5 citations · 5 across the 4 of their papers we have counts for
6 papers
SiamJEPA: On the Role of Siamese Student Encoders in JEPA
Makoto Yamada
Recently, Joint Embedding Predictive Architectures (JEPAs) have attracted significant attention in the computer vision and machine learning communities as a promising framework for…
Task Vector Bases: A Unified and Scalable Framework for Compressed Task Arithmetic
Siqi Zeng, Yifei He, Meitong Liu +5
Task arithmetic, representing downstream tasks through linear operations on task vectors, has emerged as a simple yet powerful paradigm for transferring knowledge across diverse se…
Brain-Inspired Stochastic Joint Embedding Representation Learning
Makoto Yamada, Kian Ming A. Chai, Ayoub Rhim +3
Representation learning is one of the key research topics in machine learning, and the framework of self-supervised learning (SSL) has revolutionized computer vision. However, thes…
On Verbalized Confidence Scores for LLMs
Daniel Yang, Yao-Hung Hubert Tsai, Makoto Yamada
The rise of large language models (LLMs) and their tight integration into our daily life make it essential to dedicate efforts towards their trustworthiness. Uncertainty quantifica…
TIPS Over Tricks: Simple Prompts for Effective Zero-shot Anomaly Detection
Alireza Salehi, Ehsan Karami, Sepehr Noey +4
Anomaly detection identifies departures from expected behavior in safety-critical settings. When target-domain normal data are unavailable, zero-shot anomaly detection (ZSAD) lever…
Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly Detection
Alireza Salehi, Mohammadreza Salehi, Reshad Hosseini +3
Zero-shot anomaly detection and localization aims to learn from source-domain data and generalize to unseen target domains without target-domain samples. Recent CLIP-based methods…