Publications (10)
Unsupervised Classification in Hyperspectral Imagery with Nonlocal Total Variation and Primal-Dual Hybrid Gradient Algorithm
Wei Zhu, Victoria Chayes, Alexandre Tiard +6
In this paper, a graph-based nonlocal total variation method (NLTV) is proposed for unsupervised classification of hyperspectral images (HSI). The variational problem is solved by…
Hierarchical Clustering of Hyperspectral Images using Rank-Two Nonnegative Matrix Factorization
Nicolas Gillis, Da Kuang, Haesun Park
In this paper, we design a hierarchical clustering algorithm for high-resolution hyperspectral images. At the core of the algorithm, a new rank-two nonnegative matrix factorization…
piCholesky: Polynomial Interpolation of Multiple Cholesky Factors for Efficient Approximate Cross-Validation
Da Kuang, Alex Gittens, Raffay Hamid
The dominant cost in solving least-square problems using Newton's method is often that of factorizing the Hessian matrix over multiple values of the regularization parameter ()…
A Harmonic Extension Approach for Collaborative Ranking
Da Kuang, Zuoqiang Shi, Stanley Osher +1
We present a new perspective on graph-based methods for collaborative ranking for recommender systems. Unlike user-based or item-based methods that compute a weighted average of ra…
Crime Topic Modeling
Da Kuang, P. Jeffrey Brantingham, Andrea L. Bertozzi
The classification of crime into discrete categories entails a massive loss of information. Crimes emerge out of a complex mix of behaviors and situations, yet most of these detail…
Fast Clustering and Topic Modeling Based on Rank-2 Nonnegative Matrix Factorization
Da Kuang, Barry Drake, Haesun Park
The importance of unsupervised clustering and topic modeling is well recognized with ever-increasing volumes of text data. In this paper, we propose a fast method for hierarchical…
Reconstructing Cell Lineage Trees from Phenotypic Features with Metric Learning
Da Kuang, Guanwen Qiu, Junhyong Kim
How a single fertilized cell gives rise to a complex array of specialized cell types in development is a central question in biology. The cells grow, divide, and acquire differenti…
Deep Pairwise Learning To Rank For Search Autocomplete
Kai Yuan, Da Kuang
Autocomplete (a.k.a "Query Auto-Completion", "AC") suggests full queries based on a prefix typed by customer. Autocomplete has been a core feature of commercial search engine. In t…
Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs
Yehui Tang, Yichun Yin, Yaoyuan Wang +71
Sparse large language models (LLMs) with Mixture of Experts (MoE) and close to a trillion parameters are dominating the realm of most capable language models. However, the massive…
Complexity Matters: Dynamics of Feature Learning in the Presence of Spurious Correlations
GuanWen Qiu, Da Kuang, Surbhi Goel
Existing research often posits spurious features as easier to learn than core features in neural network optimization, but the impact of their relative simplicity remains under-exp…