110 citations · 121 across the 23 of their papers we have counts for
24 papers
Same Concept, Different Directions: Cross-Modal Feature Heterogeneity in Sparse Autoencoders
Chungpa Lee, Jihoon Kwon, Kyle Min +1
Vision-language models map images and text into a joint embedding space. However, these embeddings often entangle multiple semantic features, which limits their interpretability an…
Transformers in the Dark: Navigating Unknown Search Spaces via Bandit Feedback
Jungtaek Kim, Thomas Zeng, Ziqian Lin +5
Effective problem solving with Large Language Models (LLMs) can be enhanced when they are paired with external search algorithms. By viewing the space of diverse ideas and their fo…
Soft Task-Aware Routing of Experts for Equivariant Representation Learning
Jaebyeong Jeon, Hyeonseo Jang, Jy-yong Sohn +1
Equivariant representation learning aims to capture variations induced by input transformations in the representation space, whereas invariant representation learning encodes seman…
Enhancing Compositional Reasoning in CLIP via Reconstruction and Alignment of Text Descriptions
Jihoon Kwon, Kyle Min, Jy-yong Sohn
Despite recent advances, vision-language models trained with standard contrastive objectives still struggle with compositional reasoning -- the ability to understand structured rel…
On the Similarities of Embeddings in Contrastive Learning
Chungpa Lee, Sehee Lim, Kibok Lee +1
Contrastive learning operates on a simple yet effective principle: Embeddings of positive pairs are pulled together, while those of negative pairs are pushed apart. In this paper,…
Measuring Representational Shifts in Continual Learning: A Linear Transformation Perspective
Joonkyu Kim, Yejin Kim, Jy-yong Sohn
In continual learning scenarios, catastrophic forgetting of previously learned tasks is a critical issue, making it essential to effectively measure such forgetting. Recently, ther…