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Federated Learning for Speech Recognition: Revisiting Current Trends Towards Large-Scale ASR

This paper was accepted at the Federated Learning in the Age of Foundation Models workshop at NeurIPS 2023. While automatic speech recognition (ASR) has...

Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration

A recent line of work shows that notions of multigroup fairness imply surprisingly strong notions of omniprediction: loss minimization guarantees that apply not...

What Algorithms can Transformers Learn? A Study in Length Generalization

This paper was accepted at the MATH workshop at NeurIPS 2023. Large language models exhibit surprising emergent generalization properties, yet also struggle on many...

SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understanding

This paper was accepted at the UniReps Workshop at NeurIPS 2023. The landscape of publicly available vision foundation models (VFMs), such as CLIP and...

Increasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs

Recent work in Natural Language Processing and Computer Vision has been using textual information – e.g., entity names and descriptions – available in...

Empirical Methods in Natural Language Processing (EMNLP) 2023

Apple is sponsoring the Empirical Methods in Natural Language Processing (EMNLP) conference, which will take place in person from December 6 to 10...

ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models

Large Language Models (LLMs) with billions of parameters have drastically transformed AI applications. However, their demanding computation during inference has raised significant challenges...

Automating Behavioral Testing in Machine Translation

Behavioral testing in NLP allows fine-grained evaluation of systems by examining their linguistic capabilities through the analysis of input-output behavior. Unfortunately, existing work...

How to Scale Your EMA

*=Equal Contributors Preserving training dynamics across batch sizes is an important tool for practical machine learning as it enables the trade-off between batch size...

Diffusion Models as Masked Audio-Video Learners

This paper was accepted at the Machine Learning for Audio Workshop at NeurIPS 2023. Over the past several years, the synchronization between audio and...

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