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Jared Casper

Researcher at Nvidia

Publications -  28
Citations -  8773

Jared Casper is an academic researcher from Nvidia. The author has contributed to research in topics: Transactional memory & Software transactional memory. The author has an hindex of 18, co-authored 25 publications receiving 6648 citations. Previous affiliations of Jared Casper include Massachusetts Institute of Technology & Stanford University.

Papers
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End to end speech recognition in English and Mandarin

Dario Amodei, +33 more
TL;DR: It is shown that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech-two vastly different languages, and is competitive with the transcription of human workers when benchmarked on standard datasets.
Posted Content

Deep Speech: Scaling up end-to-end speech recognition

Awni Hannun, +10 more
- 17 Dec 2014 - 
TL;DR: Deep Speech, a state-of-the-art speech recognition system developed using end-to-end deep learning, outperforms previously published results on the widely studied Switchboard Hub5'00, achieving 16.0% error on the full test set.
Proceedings Article

Deep speech 2: end-to-end speech recognition in English and mandarin

Dario Amodei, +68 more
TL;DR: In this article, an end-to-end deep learning approach was used to recognize either English or Mandarin Chinese speech-two vastly different languages-using HPC techniques, enabling experiments that previously took weeks to now run in days.
Posted Content

Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Mohammad Shoeybi, +5 more
- 17 Sep 2019 - 
TL;DR: A simple, efficient intra-layer model parallel approach that enables training transformer models with billions of parameters and shows that careful attention to the placement of layer normalization in BERT-like models is critical to achieving increased performance as the model size grows.
Journal ArticleDOI

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Teven Le Scao, +386 more
- 09 Nov 2022 - 
TL;DR: BLOOM as discussed by the authors is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total).