Chang D. Yoo

Learning Imbalanced Datasets With Maximum Margin Loss

2021

A learning algorithm referred to as Maximum Margin (MM) is proposed for considering the class-imbalance data learning issue: the deep model tends to predict the majority classes rather than the minority ones. For better generalization …

A Theory of Unsupervised Speech Recognition

2023 · arXiv (Cornell University)

Unsupervised speech recognition (ASR-U) is the problem of learning automatic speech recognition (ASR) systems from unpaired speech-only and text-only corpora. While various algorithms exist to solve this problem, a theoretical framework is missing from studying …

Unsupervised Speech Recognition with N-Skipgram and Positional Unigram Matching

2023 · arXiv (Cornell University)

Training unsupervised speech recognition systems presents challenges due to GAN-associated instability, misalignment between speech and text, and significant memory demands. To tackle these challenges, we introduce a novel ASR system, ESPUM. This system harnesses the …

Efficient Latent Variable Modeling for Knowledge-Grounded Dialogue Generation

2023

Knowledge-grounded dialogue generation requires first retrieving appropriate external knowledge based on a conversational context and then generating a response grounded on the retrieved knowledge. In general, these two sequential modules, a knowledge retriever and a …

Reward Generation via Large Vision-Language Model in Offline Reinforcement Learning

2025

In offline reinforcement learning (RL), learning from fixed datasets presents a promising solution for domains where real-time interaction with the environment is expensive or risky. However, designing dense reward signals for offline dataset requires significant …