Dheevatsa Mudigere

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 16 papers; trusts 237 researchers and is trusted by 47 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 292 citations into it; #5,151 of 35,345 by trust here, #1,746 by citations
• Computer Vision and Pattern Recognition — 215 citations into it; #18,862 of 21,575 by trust here, #7,584 by citations
• Optimization & Training — 141 citations into it; #739 of 2,601 by trust here, #213 by citations
• Efficiency & Compression — 82 citations into it
• Information Systems — 71 citations into it; #7,042 of 8,771 by trust here, #1,118 by citations
• Model Architectures — 46 citations into it
• Management Science and Operations Research — 44 citations into it
• Computational Mechanics — 37 citations into it

Whom Dheevatsa Mudigere trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Kaiming He
5. Yoshua Bengio
6. Yann LeCun
7. Ilya Sutskever
8. Karen Simonyan
9. L. Eon Bottou
10. Alex Graves

Who trusts Dheevatsa Mudigere overall

1. Behnam Neyshabur
2. Yang You
3. Zi-Mei Yao
4. Stanislav Fort
5. Elad Hoffer
6. Nicholas Carlini
7. Stanislaw Jastrzebski
8. A. Gholami
9. Yuanzhi Li
10. Bilge Acun

Papers from Dheevatsa Mudigere's perspective

Ordered by how strongly the researchers Dheevatsa Mudigere trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima (2016) — 3,531 citations
• Ternary Neural Networks with Fine-Grained Quantization (2017) — 112 citations
• A Study of BFLOAT16 for Deep Learning Training (2019) — 492 citations
• Mixed Precision Training of Convolutional Neural Networks using Integer Operations (2018) — 169 citations
• Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems (2020) — 107 citations
• Deep Learning Recommendation Model for Personalization and Recommendation Systems (2019) — 944 citations
• RAIL: Risk-Averse Imitation Learning (2017) — 21 citations
• DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction (2022) — 17 citations
• Software-hardware co-design for fast and scalable training of deep learning recommendation models (2021) — 209 citations
• High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models (2021) — 49 citations
• RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing (2019) — 273 citations
• Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems (2019) — 114 citations
• The Architectural Implications of Facebook's DNN-Based Personalized Recommendation (2019) — 330 citations
• Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy (2018) — 21 citations
• A Progressive Batching L-BFGS Method for Machine Learning (2018) — 176 citations
• This paper is included in the Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation.