187 citations · 190 across the 6 of their papers we have counts for
6 papers
Multi-Label Self-Supervised Learning with Scene Images
Ke Zhu, Minghao Fu, Jianxin Wu
Self-supervised learning (SSL) methods targeting scene images have seen a rapid growth recently, and they mostly rely on either a dedicated dense matching mechanism or a costly uns…
Atom-by-atom protein generation and beyond with language models
Daniel Flam-Shepherd, Kevin Zhu, Alán Aspuru-Guzik
Protein language models learn powerful representations directly from sequences of amino acids. However, they are constrained to generate proteins with only the set of amino acids r…
Quantized Feature Distillation for Network Quantization
Ke Zhu, Yin-Yin He, Jianxin Wu
Neural network quantization aims to accelerate and trim full-precision neural network models by using low bit approximations. Methods adopting the quantization aware training (QAT)…
Instance-based Max-margin for Practical Few-shot Recognition
Minghao Fu, Ke Zhu, Jianxin Wu
In order to mimic the human few-shot learning (FSL) ability better and to make FSL closer to real-world applications, this paper proposes a practical FSL (pFSL) setting. pFSL is ba…
FenceNet: Fine-grained Footwork Recognition in Fencing
Kevin Zhu, Alexander Wong, John McPhee
Current data analysis for the Canadian Olympic fencing team is primarily done manually by coaches and analysts. Due to the highly repetitive, yet dynamic and subtle movements in fe…
Keeping it Simple: Language Models can learn Complex Molecular Distributions
Daniel Flam-Shepherd, Kevin Zhu, Alán Aspuru-Guzik
Deep generative models of molecules have grown immensely in popularity, trained on relevant datasets, these models are used to search through chemical space. The downstream utility…