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ORCIDSamuel S. Schoenholz
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- affiliation: OpenAI, San Francisco, CA, USA
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2020 – today
- 2023
[j3]Amil Merchant
, Simon L. Batzner, Samuel S. Schoenholz, Muratahan Aykol, Gowoon Cheon, Ekin Dogus Cubuk:
Scaling deep learning for materials discovery. Nat. 624(7990): 80-85 (2023)- [j2]Atish Agarwala, Samuel Stern Schoenholz, Jeffrey Pennington, Yann N. Dauphin:
Temperature check: theory and practice for training models with softmax-cross-entropy losses. Trans. Mach. Learn. Res. 2023 (2023) - [c25]Mehmet Cagri Kaymak, Samuel S. Schoenholz, Ekin D. Cubuk, Kurt A. O'Hearn, Kenneth M. Merz Jr., Hasan Metin Aktulga:
End-to-End Differentiable Reactive Molecular Dynamics Simulations Using JAX. ISC 2023: 202-219 - 2022
- [j1]Sean Mann, Eric Fadel, Samuel S. Schoenholz, Ekin D. Cubuk, Steven G. Johnson, Giuseppe Romano:
∂PV: An end-to-end differentiable solar-cell simulator. Comput. Phys. Commun. 272: 108232 (2022) - [c24]Atish Agarwala, Samuel S. Schoenholz:
Deep equilibrium networks are sensitive to initialization statistics. ICML 2022: 136-160 - [c23]Roman Novak, Jascha Sohl-Dickstein, Samuel S. Schoenholz:
Fast Finite Width Neural Tangent Kernel. ICML 2022: 17018-17044 - [i27]Roman Novak, Jascha Sohl-Dickstein, Samuel S. Schoenholz:
Fast Finite Width Neural Tangent Kernel. CoRR abs/2206.08720 (2022) - [i26]Atish Agarwala, Samuel S. Schoenholz:
Deep equilibrium networks are sensitive to initialization statistics. CoRR abs/2207.09432 (2022) - [i25]Stanislav Fort, Ekin Dogus Cubuk, Surya Ganguli, Samuel S. Schoenholz:
What does a deep neural network confidently perceive? The effective dimension of high certainty class manifolds and their low confidence boundaries. CoRR abs/2210.05546 (2022) - 2021
- [c22]Amil Merchant, Luke Metz, Samuel S. Schoenholz, Ekin D. Cubuk:
Learn2Hop: Learned Optimization on Rough Landscapes. ICML 2021: 7643-7653 - [c21]Miguel Ruiz-Garcia, Ge Zhang, Samuel S. Schoenholz, Andrea J. Liu:
Tilting the playing field: Dynamical loss functions for machine learning. ICML 2021: 9157-9167 - [c20]Neha S. Wadia, Daniel Duckworth, Samuel S. Schoenholz, Ethan Dyer, Jascha Sohl-Dickstein:
Whitening and Second Order Optimization Both Make Information in the Dataset Unusable During Training, and Can Reduce or Prevent Generalization. ICML 2021: 10617-10629 - [i24]Miguel Ruiz-Garcia, Ge Zhang, Samuel S. Schoenholz, Andrea J. Liu:
Tilting the playing field: Dynamical loss functions for machine learning. CoRR abs/2102.03793 (2021) - [i23]Amil Merchant, Luke Metz, Samuel S. Schoenholz, Ekin Dogus Cubuk:
Learn2Hop: Learned Optimization on Rough Landscapes. CoRR abs/2107.09661 (2021) - [i22]James Martens, Andy Ballard, Guillaume Desjardins, Grzegorz Swirszcz, Valentin Dalibard, Jascha Sohl-Dickstein, Samuel S. Schoenholz:
Rapid training of deep neural networks without skip connections or normalization layers using Deep Kernel Shaping. CoRR abs/2110.01765 (2021) - [i21]Luke Metz, C. Daniel Freeman, Samuel S. Schoenholz, Tal Kachman:
Gradients are Not All You Need. CoRR abs/2111.05803 (2021) - 2020
- [c19]Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz:
