4 citations · 5 across the 6 of their papers we have counts for
6 papers
Partial Feedback Online Learning
Shihao Shao, Cong Fang, Zhouchen Lin +1
We study a new learning protocol, termed partial-feedback online learning, where each instance admits a set of acceptable labels, but the learner observes only one acceptable label…
High-Rank Irreducible Cartesian Tensor Decomposition and Bases of Equivariant Spaces
Shihao Shao, Yikang Li, Zhouchen Lin +1
Irreducible Cartesian tensors (ICTs) play a crucial role in the design of equivariant graph neural networks, as well as in theoretical chemistry and chemical physics. Meanwhile, th…
FreeCG: Free the Design Space of Clebsch-Gordan Transform for Machine Learning Force Fields
Shihao Shao, Haoran Geng, Zun Wang +1
Machine Learning Force Fields (MLFFs) are of great importance for chemistry, physics, materials science, and many other related fields. The Clebsch-Gordan Transform (CG transform)…
Global Features are All You Need for Image Retrieval and Reranking
Shihao Shao, Kaifeng Chen, Arjun Karpur +3
Image retrieval systems conventionally use a two-stage paradigm, leveraging global features for initial retrieval and local features for reranking. However, the scalability of this…
1st Place Solution in Google Universal Images Embedding
Shihao Shao, Qinghua Cui
This paper presents the 1st place solution for the Google Universal Images Embedding Competition on Kaggle. The highlighted part of our solution is based on 1) A novel way to condu…
A layer-stress learning framework universally augments deep neural network tasks
Shihao Shao, Yong Liu, Qinghua Cui
Deep neural networks (DNN) such as Multi-Layer Perception (MLP) and Convolutional Neural Networks (CNN) represent one of the most established deep learning algorithms. Given the tr…