2 citations · 3 across the 4 of their papers we have counts for
5 papers
One Figure, Every Canvas: Editable Flowchart Relayout via Agentic Pipeline
Shih-Chen Tseng, Chih-Hsuan Chen, Ryan Yang +3
Pipeline figures in ML papers must be repurposed across many canvases, including paper columns, 16:9 slides, portrait posters, 1:1 social teasers, 9:16 phone previews. Each format…
Learning to Look: Cognitive Attention Alignment with Vision-Language Models
Ryan L. Yang, Dipkamal Bhusal, Nidhi Rastogi
Convolutional Neural Networks (CNNs) frequently "cheat" by exploiting superficial correlations, raising concerns about whether they make predictions for the right reasons. Inspired…
Aggregation in the Mirror Space (AIMS): Fast, Accurate Distributed Machine Learning in Military Settings
Ryan Yang, Haizhou Du, Andre Wibisono +1
Distributed machine learning (DML) can be an important capability for modern military to take advantage of data and devices distributed at multiple vantage points to adapt and lear…
PG3: Policy-Guided Planning for Generalized Policy Generation
Ryan Yang, Tom Silver, Aidan Curtis +2
A longstanding objective in classical planning is to synthesize policies that generalize across multiple problems from the same domain. In this work, we study generalized policy se…
Achieving Efficient Distributed Machine Learning Using a Novel Non-Linear Class of Aggregation Functions
Haizhou Du, Ryan Yang, Yijian Chen +3
Distributed machine learning (DML) over time-varying networks can be an enabler for emerging decentralized ML applications such as autonomous driving and drone fleeting. However, t…