activity
20172026
most citedPowerAI DDL

4 citations · 14 across the 9 of their papers we have counts for

collaborators

11 papers

cs.SE2026

Automated Customization of LLMs for Enterprise Code Repositories Using Semantic Scopes

Ulrich Finkler, Irene Manotas, Wei Zhang +3

Code completion (CC) is a task frequently used by developers when working in collaboration with LLM-based programming assistants. Despite the increased performance of LLMs on publi…

cs.CL20211 cited

Asynchronous Decentralized Distributed Training of Acoustic Models

Xiaodong Cui, Wei Zhang, Abdullah Kayi +5

Large-scale distributed training of deep acoustic models plays an important role in today's high-performance automatic speech recognition (ASR). In this paper we investigate a vari…

cs.SE2021

CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks

Ruchir Puri, David S. Kung, Geert Janssen +14

Over the last several decades, software has been woven into the fabric of every aspect of our society. As software development surges and code infrastructure of enterprise applicat…

cs.CV20202 cited

Large Scale Neural Architecture Search with Polyharmonic Splines

Ulrich Finkler, Michele Merler, Rameswar Panda +8

Neural Architecture Search (NAS) is a powerful tool to automatically design deep neural networks for many tasks, including image classification. Due to the significant computationa…

cs.CV2020

NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search

Rameswar Panda, Michele Merler, Mayoore Jaiswal +8

Neural Architecture Search (NAS) is an open and challenging problem in machine learning. While NAS offers great promise, the prohibitive computational demand of most of the existin…

cs.CV20202 cited

Map Generation from Large Scale Incomplete and Inaccurate Data Labels

Rui Zhang, Conrad Albrecht, Wei Zhang +4

Accurately and globally mapping human infrastructure is an important and challenging task with applications in routing, regulation compliance monitoring, and natural disaster respo…