b'Dan Hendrycks'

research

∙ 08/28/2023

Identifying and Mitigating the Security Risks of Generative AI

Every major technical invention resurfaces the dual-use dilemma – the ne...

0 Clark Barrett, et al. ∙

research

∙ 08/28/2023

AI Deception: A Survey of Examples, Risks, and Potential Solutions

This paper argues that a range of current AI systems have learned how to...

0 Peter S. Park, et al. ∙

research

∙ 06/21/2023

An Overview of Catastrophic AI Risks

Rapid advancements in artificial intelligence (AI) have sparked growing ...

0 Dan Hendrycks, et al. ∙

research

∙ 06/20/2023

DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Generative Pre-trained Transformer (GPT) models have exhibited exciting ...

0 Boxin Wang, et al. ∙

research

∙ 04/06/2023

Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark

Artificial agents have traditionally been trained to maximize reward, wh...

0 Alexander Pan, et al. ∙

research

∙ 03/28/2023

Natural Selection Favors AIs over Humans

For billions of years, evolution has been the driving force behind the d...

0 Dan Hendrycks, et al. ∙

research

∙ 01/02/2023

MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding

Reading comprehension of legal text can be a particularly challenging ta...

0 Steven H. Wang, et al. ∙

research

∙ 10/18/2022

How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios

In recent years, deep neural networks have demonstrated increasingly str...

0 Mantas Mazeika, et al. ∙

research

∙ 10/13/2022

OpenOOD: Benchmarking Generalized Out-of-Distribution Detection

Out-of-distribution (OOD) detection is vital to safety-critical machine ...

0 Jingkang Yang, et al. ∙

research

∙ 06/30/2022

Forecasting Future World Events with Neural Networks

Forecasting future world events is a challenging but valuable task. Fore...

0 Andy Zou, et al. ∙

research

∙ 06/17/2022

Actionable Guidance for High-Consequence AI Risk Management: Towards Standards Addressing AI Catastrophic Risks

Artificial intelligence (AI) systems can provide many beneficial capabil...

0 Anthony M. Barrett, et al. ∙

research

∙ 06/13/2022

X-Risk Analysis for AI Research

Artificial intelligence (AI) has the potential to greatly improve societ...

0 Dan Hendrycks, et al. ∙

research

∙ 12/09/2021

PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures

In real-world applications of machine learning, reliable and safe system...

3 Dan Hendrycks, et al. ∙

research

∙ 10/26/2021

A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges

Machine learning models often encounter samples that are diverged from t...

17 Mohammadreza Salehi, et al. ∙

research

∙ 10/25/2021

What Would Jiminy Cricket Do? Towards Agents That Behave Morally

When making everyday decisions, people are guided by their conscience, a...

0 Dan Hendrycks, et al. ∙

research

∙ 09/28/2021

Unsolved Problems in ML Safety

Machine learning (ML) systems are rapidly increasing in size, are acquir...

0 Dan Hendrycks, et al. ∙

research

∙ 07/23/2021

VisDA-2021 Competition Universal Domain Adaptation to Improve Performance on Out-of-Distribution Data

Progress in machine learning is typically measured by training and testi...

0 Dina Bashkirova, et al. ∙

research

∙ 05/20/2021

Measuring Coding Challenge Competence With APPS

While programming is one of the most broadly applicable skills in modern...

0 Dan Hendrycks, et al. ∙

research

∙ 03/10/2021

CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review

Many specialized domains remain untouched by deep learning, as large lab...

0 Dan Hendrycks, et al. ∙

research

∙ 03/05/2021

Measuring Mathematical Problem Solving With the MATH Dataset

Many intellectual endeavors require mathematical problem solving, but th...

0 Dan Hendrycks, et al. ∙

research

∙ 09/07/2020

Measuring Massive Multitask Language Understanding

We propose a new test to measure a text model's multitask accuracy. The ...

28 Dan Hendrycks, et al. ∙

research

∙ 08/05/2020

Aligning AI With Shared Human Values

We show how to assess a language model's knowledge of basic concepts of ...

13 Dan Hendrycks, et al. ∙

research

∙ 06/29/2020

The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization

We introduce three new robustness benchmarks consisting of naturally occ...

5 Dan Hendrycks, et al. ∙

research

∙ 04/13/2020

Pretrained Transformers Improve Out-of-Distribution Robustness

Although pretrained Transformers such as BERT achieve high accuracy on i...

0 Dan Hendrycks, et al. ∙

research

∙ 12/05/2019

AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

Modern deep neural networks can achieve high accuracy when the training ...

21 Dan Hendrycks, et al. ∙

research

∙ 11/25/2019

A Benchmark for Anomaly Segmentation

Detecting out-of-distribution examples is important for safety-critical ...

26 Dan Hendrycks, et al. ∙

research

∙ 08/21/2019

Testing Robustness Against Unforeseen Adversaries

Considerable work on adversarial defense has studied robustness to a fix...

1 Daniel Kang, et al. ∙

research

∙ 07/16/2019

Natural Adversarial Examples

We introduce natural adversarial examples -- real-world, unmodified, and...

6 Dan Hendrycks, et al. ∙

research

∙ 06/28/2019

Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty

Self-supervision provides effective representations for downstream tasks...

5 Dan Hendrycks, et al. ∙

research

∙ 05/03/2019

Transfer of Adversarial Robustness Between Perturbation Types

We study the transfer of adversarial robustness of deep neural networks ...

12 Daniel Kang, et al. ∙

research

∙ 03/28/2019

Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

In this paper we establish rigorous benchmarks for image classifier robu...

30 Dan Hendrycks, et al. ∙

research

∙ 01/28/2019

Using Pre-Training Can Improve Model Robustness and Uncertainty

Tuning a pre-trained network is commonly thought to improve data efficie...

17 Dan Hendrycks, et al. ∙

research

∙ 12/11/2018

Deep Anomaly Detection with Outlier Exposure

It is important to detect and handle anomalous inputs when deploying mac...

8 Dan Hendrycks, et al. ∙

research

∙ 08/01/2018

Open Category Detection with PAC Guarantees

Open category detection is the problem of detecting "alien" test instanc...

4 Si Liu, et al. ∙

research

∙ 07/04/2018

Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations

In this paper we establish rigorous benchmarks for image classifier robu...

0 Dan Hendrycks, et al. ∙

research

∙ 02/14/2018

Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise

The growing importance of massive datasets with the advent of deep learn...

0 Dan Hendrycks, et al. ∙

research

∙ 10/07/2016

A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

We consider the two related problems of detecting if an example is miscl...

0 Dan Hendrycks, et al. ∙

research

∙ 08/01/2016

Early Methods for Detecting Adversarial Images

Many machine learning classifiers are vulnerable to adversarial perturba...

0 Dan Hendrycks, et al. ∙

research

∙ 07/08/2016

Adjusting for Dropout Variance in Batch Normalization and Weight Initialization

We show how to adjust for the variance introduced by dropout with correc...

0 Dan Hendrycks, et al. ∙

Dan Hendrycks

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