Avraham Ruderman
Artificial Intelligence · 2012–2025
A researcher in the Science corpus: 7 papers; trusts 29 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 93 citations into it; #31,868 of 35,345 by trust here, #28,481 by citations
- Computer Vision and Pattern Recognition — 71 citations into it; #19,479 of 21,575 by trust here, #6,206 by citations
- Reinforcement Learning — 58 citations into it
- Model Architectures — 16 citations into it; #2,000 of 2,278 by trust here, #2,268 by citations
- Control and Systems Engineering — 11 citations into it
- Statistics and Probability — 9 citations into it
- Computational Mechanics — 8 citations into it
Whom Avraham Ruderman trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Karen Simonyan
- Geoffrey E. Hinton
- Alex Krizhevsky
- Stéphane Mallat
- Yann LeCun
- Yoshua Bengio
- Kaiming He
- Andrew Zisserman
- Diederik P. Kingma
- Alex Smola
Who trusts Avraham Ruderman overall
- Richard Zhang
- Jiaming Song
- Kazuto Fukuchi
- Michael Tschannen
- Ishmael Belghazi
- Mohamed Ishmael Belghazi
- Stefano Ermon
- Valentin Thomas
- Sai Rajeswar
- Aristide Baratin
Papers from Avraham Ruderman's perspective
Ordered by how strongly the researchers Avraham Ruderman 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
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- 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)
- ImageNet: A large-scale hierarchical image database
- 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
- Neural scene representation and rendering (2018) — 674 citations
- Pooling is neither necessary nor sufficient for appropriate deformation stability in CNNs (2018) — 44 citations
- VALUATION : A N ADVERSARIAL APPROACH TO UNCOVER CATASTROPHIC FAILURES (2018) — 94 citations
- Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
- Human-level performance in 3D multiplayer games with population-based reinforcement learning (2018) — 819 citations
- Model-Free Episodic Control (2016) — 289 citations
- Tighter Variational Representations of f-Divergences via Restriction to Probability Measures (2012) — 69 citations