Neural Tangents: Fast and Easy Infinite Neural Networks in Python. ICLR 2020 - [c18]Lechao Xiao, Jeffrey Pennington, Samuel Stern Schoenholz:
Disentangling Trainability and Generalization in Deep Neural Networks. ICML 2020: 10462-10472 - [c17]Jaehoon Lee, Samuel S. Schoenholz, Jeffrey Pennington, Ben Adlam, Lechao Xiao, Roman Novak, Jascha Sohl-Dickstein:
Finite Versus Infinite Neural Networks: an Empirical Study. NeurIPS 2020 - [c16]Samuel S. Schoenholz, Ekin Dogus Cubuk:
JAX MD: A Framework for Differentiable Physics. NeurIPS 2020 - [i20]Jascha Sohl-Dickstein, Roman Novak, Samuel S. Schoenholz, Jaehoon Lee:
On the infinite width limit of neural networks with a standard parameterization. CoRR abs/2001.07301 (2020) - [i19]Jaehoon Lee, Samuel S. Schoenholz, Jeffrey Pennington, Ben Adlam, Lechao Xiao, Roman Novak, Jascha Sohl-Dickstein:
Finite Versus Infinite Neural Networks: an Empirical Study. CoRR abs/2007.15801 (2020) - [i18]Neha S. Wadia, Daniel Duckworth, Samuel S. Schoenholz, Ethan Dyer, Jascha Sohl-Dickstein:
Whitening and second order optimization both destroy information about the dataset, and can make generalization impossible. CoRR abs/2008.07545 (2020) - [i17]Atish Agarwala, Jeffrey Pennington, Yann N. Dauphin, Samuel S. Schoenholz:
Temperature check: theory and practice for training models with softmax-cross-entropy losses. CoRR abs/2010.07344 (2020)
2010 – 2019
- 2019
- [c15]Greg Yang, Jeffrey Pennington, Vinay Rao, Jascha Sohl-Dickstein, Samuel S. Schoenholz:
A Mean Field Theory of Batch Normalization. ICLR (Poster) 2019 - [c14]Jaehoon Lee, Lechao Xiao, Samuel S. Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, Jeffrey Pennington:
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent. NeurIPS 2019: 8570-8581 - [c13]Yann N. Dauphin, Samuel S. Schoenholz:
MetaInit: Initializing learning by learning to initialize. NeurIPS 2019: 12624-12636 - [i16]Dar Gilboa, Bo Chang, Minmin Chen, Greg Yang, Samuel S. Schoenholz, Ed H. Chi, Jeffrey Pennington:
Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs. CoRR abs/1901.08987 (2019) - [i15]Jaehoon Lee, Lechao Xiao, Samuel S. Schoenholz, Yasaman Bahri, Jascha Sohl-Dickstein, Jeffrey Pennington:
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent. CoRR abs/1902.06720 (2019) - [i14]Greg Yang, Jeffrey Pennington, Vinay Rao, Jascha Sohl-Dickstein, Samuel S. Schoenholz:
A Mean Field Theory of Batch Normalization. CoRR abs/1902.08129 (2019) - [i13]Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz:
Neural Tangents: Fast and Easy Infinite Neural Networks in Python. CoRR abs/1912.02803 (2019) - [i12]Lechao Xiao, Jeffrey Pennington, Samuel S. Schoenholz:
Disentangling trainability and generalization in deep learning. CoRR abs/1912.13053 (2019) - 2018
- [c12]Jeffrey Pennington, Samuel S. Schoenholz, Surya Ganguli:
The emergence of spectral universality in deep networks. AISTATS 2018: 1924-1932 - [c11]Ekin Dogus Cubuk, Barret Zoph, Samuel S. Schoenholz, Quoc V. Le:
Intriguing Properties of Adversarial Examples. ICLR (Workshop) 2018 - [c10]Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian J. Goodfellow:
Adversarial Spheres. ICLR (Workshop) 2018 - [c9]Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel S. Schoenholz, Jeffrey Pennington, Jascha Sohl-Dickstein:
