Stanislav Fort
Stanislav Fort
Graduate student, 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
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
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
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
arXiv preprint arXiv:2010.15110, 2020
Emergent properties of the local geometry of neural loss landscapes
S Fort, S Ganguli
arXiv preprint arXiv:1910.05929, 2019
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
Training independent subnetworks for robust prediction
M Havasi, R Jenatton, S Fort, JZ Liu, J Snoek, B Lakshminarayanan, ...
arXiv preprint arXiv:2010.06610, 2020
Adaptive quantum state tomography with neural networks
Y Quek, S Fort, HK Ng
npj Quantum Information 7 (1), 1-7, 2021
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
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
Exploring the Limits of Out-of-Distribution Detection
S Fort, J Ren, B Lakshminarayanan
arXiv preprint arXiv:2106.03004, 2021
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
On Monotonic Linear Interpolation of Neural Network Parameters
JR Lucas, J Bae, MR Zhang, S Fort, R Zemel, RB Grosse
International Conference on Machine Learning, 7168-7179, 2021
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
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
Identifying charged particle background events in x-ray imaging detectors with novel machine learning algorithms
DR Wilkins, SW Allen, ED Miller, M Bautz, T Chattopadhyay, S Fort, ...
Space Telescopes and Instrumentation 2020: Ultraviolet to Gamma Ray 11444 …, 2020
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