Stanislav Fort

42 papers A* 9Journal 32Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Representations of Text and Images Align From Layer One. CoRR Evzen Wybitul, Javier Rando, Florian Tramèr, Stanislav Fort
2026	J	`jnl`	Solving adversarial examples requires solving exponential misalignment. CoRR Alessandro Salvatore, Stanislav Fort, Surya Ganguli
2025	J	`jnl`	A Note on Implementation Errors in Recent Adaptive Attacks Against Multi-Resolution Self-Ensembles. CoRR Stanislav Fort
2025	J	`jnl`	Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models. CoRR Stanislav Fort, Jonathan Whitaker
2025	J	`jnl`	Get RICH or Die Scaling: Profitably Trading Inference Compute for Robustness. CoRR Tavish McDonald, Bo Lei, Stanislav Fort, Bhavya Kailkhura, Brian R. Bartoldson
2025	A*	`conf`	Scaling Laws for Adversarial Attacks on Language Model Activations and Tokens. ICLR Stanislav Fort
2024	J	`jnl`	Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness. CoRR Stanislav Fort, Balaji Lakshminarayanan
2023	J	`jnl`	Multi-attacks: Many images + the same adversarial attack → many target labels. CoRR Stanislav Fort
2023	J	`jnl`	Scaling Laws for Adversarial Attacks on Language Model Activations. CoRR Stanislav Fort
2022	J	`jnl`	Adversarial vulnerability of powerful near out-of-distribution detection. CoRR Stanislav Fort
2022	J	`jnl`	Constitutional AI: Harmlessness from AI Feedback. CoRR 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
2022	A*	`conf`	How many degrees of freedom do we need to train deep networks: a loss landscape perspective. ICLR Brett W. Larsen, Stanislav Fort, Nic Becker, Surya Ganguli
2022	J	`jnl`	Language Models (Mostly) Know What They Know. CoRR 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
2022	J	`jnl`	Measuring Progress on Scalable Oversight for Large Language Models. CoRR 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
2022	—	`conf`	Predictability and Surprise in Large Generative Models. FAccT 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
2022	J	`jnl`	Predictability and Surprise in Large Generative Models. CoRR 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
2022	J	`jnl`	Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned. CoRR 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
2022	J	`jnl`	Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. CoRR 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
2022	J	`jnl`	What does a deep neural network confidently perceive? The effective dimension of high certainty class manifolds and their low confidence boundaries. CoRR Stanislav Fort, Ekin Dogus Cubuk, Surya Ganguli, Samuel S. Schoenholz
2021	J	`jnl`	A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection. CoRR Jie Ren, Stanislav Fort, Jeremiah Z. Liu, Abhijit Guha Roy, Shreyas Padhy, Balaji Lakshminarayanan
2021	J	`jnl`	Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes. CoRR James Lucas, Juhan Bae, Michael R. Zhang, Stanislav Fort, Richard S. Zemel, Roger B. Grosse
2021	J	`jnl`	Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error. CoRR Stanislav Fort, Andrew Brock, Razvan Pascanu, Soham De, Samuel L. Smith
2021	A*	`conf`	Exploring the Limits of Out-of-Distribution Detection. NeurIPS Stanislav Fort, Jie Ren, Balaji Lakshminarayanan
2021	J	`jnl`	Exploring the Limits of Out-of-Distribution Detection. CoRR Stanislav Fort, Jie Ren, Balaji Lakshminarayanan
2021	J	`jnl`	How many degrees of freedom do we need to train deep networks: a loss landscape perspective. CoRR Brett W. Larsen, Stanislav Fort, Nic Becker, Surya Ganguli
2021	A*	`conf`	On Monotonic Linear Interpolation of Neural Network Parameters. ICML James Lucas, Juhan Bae, Michael R. Zhang, Stanislav Fort, Richard S. Zemel, Roger B. Grosse
2021	A*	`conf`	Training independent subnetworks for robust prediction. ICLR Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew Mingbo Dai, Dustin Tran
2020	A*	`conf`	Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel. NeurIPS Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy, Surya Ganguli
2020	J	`jnl`	Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel. CoRR Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy, Surya Ganguli
2020	A*	`conf`	The Break-Even Point on Optimization Trajectories of Deep Neural Networks. ICLR Stanislaw Jastrzebski, Maciej Szymczak, Stanislav Fort, Devansh Arpit, Jacek Tabor, Kyunghyun Cho, Krzysztof J. Geras
2020	J	`jnl`	The Break-Even Point on Optimization Trajectories of Deep Neural Networks. CoRR Stanislaw Jastrzebski, Maciej Szymczak, Stanislav Fort, Devansh Arpit, Jacek Tabor, Kyunghyun Cho, Krzysztof J. Geras
2020	J	`jnl`	Training independent subnetworks for robust prediction. CoRR Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew M. Dai, Dustin Tran
2019	J	`jnl`	Deep Ensembles: A Loss Landscape Perspective. CoRR Stanislav Fort, Huiyi Hu, Balaji Lakshminarayanan
2019	J	`jnl`	Emergent properties of the local geometry of neural loss landscapes. CoRR Stanislav Fort, Surya Ganguli
2019	A*	`conf`	Large Scale Structure of Neural Network Loss Landscapes. NeurIPS Stanislav Fort, Stanislaw Jastrzebski
2019	J	`jnl`	Large Scale Structure of Neural Network Loss Landscapes. CoRR Stanislav Fort, Stanislaw Jastrzebski
2019	J	`jnl`	Stiffness: A New Perspective on Generalization in Neural Networks. CoRR Stanislav Fort, Pawel Krzysztof Nowak, Srini Narayanan
2019	A*	`conf`	The Goldilocks Zone: Towards Better Understanding of Neural Network Loss Landscapes. AAAI Stanislav Fort, Adam Scherlis
2018	J	`jnl`	Adaptive Quantum State Tomography with Neural Networks. CoRR Yihui Quek, Stanislav Fort, Hui Khoon Ng
2018	J	`jnl`	The Goldilocks zone: Towards better understanding of neural network loss landscapes. CoRR Stanislav Fort, Adam Scherlis
2017	J	`jnl`	Gaussian Prototypical Networks for Few-Shot Learning on Omniglot. CoRR Stanislav Fort
2017	J	`jnl`	Towards understanding feedback from supermassive black holes using convolutional neural networks. CoRR Stanislav Fort