Jared Casper
Artificial Intelligence · 2007–2022
A researcher in the Science corpus: 8 papers; trusts 50 researchers and is trusted by 22 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 292 citations into it; #10,380 of 35,345 by trust here, #2,028 by citations
- Natural Language Processing — 165 citations into it; #3,796 of 5,688 by trust here, #378 by citations
- Computer Vision and Pattern Recognition — 108 citations into it
- Large Language Models — 90 citations into it
- Optimization & Training — 61 citations into it; #1,226 of 2,601 by trust here, #325 by citations
- Model Architectures — 59 citations into it
- Efficiency & Compression — 50 citations into it; #439 of 1,264 by trust here, #71 by citations
- Hardware and Architecture — 42 citations into it; #1,439 of 1,551 by trust here, #520 by citations
Whom Jared Casper trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Ilya Sutskever
- Alex Krizhevsky
- Yoshua Bengio
- Alex Graves
- Diederik P. Kingma
- Yann LeCun
- Samyam Rajbhandari
- Kaiming He
- Quoc V. Le
Who trusts Jared Casper overall
- Nicholas Carlini
- Samyam Rajbhandari
- William Chan
- Quoc V. Le
- Ronan Collobert
- Tri Dao
- Song Han
- Yoshua Bengio
- J. Urgen Schmidhuber
- Ekin Dogus Cubuk
Papers from Jared Casper's perspective
Ordered by how strongly the researchers Jared Casper trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Going deeper with convolutions
Their papers
- Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism (2019) — 2,986 citations
- Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model (2022) — 859 citations
- Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM (2021) — 1,287 citations
- Reducing Activation Recomputation in Large Transformer Models (2022) — 492 citations
- BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2022) — 2,975 citations
- Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin (2015) — 3,177 citations
- Deep Speech: Scaling up end-to-end speech recognition (2014) — 2,263 citations
- An effective hybrid transactional memory system with strong isolation guarantees (2007) — 307 citations