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20182026
most citedTowards Understanding and Mitigating Social Biases in Language Models

127 citations · 213 across the 23 of their papers we have counts for

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Showing cs.LGShow all

30 papers · 1 filter

cs.LG2026

Continuous First, Discrete Later: VQ-VAEs Without Dimensional Collapse

Xinyu Zhao, Nikita Karagodin, Hamed Hassani +3

While many approaches to improve VQ-VAE performance focus on codebook size and utilization, the effect of dimensional collapse, where trained VQ-VAE representations live in an extr…

cs.LG20245 cited

IoT-LM: Large Multisensory Language Models for the Internet of Things

Shentong Mo, Russ Salakhutdinov, Louis-Philippe Morency +1

The Internet of Things (IoT) network integrating billions of smart physical devices embedded with sensors, software, and communication technologies is a critical and rapidly expand…

cs.LG20243 cited

HEMM: Holistic Evaluation of Multimodal Foundation Models

Paul Pu Liang, Akshay Goindani, Talha Chafekar +4

Multimodal foundation models that can holistically process text alongside images, video, audio, and other sensory modalities are increasingly used in a variety of real-world applic…

cs.LG20241 cited

Foundations of Multisensory Artificial Intelligence

Paul Pu Liang

Building multisensory AI systems that learn from multiple sensory inputs such as text, speech, video, real-world sensors, wearable devices, and medical data holds great promise for…

cs.LG20231 cited

Comparative Knowledge Distillation

Alex Wilf, Alex Tianyi Xu, Paul Pu Liang +3

In the era of large scale pretrained models, Knowledge Distillation (KD) serves an important role in transferring the wisdom of computationally heavy teacher models to lightweight,…

cs.LG2023

MultiIoT: Benchmarking Machine Learning for the Internet of Things

Shentong Mo, Louis-Philippe Morency, Russ Salakhutdinov +1

The next generation of machine learning systems must be adept at perceiving and interacting with the physical world through a diverse array of sensory channels. Commonly referred t…