Vivek Seshadri
Hardware and Architecture · 2012–2020
A researcher in the Science corpus: 9 papers; trusts 59 researchers and is trusted by 9 researchers here, as inferred from the citation graph.
Subfields
- Hardware and Architecture — 75 citations into it; #509 of 1,551 by trust here, #216 by citations
- Computer Networks and Communications — 68 citations into it; #5,765 of 9,886 by trust here, #1,744 by citations
- Artificial Intelligence — 46 citations into it; #27,001 of 35,345 by trust here, #7,797 by citations
- Computer Vision and Pattern Recognition — 37 citations into it; #16,551 of 21,575 by trust here, #5,890 by citations
- Optimization & Training — 27 citations into it; #2,163 of 2,601 by trust here, #759 by citations
- Electrical and Electronic Engineering — 23 citations into it; #9,555 of 16,539 by trust here, #4,766 by citations
- ML Systems & Hardware — 13 citations into it
- Information Systems — 11 citations into it
Whom Vivek Seshadri trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoongu Kim
- Onur Mutlu
- Moinuddin K. Qureshi
- Stijn Eyerman
- Aamer Jaleel
- Donghyuk Lee
- Alex Krizhevsky
- Dean M. Tullsen
- Yuejian Xie
- Geoffrey E. Hinton
Who trusts Vivek Seshadri overall
- Onur Mutlu
- Chang Liu
- Yoongu Kim
- Hokuto Kagaya
- Yuji Matsuda
- Donghyuk Lee
- Weiqing Min
- Yu Cao
- Keiji Yanai
- Emre Kultursay
Papers from Vivek Seshadri's perspective
Ordered by how strongly the researchers Vivek Seshadri 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)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Long Short-Term Memory
- ImageNet Large Scale Visual Recognition Challenge
- ImageNet: A large-scale hierarchical image database
Their papers
- PipeDream: generalized pipeline parallelism for DNN training (2019) — 1,228 citations
- PipeDream: Fast and Efficient Pipeline Parallel DNN Training (2018) — 317 citations
- The evicted-address filter: A unified mechanism to address both cache pollution and thrashing (2012) — 407 citations
- Tiered-latency DRAM: A low latency and low cost DRAM architecture (2013) — 303 citations
- Crowdsourcing Speech Data for Low-Resource Languages from Low-Income Workers (2020) — 47 citations
- Ambit: In-Memory Accelerator for Bulk Bitwise Operations Using Commodity DRAM Technology (2017) — 638 citations
- RowClone: Fast and energy-efficient in-DRAM bulk data copy and initialization (2013) — 491 citations
- Base-delta-immediate compression: Practical data compression for on-chip caches (2012) — 402 citations
- A case for exploiting subarray-level parallelism (SALP) in DRAM (2012) — 415 citations