Search Results for author:
Found 14 papers, 4 papers with code
FASIONAD : FAst and Slow FusION Thinking Systems for Human-Like Autonomous Driving with Adaptive Feedback
FASIONAD achieves state-of-the-art performance on this benchmark, establishing a new standard for frameworks integrating fast and slow cognitive processes in autonomous driving.
From Laws to Motivation: Guiding Exploration through Law-Based Reasoning and Rewards
Large Language Models (LLMs) and Reinforcement Learning (RL) are two powerful approaches for building autonomous agents.
Equivariant score-based generative models provably learn distributions with symmetries efficiently
Empirical studies have demonstrated that incorporating symmetries into generative models can provide better generalization and sampling efficiency when the underlying data distribution has group symmetry.
OmniRe: Omni Urban Scene Reconstruction
To that end, we propose a comprehensive 3DGS framework for driving scenes, named OmniRe, that allows for accurate, full-length reconstruction of diverse dynamic objects in a driving log.
Robust Generative Learning with Lipschitz-Regularized $α$-Divergences Allows Minimal Assumptions on Target Distributions
We also provide the first sample complexity bounds for empirical estimations of these divergences on unbounded domains.
360-Degree Panorama Generation from Few Unregistered NFoV Images
360$^\circ$ panoramas are extensively utilized as environmental light sources in computer graphics.
Statistical Guarantees of Group-Invariant GANs
Group-invariant generative adversarial networks (GANs) are a type of GANs in which the generators and discriminators are hardwired with group symmetries.
Mosaic Representation Learning for Self-supervised Visual Pre-training
To address this problem, we propose a mosaic representation learning framework (MosRep), consisting of a new data augmentation strategy that enriches the backgrounds of each small crop and improves the quality of visual representations.
Sample Complexity of Probability Divergences under Group Symmetry
For the maximum mean discrepancy (MMD), the improvement of sample complexity is more nuanced, as it depends on not only the group size but also the choice of kernel.
SpecNet2: Orthogonalization-free spectral embedding by neural networks
The current paper introduces a new neural network approach, named SpecNet2, to compute spectral embedding which optimizes an equivalent objective of the eigen-problem and removes the orthogonalization layer in SpecNet1.
Representation-Agnostic Shape Fields
In this study, we present Representation-Agnostic Shape Fields (RASF), a generalizable and computation-efficient shape embedding module for 3D deep learning.
Significant Inverse Magnetocaloric Effect induced by Quantum Criticality
Strong fluctuations in the low-$T$ quantum critical regime can give rise to a large thermal entropy change and thus significant cooling effect when approaching the QCP.
Strongly Correlated Electrons
FHEDN: A based on context modeling Feature Hierarchy Encoder-Decoder Network for face detection
The proposed network is consist of multiple context modeling and prediction modules, which are in order to detect small, blur, occluded and diverse pose faces.
Hybrid Dirac Semimetal in CaAgBi Materials Family
Based on their formation mechanisms, Dirac points in three-dimensional systems can be classified as accidental or essential.
Materials Science
