128 citations · 323 across the 15 of their papers we have counts for
16 papers
Extreme Multi-label Learning for Semantic Matching in Product Search
Wei-Cheng Chang, Daniel Jiang, Hsiang-Fu Yu +9
We consider the problem of semantic matching in product search: given a customer query, retrieve all semantically related products from a huge catalog of size 100 million, or more.…
BoolNet: Minimizing The Energy Consumption of Binary Neural Networks
Nianhui Guo, Joseph Bethge, Haojin Yang +4
Recent works on Binary Neural Networks (BNNs) have made promising progress in narrowing the accuracy gap of BNNs to their 32-bit counterparts. However, the accuracy gains are often…
Enterprise-Scale Search: Accelerating Inference for Sparse Extreme Multi-Label Ranking Trees
Philip A. Etter, Kai Zhong, Hsiang-Fu Yu +2
Tree-based models underpin many modern semantic search engines and recommender systems due to their sub-linear inference times. In industrial applications, these models operate at…
Machine Learning for Electronic Design Automation: A Survey
Guyue Huang, Jingbo Hu, Yifan He +13
With the down-scaling of CMOS technology, the design complexity of very large-scale integrated (VLSI) is increasing. Although the application of machine learning (ML) techniques in…
PECOS: Prediction for Enormous and Correlated Output Spaces
Hsiang-Fu Yu, Kai Zhong, Jiong Zhang +2
Many large-scale applications amount to finding relevant results from an enormous output space of potential candidates. For example, finding the best matching product from a large…
Exploring the Potential of Low-bit Training of Convolutional Neural Networks
Kai Zhong, Xuefei Ning, Guohao Dai +5
In this work, we propose a low-bit training framework for convolutional neural networks, which is built around a novel multi-level scaling (MLS) tensor format. Our framework focuse…
Enabling Efficient and Flexible FPGA Virtualization for Deep Learning in the Cloud
Shulin Zeng, Guohao Dai, Hanbo Sun +5
FPGAs have shown great potential in providing low-latency and energy-efficient solutions for deep neural network (DNN) inference applications. Currently, the majority of FPGA-based…
Taming Pretrained Transformers for Extreme Multi-label Text Classification
Wei-Cheng Chang, Hsiang-Fu Yu, Kai Zhong +2
We consider the extreme multi-label text classification (XMC) problem: given an input text, return the most relevant labels from a large label collection. For example, the input te…
Binary Classification with Karmic, Threshold-Quasi-Concave Metrics
Bowei Yan, Oluwasanmi Koyejo, Kai Zhong +1
Complex performance measures, beyond the popular measure of accuracy, are increasingly being used in the context of binary classification. These complex performance measures are ty…
Nonlinear Inductive Matrix Completion based on One-layer Neural Networks
Kai Zhong, Zhao Song, Prateek Jain +1
The goal of a recommendation system is to predict the interest of a user in a given item by exploiting the existing set of ratings as well as certain user/item features. A standard…
Learning Non-overlapping Convolutional Neural Networks with Multiple Kernels
Kai Zhong, Zhao Song, Inderjit S. Dhillon
In this paper, we consider parameter recovery for non-overlapping convolutional neural networks (CNNs) with multiple kernels. We show that when the inputs follow Gaussian distribut…
Recovery Guarantees for One-hidden-layer Neural Networks
Kai Zhong, Zhao Song, Prateek Jain +2
In this paper, we consider regression problems with one-hidden-layer neural networks (1NNs). We distill some properties of activation functions that lead to $\mathit{local~strong~c…
Online Classification with Complex Metrics
Bowei Yan, Oluwasanmi Koyejo, Kai Zhong +1
We present a framework and analysis of consistent binary classification for complex and non-decomposable performance metrics such as the F-measure and the Jaccard measure. The prop…
Fast Second-order Cone Programming for Safe Mission Planning
Kai Zhong, Prateek Jain, Ashish Kapoor
This paper considers the problem of safe mission planning of dynamic systems operating under uncertain environments. Much of the prior work on achieving robust and safe control req…
Coordinate Descent Methods for Symmetric Nonnegative Matrix Factorization
Arnaud Vandaele, Nicolas Gillis, Qi Lei +2
Given a symmetric nonnegative matrix , symmetric nonnegative matrix factorization (symNMF) is the problem of finding a nonnegative matrix , usually with much fewer columns th…
Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators
Kai Zhong, Ian E. H. Yen, Inderjit S. Dhillon +1
We consider the class of optimization problems arising from computationally intensive L1-regularized M-estimators, where the function or gradient values are very expensive to compu…