2 citations · 3 across the 3 of their papers we have counts for
4 papers
From Pixels, Without Pre-training: Joint Generative and Self-Supervised Representation Learning in One Model
Vicente Balmaseda, Ching-Long Lin, Tianbao Yang
Strong image generation models are conditioned on class labels, aligned to frozen pretrained encoders, or built on separately trained autoencoders. While effective, generation then…
Discovering Global False Negatives On the Fly for Self-supervised Contrastive Learning
Vicente Balmaseda, Bokun Wang, Ching-Long Lin +1
In self-supervised contrastive learning, negative pairs are typically constructed using an anchor image and a sample drawn from the entire dataset, excluding the anchor. However, t…
Discriminative Finetuning of Generative Large Language Models without Reward Models and Human Preference Data
Siqi Guo, Ilgee Hong, Vicente Balmaseda +6
Supervised fine-tuning (SFT) has become a crucial step for aligning pretrained large language models (LLMs) using supervised datasets of input-output pairs. However, despite being…
Combinatorial Approximations for Cluster Deletion: Simpler, Faster, and Better
Vicente Balmaseda, Ying Xu, Yixin Cao +1
Cluster deletion is an NP-hard graph clustering objective with applications in computational biology and social network analysis, where the goal is to delete a minimum number of ed…