1 citations · 2 across the 4 of their papers we have counts for
7 papers
Maximum Entropy Inverse Reinforcement Learning of Diffusion Models with Energy-Based Models
Sangwoong Yoon, Himchan Hwang, Dohyun Kwon +2
We present a maximum entropy inverse reinforcement learning (IRL) approach for improving the sample quality of diffusion generative models, especially when the number of generation…
Generalized Contrastive Divergence: Joint Training of Energy-Based Model and Diffusion Model through Inverse Reinforcement Learning
Sangwoong Yoon, Dohyun Kwon, Himchan Hwang +2
We present Generalized Contrastive Divergence (GCD), a novel objective function for training an energy-based model (EBM) and a sampler simultaneously. GCD generalizes Contrastive D…
Variational Weighting for Kernel Density Ratios
Sangwoong Yoon, Frank C. Park, Gunsu S Yun +2
Kernel density estimation (KDE) is integral to a range of generative and discriminative tasks in machine learning. Drawing upon tools from the multidimensional calculus of variatio…
Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach
Sangwoong Yoon, Young-Uk Jin, Yung-Kyun Noh +1
We present a new method of training energy-based models (EBMs) for anomaly detection that leverages low-dimensional structures within data. The proposed algorithm, Manifold Project…
Learning to increase matching efficiency in identifying additional b-jets in the process
Cheongjae Jang, Sang-Kyun Ko, Yung-Kyun Noh +3
The process is an essential channel to reveal the Higgs properties but has an irreducible background from the $\text{t}\bar…
K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning
Jihun Hamm, Yung-Kyun Noh
Minimax optimization plays a key role in adversarial training of machine learning algorithms, such as learning generative models, domain adaptation, privacy preservation, and robus…