Bingliang Zhang

I am an undergraduate student in Yao Class (a special computer science pivot class found by Andrew Chi-Chih Yao) at Tsinghua University. I was visiting Carnegie Mellon University in spring semester of my junior year, where I worked with Jun-Yan Zhu, Eli Shechtman and Richard Zhang.

I am an incoming computer science PhD student at Caltech in CMS, advised by Yang Song. My research interests lie in computer vision especifically generative modeling, neural 3D shape representations and few-shot learning.

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An efficient method for ablating previously learnt concepts by providing the natural language description. The concept can be a type of artistic style, an object instance or a specific memorized image.

An efficient method for augmenting existing text-to-image models to learn new concepts with few examples without forgeting learned knowledge. Also we propose a close-form optimization strategy for combining learned weights of multiple concepts.

A paradigm to discover diverse strategies in complex RL environments. Our method is able to discover a wide spectrum of strategies in a variety of domains.

We propose a two-stage Search Supervised Network (Cryo2SSNet), an auto-encoder architecture to reconstruct a 3D density map in a two-stage pipeline. Our method can achieve a high reconstruction accuracy and efficiency.

Improved the previous Dense Passage Retriever with Generation-Augmented Retrieval and propose a modified contrastive loss. Our proposed method can improve around 13.1% on top 100 retrieval accuracy.

A simplified version of “AlphaGo” on Gomuko, using min-max search data as supervision. It can achieve intelligent human-machine competition and adjustable difficulty.

Website credits: Jon Barron. Thanks to him!