Stanislav Fort | DeepAI

DeepAI AI Chat

Featured Co-authors

Kyunghyun Cho
218 publications
Razvan Pascanu
87 publications
Samuel R. Bowman
79 publications
Jie Ren
66 publications
Jacek Tabor
63 publications
Richard Zemel
59 publications
Surya Ganguli
56 publications
Roger Grosse
56 publications
Daniel M. Roy
54 publications
Balaji Lakshminarayanan
51 publications
Dustin Tran
42 publications

∙ 08/04/2023

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

We show that we can easily design a single adversarial perturbation P th...

0 Stanislav Fort, et al. ∙

∙ 12/15/2022

Constitutional AI: Harmlessness from AI Feedback

As AI systems become more capable, we would like to enlist their help to...

0 Yuntao Bai, et al. ∙

∙ 11/04/2022

Measuring Progress on Scalable Oversight for Large Language Models

Developing safe and useful general-purpose AI systems will require us to...

0 Samuel R. Bowman, et al. ∙

∙ 10/11/2022

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

Deep neural network classifiers partition input space into high confiden...

0 Stanislav Fort, et al. ∙

∙ 08/23/2022

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

We describe our early efforts to red team language models in order to si...

0 Deep Ganguli, et al. ∙

∙ 07/11/2022

Language Models (Mostly) Know What They Know

We study whether language models can evaluate the validity of their own ...

12 Saurav Kadavath, et al. ∙

∙ 04/12/2022

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

We apply preference modeling and reinforcement learning from human feedb...

2 Yuntao Bai, et al. ∙

∙ 02/15/2022

Predictability and Surprise in Large Generative Models

Large-scale pre-training has recently emerged as a technique for creatin...

0 Deep Ganguli, et al. ∙

∙ 01/18/2022

Adversarial vulnerability of powerful near out-of-distribution detection

There has been a significant progress in detecting out-of-distribution (...

0 Stanislav Fort, et al. ∙

∙ 07/13/2021

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

A variety of recent works, spanning pruning, lottery tickets, and traini...

0 Brett W. Larsen, et al. ∙

∙ 06/16/2021

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

Mahalanobis distance (MD) is a simple and popular post-processing method...

0 Jie Ren, et al. ∙

∙ 06/06/2021

Exploring the Limits of Out-of-Distribution Detection

Near out-of-distribution detection (OOD) is a major challenge for deep n...

0 Stanislav Fort, et al. ∙

∙ 05/27/2021

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

In computer vision, it is standard practice to draw a single sample from...

0 Stanislav Fort, et al. ∙

∙ 04/22/2021

Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes

Linear interpolation between initial neural network parameters and conve...

5 James Lucas, et al. ∙

∙ 10/28/2020

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

In suitably initialized wide networks, small learning rates transform de...

0 Stanislav Fort, et al. ∙

∙ 10/13/2020

Training independent subnetworks for robust prediction

Recent approaches to efficiently ensemble neural networks have shown tha...

0 Marton Havasi, et al. ∙

∙ 02/21/2020

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

The early phase of training of deep neural networks is critical for thei...

18 Stanisław Jastrzębski, et al. ∙

∙ 12/05/2019

Deep Ensembles: A Loss Landscape Perspective

Deep ensembles have been empirically shown to be a promising approach fo...

10 Stanislav Fort, et al. ∙

∙ 10/14/2019

Emergent properties of the local geometry of neural loss landscapes

The local geometry of high dimensional neural network loss landscapes ca...

6 Stanislav Fort, et al. ∙

∙ 06/11/2019

Large Scale Structure of Neural Network Loss Landscapes

There are many surprising and perhaps counter-intuitive properties of op...

0 Stanislav Fort, et al. ∙

∙ 01/28/2019

Stiffness: A New Perspective on Generalization in Neural Networks

We investigate neural network training and generalization using the conc...

0 Stanislav Fort, et al. ∙

∙ 12/17/2018

Adaptive Quantum State Tomography with Neural Networks

Quantum State Tomography is the task of determining an unknown quantum s...

0 Yihui Quek, et al. ∙

∙ 07/06/2018

The Goldilocks zone: Towards better understanding of neural network loss landscapes

We explore the loss landscape of fully-connected neural networks using r...

0 Stanislav Fort, et al. ∙

∙ 12/02/2017

Towards understanding feedback from supermassive black holes using convolutional neural networks

Supermassive black holes at centers of clusters of galaxies strongly int...

0 Stanislav Fort, et al. ∙

∙ 08/09/2017

Gaussian Prototypical Networks for Few-Shot Learning on Omniglot

We propose a novel architecture for k-shot classification on the Omniglo...

0 Stanislav Fort, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world