Deep Neural Networks as Gaussian Processes. ICLR (Poster) 2018 - [c8]Minmin Chen, Jeffrey Pennington, Samuel S. Schoenholz:
Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks. ICML 2018: 872-881 - [c7]Lechao Xiao, Yasaman Bahri, Jascha Sohl-Dickstein, Samuel S. Schoenholz, Jeffrey Pennington:
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10, 000-Layer Vanilla Convolutional Neural Networks. ICML 2018: 5389-5398 - [i11]Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian J. Goodfellow:
Adversarial Spheres. CoRR abs/1801.02774 (2018) - [i10]Jeffrey Pennington, Samuel S. Schoenholz, Surya Ganguli:
The Emergence of Spectral Universality in Deep Networks. CoRR abs/1802.09979 (2018) - [i9]Lechao Xiao, Yasaman Bahri, Jascha Sohl-Dickstein, Samuel S. Schoenholz, Jeffrey Pennington:
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10, 000-Layer Vanilla Convolutional Neural Networks. CoRR abs/1806.05393 (2018) - [i8]Minmin Chen, Jeffrey Pennington, Samuel S. Schoenholz:
Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks. CoRR abs/1806.05394 (2018) - 2017
- [c6]Justin Gilmer, Colin Raffel, Samuel S. Schoenholz, Maithra Raghu, Jascha Sohl-Dickstein:
Explaining the Learning Dynamics of Direct Feedback Alignment. ICLR (Workshop) 2017 - [c5]Samuel S. Schoenholz, Justin Gilmer, Surya Ganguli, Jascha Sohl-Dickstein:
Deep Information Propagation. ICLR (Poster) 2017 - [c4]Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl:
Neural Message Passing for Quantum Chemistry. ICML 2017: 1263-1272 - [c3]Jeffrey Pennington, Samuel S. Schoenholz, Surya Ganguli:
Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice. NIPS 2017: 4785-4795 - [c2]Greg Yang, Samuel S. Schoenholz:
Mean Field Residual Networks: On the Edge of Chaos. NIPS 2017: 7103-7114 - [i7]Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl:
Neural Message Passing for Quantum Chemistry. CoRR abs/1704.01212 (2017) - [i6]Samuel S. Schoenholz, Jeffrey Pennington, Jascha Sohl-Dickstein:
A Correspondence Between Random Neural Networks and Statistical Field Theory. CoRR abs/1710.06570 (2017) - [i5]Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel S. Schoenholz, Jeffrey Pennington, Jascha Sohl-Dickstein:
Deep Neural Networks as Gaussian Processes. CoRR abs/1711.00165 (2017) - [i4]Ekin Dogus Cubuk, Barret Zoph, Samuel S. Schoenholz, Quoc V. Le:
Intriguing Properties of Adversarial Examples. CoRR abs/1711.02846 (2017) - [i3]Jeffrey Pennington, Samuel S. Schoenholz, Surya Ganguli:
Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice. CoRR abs/1711.04735 (2017) - [i2]Greg Yang, Samuel S. Schoenholz:
Mean Field Residual Networks: On the Edge of Chaos. CoRR abs/1712.08969 (2017) - 2016
- [i1]Samuel S. Schoenholz, Justin Gilmer, Surya Ganguli, Jascha Sohl-Dickstein:
Deep Information Propagation. CoRR abs/1611.01232 (2016)
2000 – 2009
- 2009
- [c1]M. Ani Hsieh, Ádám M. Halász, Ekin Dogus Cubuk, Samuel S. Schoenholz, Alcherio Martinoli:
Specialization as an optimal strategy under varying external conditions. ICRA 2009: 1941-1946
Coauthor Index
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last updated on 2024-04-25 02:06 CEST by the dblp team
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