| Deep Ensembles: A Loss Landscape Perspective S Fort, H Hu, B Lakshminarayanan arXiv preprint arXiv:1912.02757, 2019 | 277 | 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 | 78 | 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 | 69 | 2020 |
| Gaussian Prototypical Networks for Few-Shot Learning on Omniglot S Fort arXiv preprint arXiv:1708.02735, 2017 | 57 | 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 | 54 | 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 | 54 | 2015 |
| Stiffness: A new perspective on generalization in neural networks S Fort, PK Nowak, S Jastrzebski, S Narayanan arXiv preprint arXiv:1901.09491, 2019 | 51 | 2019 |
| Large Scale Structure of Neural Network Loss Landscapes S Fort, S Jastrzebski arXiv preprint arXiv:1906.04724, 2019 | 46 | 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 | 45 | 2020 |
| Adaptive quantum state tomography with neural networks Y Quek, S Fort, HK Ng arXiv preprint arXiv:1812.06693, 2018 | 27 | 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 | 24 | 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 | 22 | 2021 |
| Emergent properties of the local geometry of neural loss landscapes S Fort, S Ganguli arXiv preprint arXiv:1910.05929, 2019 | 20 | 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 | 10* | 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 | 8 | 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 | 7 | 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 | 5* | 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 | 4 | 2018 |
| Towards understanding feedback from supermassive black holes using convolutional neural networks S Fort arXiv preprint arXiv:1712.00523, 2017 | 4 | 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 | 3 | 2021 |
Balaji LakshminarayananStaff Research Scientist at Google BrainVerified email at google.com
