Philip Schniter
Electrical and Electronic Engineering · 2004–2019
A researcher in the Science corpus: 9 papers; trusts 95 researchers and is trusted by 39 researchers here, as inferred from the citation graph.
Subfields
- Electrical and Electronic Engineering — 63 citations into it; #2,194 of 16,539 by trust here, #5,811 by citations
- Computer Networks and Communications — 55 citations into it; #1,104 of 9,886 by trust here, #3,318 by citations
- Computational Mechanics — 48 citations into it; #1,956 of 10,001 by trust here, #4,937 by citations
- Signal Processing — 28 citations into it; #1,816 of 7,572 by trust here, #5,340 by citations
- Computer Vision and Pattern Recognition — 26 citations into it; #10,658 of 21,575 by trust here, #14,478 by citations
- Atomic and Molecular Physics, and Optics — 9 citations into it
- Optimization & Training — 9 citations into it
- Aerospace Engineering — 8 citations into it; #5,433 of 6,421 by trust here, #5,172 by citations
Whom Philip Schniter trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David L. Donoho
- Sundeep Rangan
- Andrea Montanari
- Arian Maleki
- E.J. Candes
- Jeremy P. Vila
- Mohsen Bayati
- Arthur P. Dempster
- Michael Tüchler
- Anastasios Stamoulis
Who trusts Philip Schniter overall
- Jingxian Wu
- Lenka Zdeborov'a
- Junjie Ma
- P. Fan
- Florent Krzakala
- Sundeep Rangan
- Chongwen Huang
- Jian Zhang
- Jianhua Mo
- Christopher A. Metzler
Papers from Philip Schniter's perspective
Ordered by how strongly the researchers Philip Schniter trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Regression Shrinkage and Selection via the Lasso
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- In-Band Full-Duplex Wireless: Challenges and Opportunities (2013) — 2,209 citations
- On the achievable diversity-multiplexing tradeoff in half-duplex cooperative channels (2005) — 1,192 citations
- Channel Estimation in Broadband Millimeter Wave MIMO Systems With Few-Bit ADCs (2016) — 336 citations
- Full-duplex bidirectional MIMO: Achievable rates under limited dynamic range (2011) — 337 citations
- Vector Approximate Message Passing (2019) — 455 citations
- Low-complexity equalization of OFDM in doubly selective channels (2004) — 545 citations
- AMP-Inspired Deep Networks for Sparse Linear Inverse Problems (2016) — 428 citations
- Expectation-Maximization Gaussian-Mixture Approximate Message Passing (2012) — 423 citations
- Compressive phase retrieval via generalized approximate message passing (2012) — 313 citations