Malte Schwarzkopf
Computer Networks and Communications · 2011–2018
A researcher in the Science corpus: 3 papers; trusts 17 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Computer Networks and Communications — 38 citations into it; #8,537 of 9,886 by trust here, #2,761 by citations
- Information Systems — 31 citations into it; #7,331 of 8,771 by trust here, #2,471 by citations
- Artificial Intelligence — 30 citations into it
- Reinforcement Learning — 20 citations into it
- ML Systems & Hardware — 14 citations into it
- Computer Vision and Pattern Recognition — 13 citations into it
- Hardware and Architecture — 13 citations into it; #1,245 of 1,551 by trust here, #494 by citations
- Optimization & Training — 5 citations into it
Whom Malte Schwarzkopf trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ronald J. Williams
- Muthu Dayalan
- Richard S. Sutton
- Radford M. Neal
- Matei A. Zaharia
- Daniel Peng
- Hongzi Mao
- Geoffrey E. Hinton
- Pieter Abbeel
- Diederik P. Kingma
Who trusts Malte Schwarzkopf overall
- Simon Shillaker
- Francesco Pace
- Peter R. Pietzuch
- Philipp Moritz
- Sheng-Chun Kao
- Christina Delimitrou
- Myeongjae Jeon
- Daniele Venzano
Papers from Malte Schwarzkopf's perspective
Ordered by how strongly the researchers Malte Schwarzkopf 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Human-level control through deep reinforcement learning
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Learning scheduling algorithms for data processing clusters (2018) — 812 citations
- Omega: flexible, scalable schedulers for large compute clusters (2013) — 789 citations
- CIEL: A Universal Execution Engine for Distributed Data-Flow Computing (2011) — 335 citations