Search Results for author: Ulrich Finkler

Found 10 papers, 1 papers with code

Asynchronous Decentralized Distributed Training of Acoustic Models

no code implementations21 Oct 2021 Xiaodong Cui, Wei zhang, Abdullah Kayi, Mingrui Liu, Ulrich Finkler, Brian Kingsbury, George Saon, David Kung

Specifically, we study three variants of asynchronous decentralized parallel SGD (ADPSGD), namely, fixed and randomized communication patterns on a ring as well as a delay-by-one scheme.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

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

1 code implementation25 May 2021 Ruchir Puri, David S. Kung, Geert Janssen, Wei zhang, Giacomo Domeniconi, Vladimir Zolotov, Julian Dolby, Jie Chen, Mihir Choudhury, Lindsey Decker, Veronika Thost, Luca Buratti, Saurabh Pujar, Shyam Ramji, Ulrich Finkler, Susan Malaika, Frederick Reiss

In addition to its large scale, CodeNet has a rich set of high-quality annotations to benchmark and help accelerate research in AI techniques for a variety of critical coding tasks, including code similarity and classification, code translation between a large variety of programming languages, and code performance (runtime and memory) improvement techniques.

BIG-bench Machine Learning Code Classification +1

NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search

no code implementations23 Jun 2020 Rameswar Panda, Michele Merler, Mayoore Jaiswal, Hui Wu, Kandan Ramakrishnan, Ulrich Finkler, Chun-Fu Chen, Minsik Cho, David Kung, Rogerio Feris, Bishwaranjan Bhattacharjee

The typical way of conducting large scale NAS is to search for an architectural building block on a small dataset (either using a proxy set from the large dataset or a completely different small scale dataset) and then transfer the block to a larger dataset.

Neural Architecture Search

Map Generation from Large Scale Incomplete and Inaccurate Data Labels

no code implementations20 May 2020 Rui Zhang, Conrad Albrecht, Wei zhang, Xiaodong Cui, Ulrich Finkler, David Kung, Siyuan Lu

Accurately and globally mapping human infrastructure is an important and challenging task with applications in routing, regulation compliance monitoring, and natural disaster response management etc..

Disaster Response Management

Improving Efficiency in Large-Scale Decentralized Distributed Training

no code implementations4 Feb 2020 Wei Zhang, Xiaodong Cui, Abdullah Kayi, Mingrui Liu, Ulrich Finkler, Brian Kingsbury, George Saon, Youssef Mroueh, Alper Buyuktosunoglu, Payel Das, David Kung, Michael Picheny

Decentralized Parallel SGD (D-PSGD) and its asynchronous variant Asynchronous Parallel SGD (AD-PSGD) is a family of distributed learning algorithms that have been demonstrated to perform well for large-scale deep learning tasks.

speech-recognition Speech Recognition

A Highly Efficient Distributed Deep Learning System For Automatic Speech Recognition

no code implementations10 Jul 2019 Wei Zhang, Xiaodong Cui, Ulrich Finkler, George Saon, Abdullah Kayi, Alper Buyuktosunoglu, Brian Kingsbury, David Kung, Michael Picheny

On commonly used public SWB-300 and SWB-2000 ASR datasets, ADPSGD can converge with a batch size 3X as large as the one used in SSGD, thus enable training at a much larger scale.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Distributed Deep Learning Strategies For Automatic Speech Recognition

no code implementations10 Apr 2019 Wei Zhang, Xiaodong Cui, Ulrich Finkler, Brian Kingsbury, George Saon, David Kung, Michael Picheny

We show that we can train the LSTM model using ADPSGD in 14 hours with 16 NVIDIA P100 GPUs to reach a 7. 6% WER on the Hub5- 2000 Switchboard (SWB) test set and a 13. 1% WER on the CallHome (CH) test set.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

PowerAI DDL

no code implementations7 Aug 2017 Minsik Cho, Ulrich Finkler, Sameer Kumar, David Kung, Vaibhav Saxena, Dheeraj Sreedhar

We train Resnet-101 on Imagenet 22K with 64 IBM Power8 S822LC servers (256 GPUs) in about 7 hours to an accuracy of 33. 8 % validation accuracy.

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