Gaussian Prototypical Networks for Few-Shot Learning on Omniglot. - Stanislav Fort (CoRR, 2017)

Towards understanding feedback from supermassive black holes using convolutional neural networks. - Stanislav Fort (CoRR, 2017)

Adaptive Quantum State Tomography with Neural Networks. - Yihui Quek; Stanislav Fort; Hui Khoon Ng (CoRR, 2018)

The Goldilocks Zone: Towards Better Understanding of Neural Network Loss Landscapes. - Stanislav Fort; Adam Scherlis (AAAI, 2019)

Large Scale Structure of Neural Network Loss Landscapes. - Stanislav Fort; Stanislaw Jastrzebski (NeurIPS, 2019)

Stiffness: A New Perspective on Generalization in Neural Networks. - Stanislav Fort; Pawel Krzysztof Nowak; Srini Narayanan (CoRR, 2019)

Emergent properties of the local geometry of neural loss landscapes. - Stanislav Fort; Surya Ganguli (CoRR, 2019)

Deep Ensembles: A Loss Landscape Perspective. - Stanislav Fort; Huiyi Hu; Balaji Lakshminarayanan (CoRR, 2019)

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 (ICLR, 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 001; Surya Ganguli (NeurIPS, 2020)

Training independent subnetworks for robust prediction. - Marton Havasi; Rodolphe Jenatton; Stanislav Fort; Jeremiah Zhe Liu; Jasper Snoek; Balaji Lakshminarayanan; Andrew Mingbo Dai; Dustin Tran (ICLR, 2021)

On Monotonic Linear Interpolation of Neural Network Parameters. - James Lucas; Juhan Bae; Michael R. Zhang; Stanislav Fort; Richard S. Zemel; Roger B. Grosse (ICML, 2021)

Exploring the Limits of Out-of-Distribution Detection. - Stanislav Fort; Jie Ren 006; Balaji Lakshminarayanan (NeurIPS, 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)

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)

A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection. - Jie Ren 006; Stanislav Fort; Jeremiah Z. Liu; Abhijit Guha Roy; Shreyas Padhy; Balaji Lakshminarayanan (CoRR, 2021)

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; et al. (FAccT, 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 (ICLR, 2022)

Adversarial vulnerability of powerful near out-of-distribution detection. - Stanislav Fort (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; et al. (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; et al. (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; et al. (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 001; et al. (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)

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; et al. (CoRR, 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; et al. (CoRR, 2022)

Multi-attacks: Many images + the same adversarial attack → many target labels. - Stanislav Fort (CoRR, 2023)

Scaling Laws for Adversarial Attacks on Language Model Activations. - Stanislav Fort (CoRR, 2023)

Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness. - Stanislav Fort; Balaji Lakshminarayanan (CoRR, 2024)

Scaling Laws for Adversarial Attacks on Language Model Activations and Tokens. - Stanislav Fort (ICLR, 2025)

A Note on Implementation Errors in Recent Adaptive Attacks Against Multi-Resolution Self-Ensembles. - Stanislav Fort (CoRR, 2025)

Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative Models. - Stanislav Fort; Jonathan Whitaker (CoRR, 2025)