Stanislav Fort
Stanislav Fort
Anthropic / Stanford University / Google Brain / DeepMind
Verified email at - Homepage
Cited by
Cited by
Deep Ensembles: A Loss Landscape Perspective
S Fort, H Hu, B Lakshminarayanan
arXiv preprint arXiv:1912.02757, 2019
Training independent subnetworks for robust prediction
M Havasi, R Jenatton, S Fort, JZ Liu, J Snoek, B Lakshminarayanan, ...
arXiv preprint arXiv:2010.06610, 2020
The Break-Even Point on Optimization Trajectories of Deep Neural Networks
S Jastrzebski, M Szymczak, S Fort, D Arpit, J Tabor, K Cho, K Geras
arXiv preprint arXiv:2002.09572, 2020
Gaussian Prototypical Networks for Few-Shot Learning on Omniglot
S Fort
arXiv preprint arXiv:1708.02735, 2017
Exploring the limits of out-of-distribution detection
S Fort, J Ren, B Lakshminarayanan
Advances in Neural Information Processing Systems 34, 7068-7081, 2021
Discovery of gamma-ray pulsations from the transitional redback PSR J1227-4853
TJ Johnson, PS Ray, J Roy, CC Cheung, AK Harding, HJ Pletsch, S Fort, ...
The Astrophysical Journal 806 (1), 91, 2015
Stiffness: A new perspective on generalization in neural networks
S Fort, PK Nowak, S Jastrzebski, S Narayanan
arXiv preprint arXiv:1901.09491, 2019
Large Scale Structure of Neural Network Loss Landscapes
S Fort, S Jastrzebski
arXiv preprint arXiv:1906.04724, 2019
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the neural tangent kernel
S Fort, GK Dziugaite, M Paul, S Kharaghani, DM Roy, S Ganguli
Advances in Neural Information Processing Systems 33, 5850-5861, 2020
Adaptive quantum state tomography with neural networks
Y Quek, S Fort, HK Ng
arXiv preprint arXiv:1812.06693, 2018
The goldilocks zone: Towards better understanding of neural network loss landscapes
S Fort, A Scherlis
Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 3574-3581, 2019
A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection
J Ren, S Fort, J Liu, AG Roy, S Padhy, B Lakshminarayanan
arXiv preprint arXiv:2106.09022, 2021
Emergent properties of the local geometry of neural loss landscapes
S Fort, S Ganguli
arXiv preprint arXiv:1910.05929, 2019
Analyzing monotonic linear interpolation in neural network loss landscapes
J Lucas, J Bae, MR Zhang, S Fort, R Zemel, R Grosse
arXiv preprint arXiv:2104.11044, 2021
Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error
S Fort, A Brock, R Pascanu, S De, SL Smith
arXiv preprint arXiv:2105.13343, 2021
Predictability and surprise in large generative models
D Ganguli, D Hernandez, L Lovitt, A Askell, Y Bai, A Chen, T Conerly, ...
2022 ACM Conference on Fairness, Accountability, and Transparency, 1747-1764, 2022
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
Y Bai, A Jones, K Ndousse, A Askell, A Chen, N DasSarma, D Drain, ...
arXiv preprint arXiv:2204.05862, 2022
The ATHENA WFI science products module
DN Burrows, S Allen, M Bautz, E Bulbul, J Erdley, AD Falcone, S Fort, ...
Space Telescopes and Instrumentation 2018: Ultraviolet to Gamma Ray 10699 …, 2018
Towards understanding feedback from supermassive black holes using convolutional neural networks
S Fort
arXiv preprint arXiv:1712.00523, 2017
How many degrees of freedom do we need to train deep networks: a loss landscape perspective
BW Larsen, S Fort, N Becker, S Ganguli
arXiv preprint arXiv:2107.05802, 2021
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