56 citations · 146 across the 29 of their papers we have counts for
7 papers · 1 filter
FARM: Enhancing Molecular Representations with Functional Group Awareness
Thao Nguyen, Kuan-Hao Huang, Ge Liu +3
We introduce Functional Group-Aware Representations for Small Molecules (FARM), a novel foundation model designed to bridge the gap between SMILES, natural language, and molecular…
A Comparative Survey of Deep Active Learning
Xueying Zhan, Qingzhong Wang, Kuan-hao Huang +3
While deep learning (DL) is data-hungry and usually relies on extensive labeled data to deliver good performance, Active Learning (AL) reduces labeling costs by selecting a small p…
DeepAL: Deep Active Learning in Python
Kuan-Hao Huang
We present DeepAL, a Python library that implements several common strategies for active learning, with a particular emphasis on deep active learning. DeepAL provides a simple and…
JECL: Joint Embedding and Cluster Learning for Image-Text Pairs
Sean T. Yang, Kuan-Hao Huang, Bill Howe
We propose JECL, a method for clustering image-caption pairs by training parallel encoders with regularized clustering and alignment objectives, simultaneously learning both repres…
Dynamic Principal Projection for Cost-Sensitive Online Multi-Label Classification
Hong-Min Chu, Kuan-Hao Huang, Hsuan-Tien Lin
We study multi-label classification (MLC) with three important real-world issues: online updating, label space dimensional reduction (LSDR), and cost-sensitivity. Current MLC algor…
Cost-Sensitive Reference Pair Encoding for Multi-Label Learning
Yao-Yuan Yang, Kuan-Hao Huang, Chih-Wei Chang +1
Label space expansion for multi-label classification (MLC) is a methodology that encodes the original label vectors to higher dimensional codes before training and decodes the pred…
Cost-Sensitive Label Embedding for Multi-Label Classification
Kuan-Hao Huang, Hsuan-Tien Lin
Label embedding (LE) is an important family of multi-label classification algorithms that digest the label information jointly for better performance. Different real-world applicat…