We present a novel two-stage fully sparse convolutional 3D object detection framework,
named CAGroup3D. Our proposed method first generates some high-quality 3D proposals …

Traditional analyses in non-convex optimization typically rely on the smoothness
assumption, namely requiring the gradients to be Lipschitz. However, recent evidence …

As a fundamental problem in computer vision, 3D object detection is experiencing rapid
growth. To extract the point-wise features from the irregularly and sparsely distributed points …

Adam is widely adopted in practical applications due to its fast convergence. However, its
theoretical analysis is still far from satisfactory. Existing convergence analyses for Adam rely …

Various optimal gradient-based algorithms have been developed for smooth nonconvex
optimization. However, many nonconvex machine learning problems do not belong to the …

By ensuring differential privacy in the learning algorithms, one can rigorously mitigate the
risk of large models memorizing sensitive training data. In this paper, we study two …

In this paper, we aim to optimize a contrastive loss with individualized temperatures in a
principled and systematic manner for self-supervised learning. The common practice of …

This paper investigates group distributionally robust optimization (GDRO), with the purpose
to learn a model that performs well over $ m $ different distributions. First, we formulate …

Image reconstruction enhanced by regularizers, eg, to enforce sparsity, low rank or
smoothness priors on images, has many successful applications in vision tasks such as …

Several recent works have studied the societal effects of AI; these include issues such as
fairness, robustness, and safety. In many of these objectives, a learner seeks to minimize its …