614 citations · 693 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…
HYTREL: Hypergraph-enhanced Tabular Data Representation Learning
Pei Chen, Soumajyoti Sarkar, Leonard Lausen +4
Language models pretrained on large collections of tabular data have demonstrated their effectiveness in several downstream tasks. However, many of these models do not take into ac…
Large Language Models of Code Fail at Completing Code with Potential Bugs
Tuan Dinh, Jinman Zhao, Samson Tan +4
Large language models of code (Code-LLMs) have recently brought tremendous advances to code completion, a fundamental feature of programming assistance and code intelligence. Howev…
GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing
Jian Guo, He He, Tong He +13
We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-…
NSML: A Machine Learning Platform That Enables You to Focus on Your Models
Nako Sung, Minkyu Kim, Hyunwoo Jo +8
Machine learning libraries such as TensorFlow and PyTorch simplify model implementation. However, researchers are still required to perform a non-trivial amount of manual tasks suc…