Stanislav Fort

According to our database, Stanislav Fort authored at least 29 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness.

Stanislav Fort, Balaji Lakshminarayanan

CoRR, 2024

2023

Scaling Laws for Adversarial Attacks on Language Model Activations.

Stanislav Fort

CoRR, 2023

Multi-attacks: Many images + the same adversarial attack → many target labels.

Stanislav Fort

CoRR, 2023

2022

Constitutional AI: Harmlessness from AI Feedback.

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

CoRR, 2022

Measuring Progress on Scalable Oversight for Large Language Models.

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

CoRR, 2022

What does a deep neural network confidently perceive? The effective dimension of high certainty class manifolds and their low confidence boundaries.

Stanislav Fort, Ekin Dogus Cubuk, Surya Ganguli, Samuel S. Schoenholz

CoRR, 2022

Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned.

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

CoRR, 2022

Language Models (Mostly) Know What They Know.

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

CoRR, 2022

Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

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

CoRR, 2022

Predictability and Surprise in Large Generative Models.

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

CoRR, 2022

Adversarial vulnerability of powerful near out-of-distribution detection.

Stanislav Fort

CoRR, 2022

How many degrees of freedom do we need to train deep networks: a loss landscape perspective.

Brett W. Larsen, Stanislav Fort, Nic Becker, Surya Ganguli

Proceedings of the Tenth International Conference on Learning Representations, 2022

Predictability and Surprise in Large Generative Models.

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

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

2021

A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection.

Jie Ren, Stanislav Fort, Jeremiah Z. Liu, Abhijit Guha Roy, Shreyas Padhy, Balaji Lakshminarayanan

CoRR, 2021

Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error.

Stanislav Fort, Andrew Brock, Razvan Pascanu, Soham De, Samuel L. Smith

CoRR, 2021

Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes.

James Lucas, Juhan Bae, Michael R. Zhang, Stanislav Fort, Richard S. Zemel, Roger B. Grosse

CoRR, 2021

Exploring the Limits of Out-of-Distribution Detection.

Stanislav Fort, Jie Ren, Balaji Lakshminarayanan

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On Monotonic Linear Interpolation of Neural Network Parameters.

James Lucas, Juhan Bae, Michael R. Zhang, Stanislav Fort, Richard S. Zemel, Roger B. Grosse

Proceedings of the 38th International Conference on Machine Learning, 2021

Training independent subnetworks for robust prediction.

Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew Mingbo Dai, Dustin Tran

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel.

Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy, Surya Ganguli

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

The Break-Even Point on Optimization Trajectories of Deep Neural Networks.

Stanislaw Jastrzebski, Maciej Szymczak, Stanislav Fort, Devansh Arpit, Jacek Tabor, Kyunghyun Cho, Krzysztof J. Geras

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Deep Ensembles: A Loss Landscape Perspective.

Stanislav Fort, Huiyi Hu, Balaji Lakshminarayanan

CoRR, 2019

Emergent properties of the local geometry of neural loss landscapes.

Stanislav Fort, Surya Ganguli

CoRR, 2019

Stiffness: A New Perspective on Generalization in Neural Networks.

Stanislav Fort, Pawel Krzysztof Nowak, Srini Narayanan

CoRR, 2019

Large Scale Structure of Neural Network Loss Landscapes.

Stanislav Fort, Stanislaw Jastrzebski

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

The Goldilocks Zone: Towards Better Understanding of Neural Network Loss Landscapes.

Stanislav Fort, Adam Scherlis

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Adaptive Quantum State Tomography with Neural Networks.

Yihui Quek, Stanislav Fort, Hui Khoon Ng

CoRR, 2018

2017

Towards understanding feedback from supermassive black holes using convolutional neural networks.

Stanislav Fort

CoRR, 2017

Gaussian Prototypical Networks for Few-Shot Learning on Omniglot.

Stanislav Fort

CoRR, 2017