David M. Brooks

Computer Vision and Pattern Recognition · 2000–2021

A researcher in the Science corpus: 30 papers; trusts 263 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

Computer Vision and Pattern Recognition — 232 citations into it; #12,920 of 21,575 by trust here, #3,827 by citations
Artificial Intelligence — 231 citations into it; #22,369 of 35,345 by trust here, #5,281 by citations
Electrical and Electronic Engineering — 178 citations into it; #3,597 of 16,539 by trust here, #2,937 by citations
Hardware and Architecture — 148 citations into it; #392 of 1,551 by trust here, #140 by citations
Computer Networks and Communications — 114 citations into it; #7,124 of 9,886 by trust here, #1,023 by citations
Efficiency & Compression — 104 citations into it; #1,193 of 1,264 by trust here, #483 by citations
ML Systems & Hardware — 94 citations into it; #325 of 575 by trust here, #96 by citations
Information Systems — 89 citations into it; #6,060 of 8,771 by trust here, #980 by citations

Whom David M. Brooks trusts overall

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

Who trusts David M. Brooks overall

Papers from David M. Brooks's perspective

Ordered by how strongly the researchers David M. Brooks 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)
Long Short-Term Memory
Gradient-based learning applied to document recognition
Attention is All you Need (Neural Information Processing Systems)
ImageNet: A large-scale hierarchical image database
Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Going deeper with convolutions
Learning Multiple Layers of Features from Tiny Images

Their papers

MLPerf Training Benchmark (2019) — 388 citations
Minerva: Enabling Low-Power, Highly-Accurate Deep Neural Network Accelerators (2016) — 613 citations
Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective (2018) — 626 citations
Fathom: reference workloads for modern deep learning methods (2016) — 189 citations
A dynamic compilation framework for controlling microprocessor energy and performance (2005) — 229 citations
Chasing Carbon: The Elusive Environmental Footprint of Computing (2020) — 376 citations
DeepRecSys: A System for Optimizing End-To-End At-Scale Neural Recommendation Inference (2020) — 211 citations
Power-Aware Microarchitecture: Design and Modeling Challenges for Next-Generation Microprocessors (2000) — 526 citations
Ares: A framework for quantifying the resilience of deep neural networks (2018) — 352 citations
System level analysis of fast, per-core DVFS using on-chip switching regulators (2008) — 787 citations
MaxNVM: Maximizing DNN Storage Density and Inference Efficiency with Sparse Encoding and Error Mitigation (2019) — 35 citations
MASR: A Modular Accelerator for Sparse RNNs (2019) — 48 citations
Benchmarking TPU, GPU, and CPU Platforms for Deep Learning (2019) — 325 citations
Process Variation Tolerant 3T1D-Based Cache Architectures (2007) — 323 citations
Accurate and efficient regression modeling for microarchitectural performance and power prediction (2006) — 560 citations
Dynamic-Compiler-Driven Control for Microprocessor Energy and Performance (2006) — 44 citations
Optimizing pipelines for power and performance (2002) — 160 citations
Thread motion: fine-grained power management for multi-core systems (2009) — 269 citations
RecSSD: near data processing for solid state drive based recommendation inference (2021) — 135 citations
Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference (2020) — 187 citations