Tijmen Blankevoort

Artificial Intelligence · 2018–2024

A researcher in the Science corpus: 15 papers; trusts 1,067 researchers and is trusted by 56 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 315 citations into it; #3,955 of 35,345 by trust here, #3,842 by citations
• Computer Vision and Pattern Recognition — 294 citations into it; #3,803 of 21,575 by trust here, #3,017 by citations
• Efficiency & Compression — 200 citations into it; #219 of 1,264 by trust here, #372 by citations
• Model Architectures — 87 citations into it
• Optimization & Training — 45 citations into it
• Large Language Models — 34 citations into it
• Electrical and Electronic Engineering — 25 citations into it
• Representation & Self-Supervised Learning — 25 citations into it; #1,793 of 3,145 by trust here, #1,479 by citations

Whom Tijmen Blankevoort trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Diederik P. Kingma
6. Yann LeCun
7. X. Zhang
8. Ilya Sutskever
9. Max Welling
10. Christian Szegedy

Who trusts Tijmen Blankevoort overall

1. Markus Nagel
2. Gyuhak Kim
3. Mart van Baalen
4. Yelysei Bondarenko
5. Elias Frantar
6. Zi-Mei Yao
7. Thomas Verelst
8. Jiangpeng He
9. Ali Ehteshami Bejnordi
10. Marios Fournarakis

Papers from Tijmen Blankevoort's perspective

Ordered by how strongly the researchers Tijmen Blankevoort trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Conditional Channel Gated Networks for Task-Aware Continual Learning (2020) — 208 citations
• Data-Free Quantization Through Weight Equalization and Bias Correction (2019) — 645 citations
• Understanding and Overcoming the Challenges of Efficient Transformer Quantization (2021) — 196 citations
• Up or Down? Adaptive Rounding for Post-Training Quantization (2020) — 883 citations
• Batch-shaping for learning conditional channel gated networks (2019) — 84 citations
• LSQ+: Improving low-bit quantization through learnable offsets and better initialization (2020) — 310 citations
• Relaxed Quantization for Discretized Neural Networks (2018) — 149 citations
• Taxonomy and Evaluation of Structured Compression of Convolutional Neural Networks (2019) — 27 citations
• SpinQuant: LLM quantization with learned rotations (2024) — 438 citations
• VeRA: Vector-based Random Matrix Adaptation (2023) — 345 citations
• Overcoming Oscillations in Quantization-Aware Training (2022) — 171 citations
• A White Paper on Neural Network Quantization (2021) — 894 citations
• Differentiable Joint Pruning and Quantization for Hardware Efficiency (2020) — 94 citations
• Bayesian Bits: Unifying Quantization and Pruning (2020) — 143 citations
• Batch-Shaped Channel Gated Networks (2019) — 9 citations