peres, Yifan Chen, Fei Sha \ Symbiosis of Deep Learning and Differential Equations Workshop, NeurIPS 2021 (Spotlight)\ [paper]
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Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction \ Filipe de Avila Belbute-Peres, Thomas D. Economon, J. Zico Kolter \ ICML 2020\ [paper] [code]
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Assessing the similarity of cortical object and scene representations through cross-validated voxel encoding models \ Nicholas M Blauch, Filipe de Avila Belbute-Peres, Juhi Farooqui, Alireza Chaman Zar, David Plaut, Marlene Behrmann \ Journal of Vision\ [poster] [link]
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End-to-End Differentiable Physics for Learning and Control \ Filipe de Avila Belbute-Peres, Kevin Smith, Kelsey Allen, Josh Tenenbaum, J. Zico Kolter \ NeurIPS 2018 (Spotlight)\ [paper] [code]
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A Modular Differentiable Rigid Body Physics Engine \ Filipe de Avila Belbute-Peres, J. Zico Kolter \ Deep Reinforcement Learning Symposium, NIPS 2017\ [paper] [code]
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Thinking inside the box: Motion prediction in contained spaces uses simulation\ Kevin A. Smith, Filipe de Avila Belbute-Peres, Edward Vul, Joshua B. Tenenbaum \ CogSci 2017\ [paper] [poster]