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CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review
Many specialized domains remain untouched by deep learning, as large lab...
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Measuring Mathematical Problem Solving With the MATH Dataset
Many intellectual endeavors require mathematical problem solving, but th...
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Measuring Massive Multitask Language Understanding
We propose a new test to measure a text model's multitask accuracy. The ...
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Aligning AI With Shared Human Values
We show how to assess a language model's knowledge of basic concepts of ...
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The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
We introduce three new robustness benchmarks consisting of naturally occ...
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Pretrained Transformers Improve Out-of-Distribution Robustness
Although pretrained Transformers such as BERT achieve high accuracy on i...
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AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
Modern deep neural networks can achieve high accuracy when the training ...
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A Benchmark for Anomaly Segmentation
Detecting out-of-distribution examples is important for safety-critical ...
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Testing Robustness Against Unforeseen Adversaries
Considerable work on adversarial defense has studied robustness to a fix...
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Natural Adversarial Examples
We introduce natural adversarial examples -- real-world, unmodified, and...
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Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
Self-supervision provides effective representations for downstream tasks...
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Transfer of Adversarial Robustness Between Perturbation Types
We study the transfer of adversarial robustness of deep neural networks ...
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Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
In this paper we establish rigorous benchmarks for image classifier robu...
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Using Pre-Training Can Improve Model Robustness and Uncertainty
Tuning a pre-trained network is commonly thought to improve data efficie...
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Deep Anomaly Detection with Outlier Exposure
It is important to detect and handle anomalous inputs when deploying mac...
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Open Category Detection with PAC Guarantees
Open category detection is the problem of detecting "alien" test instanc...
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Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations
In this paper we establish rigorous benchmarks for image classifier robu...
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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...
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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...
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Early Methods for Detecting Adversarial Images
Many machine learning classifiers are vulnerable to adversarial perturba...
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Adjusting for Dropout Variance in Batch Normalization and Weight Initialization
We show how to adjust for the variance introduced by dropout with correc...
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