10 citations · 35 across the 26 of their papers we have counts for
11 papers · 1 filter
Representing short-term movement to reveal animal identity and learning progress
Ping Zhang, Quang-Huy Nguyen, Yingtie Lei +5
How learning progresses over time and varies across individuals is a longstanding question in cognitive and behavioral science. However, a major challenge is identifying observable…
Ontic state recovery through prediction
Ping Zhang, Quang-Huy Nguyen, Yingtie Lei +1
We encounter a system through its responses, yet its state concerns how the system is. Predicting further responses promises knowledge beyond what is immediately observed. What tha…
A Rigorous Behavior Assessment of CNNs Using a Data-Domain Sampling Regime
Shuning Jiang, Wei-Lun Chao, Daniel Haehn +2
We present a data-domain sampling regime for quantifying CNNs' graphic perception behaviors. This regime lets us evaluate CNNs' ratio estimation ability in bar charts from three pe…
Building Machine Learning Challenges for Anomaly Detection in Science
Elizabeth G. Campolongo, Yuan-Tang Chou, Ekaterina Govorkova +148
Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not…
Federated Inverse Probability Treatment Weighting for Individual Treatment Effect Estimation
Changchang Yin, Hong-You Chen, Wei-Lun Chao +1
Individual treatment effect (ITE) estimation is to evaluate the causal effects of treatment strategies on some important outcomes, which is a crucial problem in healthcare. Most ex…
Revisiting semi-supervised learning in the era of foundation models
Ping Zhang, Zheda Mai, Quang-Huy Nguyen +1
Semi-supervised learning (SSL) leverages abundant unlabeled data alongside limited labeled data to enhance learning. As vision foundation models (VFMs) increasingly serve as the ba…
Fine-Tuning is Fine, if Calibrated
Zheda Mai, Arpita Chowdhury, Ping Zhang +8
Fine-tuning is arguably the most straightforward way to tailor a pre-trained model (e.g., a foundation model) to downstream applications, but it also comes with the risk of losing…
Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual Recognition
Zheda Mai, Ping Zhang, Cheng-Hao Tu +3
Parameter-efficient fine-tuning (PEFT) has attracted significant attention due to the growth of pre-trained model sizes and the need to fine-tune (FT) them for superior downstream…
Jigsaw Game: Federated Clustering
Jinxuan Xu, Hong-You Chen, Wei-Lun Chao +1
Federated learning has recently garnered significant attention, especially within the domain of supervised learning. However, despite the abundance of unlabeled data on end-users,…
Holistic Transfer: Towards Non-Disruptive Fine-Tuning with Partial Target Data
Cheng-Hao Tu, Hong-You Chen, Zheda Mai +7
We propose a learning problem involving adapting a pre-trained source model to the target domain for classifying all classes that appeared in the source data, using target data tha…
FLEE-GNN: A Federated Learning System for Edge-Enhanced Graph Neural Network in Analyzing Geospatial Resilience of Multicommodity Food Flows
Yuxiao Qu, Jinmeng Rao, Song Gao +6
Understanding and measuring the resilience of food supply networks is a global imperative to tackle increasing food insecurity. However, the complexity of these networks, with thei…