Recently, Miller et al. showed that a model's in-distribution (ID) accuracy has a strong linear
correlation with its out-of-distribution (OOD) accuracy, on several OOD benchmarks, a …

Several studies have compared the in-distribution (ID) and out-of-distribution (OOD)
performance of models in computer vision and NLP. They report a frequent positive …

Large web-sourced multimodal datasets have powered a slew of new methods for learning
general-purpose visual representations, advancing the state of the art in computer vision …

Researchers investigating example hardness have increasingly focused on the dynamics by
which neural networks learn and forget examples throughout training. Popular metrics …

Data augmentation (DA) encodes invariance and provides implicit regularization critical to a
model's performance in image classification tasks. However, while DA improves average …

Privacy-preserving machine learning aims to train models on private data without leaking
sensitive information. Differential privacy (DP) is considered the gold standard framework for …

It is important that consumers and regulators can verify the provenance of large neural
models to evaluate their capabilities and risks. We introduce the concept of a" Proof-of …

Current backdoor defense methods are evaluated against a single attack at a time. This is
unrealistic, as powerful machine learning systems are trained on large datasets scraped …

Historically, sign language machine translation has been posed as a sentence-level task:
datasets consisting of continuous narratives are chopped up and presented to the model as …

While most work on evaluating machine learning (ML) models focuses on computing
accuracy on batches of data, tracking accuracy alone in a streaming setting (ie, unbounded …