7 citations · 11 across the 4 of their papers we have counts for
4 papers
Understanding Silent Data Corruption in LLM Training
Jeffrey Ma, Hengzhi Pei, Leonard Lausen +1
As the scale of training large language models (LLMs) increases, one emergent failure is silent data corruption (SDC), where hardware produces incorrect computations without explic…
CoddLLM: Empowering Large Language Models for Data Analytics
Jiani Zhang, Hengrui Zhang, Rishav Chakravarti +6
Large Language Models (LLMs) have the potential to revolutionize data analytics by simplifying tasks such as data discovery and SQL query synthesis through natural language interac…
Revisiting SMoE Language Models by Evaluating Inefficiencies with Task Specific Expert Pruning
Soumajyoti Sarkar, Leonard Lausen, Volkan Cevher +3
Sparse Mixture of Expert (SMoE) models have emerged as a scalable alternative to dense models in language modeling. These models use conditionally activated feedforward subnetworks…
AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models
Zhiqiang Tang, Haoyang Fang, Su Zhou +5
AutoGluon-Multimodal (AutoMM) is introduced as an open-source AutoML library designed specifically for multimodal learning. Distinguished by its exceptional ease of use, AutoMM ena…