About Me
I like board games, sim racing, and music.
Now I work in Google Core ML.
I received my PhD degree at Stony Brook University. Before that, I worked at Baidu.
Research of interests: deep learning, computer vision, natural language processing.
Google scholar page lehou [at] google.com
News
Scaling Instruction-Finetuned Language Models out on arXiv
Instruction finetuning language models on a massive number of tasks (1,800+).
We have FLAN-PaLM (8 to 540 billion parameters), FLAN-T5, FLAN-UL2, etc.
Yes, we got state-of-the-art results!
We got significantly better zero-shot and few-shot prompting performance for every model!
FLAN-PaLM can just explain its predictions (zero-shot chain-of-thought).
Code and datasets
Code for Token Dropping for Efficient BERT Pretraining. ACL 2022.
SBU shadow dataset for Large Scale Shadow Annotation And Detection Using Lazy Annotation and Stacked CNNs. PAMI 2019.
Code and data for Robust Histopathology Image Analysis: to Label or to Synthesize? Oral in CVPR. 2019.
Individual lymphocyte classification dataset, and code and data and a Pytorch implementation (Thanks to Mihir Sahasrabudhe) for Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images. Pattern Recognition. 2018.
Code for Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Reports. 2018.
Code for Squared Earth Mover's Distance Loss for Training Deep Neural Networks on Ordered-Classes. NeurIPS workshop on Learning on Distributions, Functions, Graphs and Groups. 2017.
Code for ConvNets with Smooth Adaptive Activation Functions for Regression. AISTATS. 2017.
Nucleus classification dataset for Center-Focusing Multi-task CNN with Injected Features for Classification of Glioma Nuclear Images. WACV. 2017.