Tal Remez
Artificial Intelligence · 2017–2024
A researcher in the Science corpus: 11 papers; trusts 491 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 287 citations into it; #34,542 of 35,345 by trust here, #7,657 by citations
- Signal Processing — 134 citations into it; #7,427 of 7,572 by trust here, #3,198 by citations
- Speech & Audio — 134 citations into it
- Computer Vision and Pattern Recognition — 116 citations into it; #14,425 of 21,575 by trust here, #11,721 by citations
- Natural Language Processing — 69 citations into it
- Generative Models — 44 citations into it
- Evaluation & Benchmarks — 39 citations into it
- Model Architectures — 38 citations into it
Whom Tal Remez trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Diederik P. Kingma
- Geoffrey E. Hinton
- Alex Krizhevsky
- Yoshua Bengio
- Ilya Sutskever
- Yann LeCun
- Ross B. Girshick
- Jimmy Ba
- Alex Graves
Who trusts Tal Remez overall
- Jiwoon Ahn
- Yue Wang
- Ye Jia
- Sergey I. Nikolenko
- Yeting Jia
- Sunghyun Cho
- Qianqian Dong
- Matthias Fey
- Nicholas Sharp
- Haggai Maron
Papers from Tal Remez's perspective
Ordered by how strongly the researchers Tal Remez 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
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Deep Functional Maps: Structured Prediction for Dense Shape Correspondence (2017) — 346 citations
- Code Llama: Open Foundation Models for Code (2023) — 3,394 citations
- Translatotron 2: High-quality direct speech-to-speech translation with voice preservation (2021) — 118 citations
- Textually Pretrained Speech Language Models (2023) — 112 citations
- The Llama 3 Herd of Models (2024) — 17,085 citations
- Discrete Flow Matching (2024) — 313 citations
- Simple and Controllable Music Generation (2023) — 745 citations
- EXPRESSO: A Benchmark and Analysis of Discrete Expressive Speech Resynthesis (2023) — 130 citations
- ReVISE: Self-Supervised Speech Resynthesis with Visual Input for Universal and Generalized Speech Enhancement (2022) — 15 citations
- Translatotron 2: Robust direct speech-to-speech translation (2021) — 31 citations
- Learning to Segment via Cut-and-Paste (2018) — 107 citations