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Stanislav Fort
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2020 – today
- 2024
- [i27]Stanislav Fort, Balaji Lakshminarayanan:
Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness. CoRR abs/2408.05446 (2024) - 2023
- [i26]Stanislav Fort:
Multi-attacks: Many images + the same adversarial attack → many target labels. CoRR abs/2308.03792 (2023) - [i25]Stanislav Fort:
Scaling Laws for Adversarial Attacks on Language Model Activations. CoRR abs/2312.02780 (2023) - 2022
- [c9]Deep Ganguli, Danny Hernandez, Liane Lovitt, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Nelson Elhage, Sheer El Showk, Stanislav Fort, Zac Hatfield-Dodds, Tom Henighan, Scott Johnston, Andy Jones, Nicholas Joseph, Jackson Kernian, Shauna Kravec, Ben Mann, Neel Nanda, Kamal Ndousse, Catherine Olsson, Daniela Amodei, Tom B. Brown, Jared Kaplan, Sam McCandlish, Christopher Olah, Dario Amodei, Jack Clark:
Predictability and Surprise in Large Generative Models. FAccT 2022: 1747-1764 - [c8]Brett W. Larsen, Stanislav Fort, Nic Becker, Surya Ganguli:
How many degrees of freedom do we need to train deep networks: a loss landscape perspective. ICLR 2022 - [i24]Stanislav Fort:
Adversarial vulnerability of powerful near out-of-distribution detection. CoRR abs/2201.07012 (2022) - [i23]Deep Ganguli, Danny Hernandez, Liane Lovitt, Nova DasSarma, Tom Henighan, Andy Jones, Nicholas Joseph, Jackson Kernion, Benjamin Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Dawn Drain, Nelson Elhage, Sheer El Showk, Stanislav Fort, Zac Hatfield-Dodds, Scott Johnston, Shauna Kravec, Neel Nanda, Kamal Ndousse, Catherine Olsson, Daniela Amodei, Dario Amodei, Tom B. Brown, Jared Kaplan, Sam McCandlish, Chris Olah, Jack Clark:
Predictability and Surprise in Large Generative Models. CoRR abs/2202.07785 (2022) - [i22]Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, Nicholas Joseph, Saurav Kadavath, Jackson Kernion, Tom Conerly, Sheer El Showk, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Tristan Hume, Scott Johnston, Shauna Kravec, Liane Lovitt, Neel Nanda, Catherine Olsson, Dario Amodei, Tom B. Brown, Jack Clark, Sam McCandlish, Chris Olah, Benjamin Mann, Jared Kaplan:
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. CoRR abs/2204.05862 (2022) - [i21]Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, Scott Johnston, Sheer El Showk, Andy Jones, Nelson Elhage, Tristan Hume, Anna Chen, Yuntao Bai, Sam Bowman, Stanislav Fort, Deep Ganguli, Danny Hernandez, Josh Jacobson, Jackson Kernion, Shauna Kravec, Liane Lovitt, Kamal Ndousse, Catherine Olsson, Sam Ringer, Dario Amodei, Tom Brown, Jack Clark, Nicholas Joseph, Ben Mann, Sam McCandlish, Chris Olah, Jared Kaplan:
Language Models (Mostly) Know What They Know. CoRR abs/2207.05221 (2022) - [i20]Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, Andy Jones, Sam Bowman, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Nelson Elhage, Sheer El Showk, Stanislav Fort, Zac Hatfield-Dodds, Tom Henighan, Danny Hernandez, Tristan Hume, Josh Jacobson, Scott Johnston, Shauna Kravec, Catherine Olsson, Sam Ringer, Eli Tran-Johnson, Dario Amodei, Tom Brown, Nicholas Joseph, Sam McCandlish, Chris Olah, Jared Kaplan, Jack Clark:
Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned. CoRR abs/2209.07858 (2022) - [i19]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) - [i18]Samuel R. Bowman, Jeeyoon Hyun, Ethan Perez, Edwin Chen, Craig Pettit, Scott Heiner, Kamile Lukosiute, Amanda Askell, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Christopher Olah, Daniela Amodei, Dario Amodei, Dawn Drain, Dustin Li, Eli Tran-Johnson, Jackson Kernion, Jamie Kerr, Jared Mueller, Jeffrey Ladish, Joshua Landau, Kamal Ndousse, Liane Lovitt, Nelson Elhage, Nicholas Schiefer, Nicholas Joseph, Noemí Mercado, Nova DasSarma, Robin Larson, Sam McCandlish, Sandipan Kundu, Scott Johnston, Shauna Kravec, Sheer El Showk, Stanislav Fort, Timothy Telleen-Lawton, Tom Brown, Tom Henighan, Tristan Hume, Yuntao Bai, Zac Hatfield-Dodds, Ben Mann, Jared Kaplan:
Measuring Progress on Scalable Oversight for Large Language Models. CoRR abs/2211.03540 (2022) - [i17]Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Carol Chen, Catherine Olsson, Christopher Olah, Danny Hernandez, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jamie Kerr, Jared Mueller, Jeffrey Ladish, Joshua Landau, Kamal Ndousse, Kamile Lukosiute, Liane Lovitt, Michael Sellitto, Nelson Elhage, Nicholas Schiefer, Noemí Mercado, Nova DasSarma, Robert Lasenby, Robin Larson, Sam Ringer, Scott Johnston, Shauna Kravec, Sheer El Showk, Stanislav Fort, Tamera Lanham, Timothy Telleen-Lawton, Tom Conerly, Tom Henighan, Tristan Hume, Samuel R. Bowman, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, Jared Kaplan:
Constitutional AI: Harmlessness from AI Feedback. CoRR abs/2212.08073 (2022) - 2021
- [c7]Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew Mingbo Dai, Dustin Tran:
Training independent subnetworks for robust prediction. ICLR 2021 - [c6]James Lucas, Juhan Bae, Michael R. Zhang, Stanislav Fort, Richard S. Zemel, Roger B. Grosse:
On Monotonic Linear Interpolation of Neural Network Parameters. ICML 2021: 7168-7179 - [c5]Stanislav Fort, Jie Ren, Balaji Lakshminarayanan:
Exploring the Limits of Out-of-Distribution Detection. NeurIPS 2021: 7068-7081 - [i16]James Lucas, Juhan Bae, Michael R. Zhang, Stanislav Fort, Richard S. Zemel, Roger B. Grosse:
Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes. CoRR abs/2104.11044 (2021) - [i15]Stanislav Fort, Andrew Brock, Razvan Pascanu, Soham De, Samuel L. Smith:
Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error. CoRR abs/2105.13343 (2021) - [i14]Stanislav Fort, Jie Ren, Balaji Lakshminarayanan:
Exploring the Limits of Out-of-Distribution Detection. CoRR abs/2106.03004 (2021) - [i13]Jie Ren, Stanislav Fort, Jeremiah Z. Liu, Abhijit Guha Roy, Shreyas Padhy, Balaji Lakshminarayanan:
A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection. CoRR abs/2106.09022 (2021) - [i12]Brett W. Larsen, Stanislav Fort, Nic Becker, Surya Ganguli:
How many degrees of freedom do we need to train deep networks: a loss landscape perspective. CoRR abs/2107.05802 (2021) - 2020
- [c4]Stanislaw Jastrzebski, Maciej Szymczak, Stanislav Fort, Devansh Arpit, Jacek Tabor, Kyunghyun Cho, Krzysztof J. Geras:
The Break-Even Point on Optimization Trajectories of Deep Neural Networks. ICLR 2020 - [c3]Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy, Surya Ganguli:
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel. NeurIPS 2020 - [i11]Stanislaw Jastrzebski, Maciej Szymczak, Stanislav Fort, Devansh Arpit, Jacek Tabor, Kyunghyun Cho, Krzysztof J. Geras:
The Break-Even Point on Optimization Trajectories of Deep Neural Networks. CoRR abs/2002.09572 (2020) - [i10]Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew M. Dai, Dustin Tran:
Training independent subnetworks for robust prediction. CoRR abs/2010.06610 (2020) - [i9]Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy, Surya Ganguli:
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel. CoRR abs/2010.15110 (2020)
2010 – 2019
- 2019
- [c2]Stanislav Fort, Adam Scherlis:
The Goldilocks Zone: Towards Better Understanding of Neural Network Loss Landscapes. AAAI 2019: 3574-3581 - [c1]Stanislav Fort, Stanislaw Jastrzebski:
Large Scale Structure of Neural Network Loss Landscapes. NeurIPS 2019: 6706-6714 - [i8]Stanislav Fort, Pawel Krzysztof Nowak, Srini Narayanan:
Stiffness: A New Perspective on Generalization in Neural Networks. CoRR abs/1901.09491 (2019) - [i7]Stanislav Fort, Stanislaw Jastrzebski:
Large Scale Structure of Neural Network Loss Landscapes. CoRR abs/1906.04724 (2019) - [i6]Stanislav Fort, Surya Ganguli:
Emergent properties of the local geometry of neural loss landscapes. CoRR abs/1910.05929 (2019) - [i5]Stanislav Fort, Huiyi Hu, Balaji Lakshminarayanan:
Deep Ensembles: A Loss Landscape Perspective. CoRR abs/1912.02757 (2019) - 2018
- [i4]Stanislav Fort, Adam Scherlis:
The Goldilocks zone: Towards better understanding of neural network loss landscapes. CoRR abs/1807.02581 (2018) - [i3]Yihui Quek, Stanislav Fort, Hui Khoon Ng:
Adaptive Quantum State Tomography with Neural Networks. CoRR abs/1812.06693 (2018) - 2017
- [i2]Stanislav Fort:
Gaussian Prototypical Networks for Few-Shot Learning on Omniglot. CoRR abs/1708.02735 (2017) - [i1]Stanislav Fort:
Towards understanding feedback from supermassive black holes using convolutional neural networks. CoRR abs/1712.00523 (2017)
Coauthor Index
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