Stanislav Fort

Publication Activity (10 Years)

Years Active: 2017-2023
Publications (10 Years): 35

Top Topics

Linear Interpolation

Articulated Motion

Top Venues

Publications

Stanislav Fort
Multi-attacks: Many images + the same adversarial attack → many target labels. CoRR (2023)
Stanislav Fort
Scaling Laws for Adversarial Attacks on Language Model Activations. CoRR (2023)
Samuel R. Bowman, Jeeyoon Hyun, Ethan Perez, Edwin Chen, Craig Pettit, Scott Heiner, Kamile Lukosuite, 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 (2022)
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)
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 (2022)
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 (2022)
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 (2022)
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 (2022)
Stanislav Fort
Adversarial vulnerability of powerful near out-of-distribution detection. CoRR (2022)
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 (2022)
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 (2022)
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 Brown, Jared Kaplan, Sam McCandlish, Christopher Olah, Dario Amodei, Jack Clark
Predictability and Surprise in Large Generative Models. FAccT (2022)
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 (2021)
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)
Stanislav Fort, Andrew Brock, Razvan Pascanu, Soham De, Samuel L. Smith
Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error. CoRR (2021)
Stanislav Fort, Jie Ren, Balaji Lakshminarayanan
Exploring the Limits of Out-of-Distribution Detection. CoRR (2021)
Jie Ren, Stanislav Fort, Jeremiah Liu, Abhijit Guha Roy, Shreyas Padhy, Balaji Lakshminarayanan
A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection. CoRR (2021)
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 (2021)
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)
Stanislav Fort, Jie Ren, Balaji Lakshminarayanan
Exploring the Limits of Out-of-Distribution Detection. NeurIPS (2021)
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 (2020)
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)
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 (2020)
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 (2020)
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)
Stanislav Fort, Surya Ganguli
Emergent properties of the local geometry of neural loss landscapes. CoRR (2019)
Stanislav Fort, Stanislaw Jastrzebski
Large Scale Structure of Neural Network Loss Landscapes. CoRR (2019)
Stanislav Fort, Stanislaw Jastrzebski
Large Scale Structure of Neural Network Loss Landscapes. NeurIPS (2019)
Stanislav Fort, Adam Scherlis
The Goldilocks Zone: Towards Better Understanding of Neural Network Loss Landscapes. AAAI (2019)
Stanislav Fort, Pawel Krzysztof Nowak, Srini Narayanan
Stiffness: A New Perspective on Generalization in Neural Networks. CoRR (2019)
Stanislav Fort, Huiyi Hu, Balaji Lakshminarayanan
Deep Ensembles: A Loss Landscape Perspective. CoRR (2019)
Stanislav Fort, Adam Scherlis
The Goldilocks zone: Towards better understanding of neural network loss landscapes. CoRR (2018)
Yihui Quek, Stanislav Fort, Hui Khoon Ng
Adaptive Quantum State Tomography with Neural Networks. CoRR (2018)
Stanislav Fort
Gaussian Prototypical Networks for Few-Shot Learning on Omniglot. CoRR (2017)
Stanislav Fort
Towards understanding feedback from supermassive black holes using convolutional neural networks. CoRR (2017)