Denoising diffusion models represent a recent emerging topic in computer vision,
demonstrating remarkable results in the area of generative modeling. A diffusion model is a …

Many modern approaches to offline Reinforcement Learning (RL) utilize behavior
regularization, typically augmenting a model-free actor critic algorithm with a penalty …

Representing probability distributions by the gradient of their density functions has proven
effective in modeling a wide range of continuous data modalities. However, this …

The advancement of visual intelligence is intrinsically tethered to the availability of data. In
parallel, generative Artificial Intelligence (AI) has unlocked the potential to create synthetic …

Recently diffusion models have emerged as a new powerful generative method for 3D point
cloud generation tasks. However few works study the effect of the architecture of the …

Based on an asymptotically optimal weighted support vector machine (SVM) that introduces
label shift, a systematic procedure is derived for applying oversampling and weighted SVM …

Energy-based latent variable models (EBLVMs) are more expressive than conventional
energy-based models. However, its potential on visual tasks are limited by its training …

Training energy-based models (EBMs) with noise-contrastive estimation (NCE) is
theoretically feasible but practically challenging. Effective learning requires the noise …

While generative models hold thrilling potential, their limited usability presents substantial
challenges for their widespread adoption in real-world applications. Specifically, existing …

Abstract Recently Deep Neural Network (DNN) models have shown remarkable successes
on several tasks including classification, domain translation etc. However, those methods …