Vijay Janappa Reddi
Artificial Intelligence · 2005–2021
A researcher in the Science corpus: 14 papers; trusts 86 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 102 citations into it
- Computer Vision and Pattern Recognition — 76 citations into it
- Computer Networks and Communications — 50 citations into it; #5,125 of 9,886 by trust here, #905 by citations
- Reinforcement Learning — 37 citations into it
- Hardware and Architecture — 34 citations into it; #697 of 1,551 by trust here, #141 by citations
- Signal Processing — 30 citations into it
- Electrical and Electronic Engineering — 27 citations into it
- Information Systems — 17 citations into it
Whom Vijay Janappa Reddi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Richard S. Sutton
- Ronald J. Williams
- Volodymyr Mnih
- Geoffrey E. Hinton
- David Silver
- Michael L. Littman
- Sepp Hochreiter
- J. Urgen Schmidhuber
- Yoshua Bengio
- Mohit Sewak
Who trusts Vijay Janappa Reddi overall
Papers from Vijay Janappa Reddi's perspective
Ordered by how strongly the researchers Vijay Janappa Reddi trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- MLPerf Training Benchmark (2019) — 388 citations
- Benchmarking TinyML Systems: Challenges and Direction (2020) — 327 citations
- The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage (2021) — 149 citations
- MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance (2020) — 194 citations
- A dynamic compilation framework for controlling microprocessor energy and performance (2005) — 229 citations
- Dynamic-Compiler-Driven Control for Microprocessor Energy and Performance (2006) — 44 citations
- Data Engineering for Everyone (2021) — 10 citations
- MLPerf Tiny Benchmark (2021) — 350 citations
- The Vision Behind MLPerf: Understanding AI Inference Performance (2021) — 19 citations
- TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems (2020) — 717 citations
- Deep Reinforcement Learning for Cyber Security (2019) — 542 citations
- Cognitive Computing Safety: The New Horizon for Reliability / The Design and Evolution of Deep Learning Workloads (2017) — 10 citations
- GPUWattch: enabling energy optimizations in GPGPUs (2013) — 611 citations
- Pin: building customized program analysis tools with dynamic instrumentation (2005) — 4,533 citations