research
∙
09/02/2023
Online Targetless Radar-Camera Extrinsic Calibration Based on the Common Features of Radar and Camera
Sensor fusion is essential for autonomous driving and autonomous robots,...
share
research
∙
07/28/2023
3D Radar and Camera Co-Calibration: A Flexible and Accurate Method for Target-based Extrinsic Calibration
Advances in autonomous driving are inseparable from sensor fusion. Heter...
research
∙
06/19/2023
To Fold or Not to Fold: Graph Regularized Tensor Train for Visual Data Completion
Tensor train (TT) representation has achieved tremendous success in visu...
research
∙
04/03/2023
Can the Inference Logic of Large Language Models be Disentangled into Symbolic Concepts?
In this paper, we explain the inference logic of large language models (...
research
∙
01/31/2023
GDOD: Effective Gradient Descent using Orthogonal Decomposition for Multi-Task Learning
Multi-task learning (MTL) aims at solving multiple related tasks simulta...
research
∙
12/15/2022
Output-Dependent Gaussian Process State-Space Model
Gaussian process state-space model (GPSSM) is a fully probabilistic stat...
research
∙
06/12/2022
ChordMixer: A Scalable Neural Attention Model for Sequences with Different Lengths
Sequential data naturally have different lengths in many domains, with s...
research
∙
05/28/2022
Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling
Sparse modeling for signal processing and machine learning has been at t...
research
∙
04/22/2022
Paramixer: Parameterizing Mixing Links in Sparse Factors Works Better than Dot-Product Self-Attention
Self-Attention is a widely used building block in neural modeling to mix...
research
∙
03/18/2022
Bayesian Low-rank Matrix Completion with Dual-graph Embedding: Prior Analysis and Tuning-free Inference
Recently, there is a revival of interest in low-rank matrix completion-b...
research
∙
02/23/2022
Towards Tailored Models on Private AIoT Devices: Federated Direct Neural Architecture Search
Neural networks often encounter various stringent resource constraints w...
research
∙
01/06/2022
Classification of Long Sequential Data using Circular Dilated Convolutional Neural Networks
Classification of long sequential data is an important Machine Learning ...
research
∙
12/29/2021
Training Time Minimization for Federated Edge Learning with Optimized Gradient Quantization and Bandwidth Allocation
Training a machine learning model with federated edge learning (FEEL) is...
research
∙
11/19/2021
RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation
Cross-domain recommendation can help alleviate the data sparsity issue i...
research
∙
09/16/2021
Sparse Factorization of Large Square Matrices
Square matrices appear in many machine learning problems and models. Opt...
research
∙
08/25/2021
A Bayesian Tensor Approach to Enable RIS for 6G Massive Unsourced Random Access
This paper investigates the problem of joint massive devices separation ...
research
∙
11/06/2020
FDNAS: Improving Data Privacy and Model Diversity in AutoML
To prevent the leakage of private information while enabling automated m...
research
∙
09/07/2020
Edge Learning with Unmanned Ground Vehicle: Joint Path, Energy and Sample Size Planning
Edge learning (EL), which uses edge computing as a platform to execute m...
research
∙
09/05/2020
Towards Probabilistic Tensor Canonical Polyadic Decomposition 2.0: Automatic Tensor Rank Learning Using Generalized Hyperbolic Prior
Tensor rank learning for canonical polyadic decomposition (CPD) has long...
research
∙
04/21/2020
A Covariance-based User Activity Detection and Channel Estimation Approach with Novel Pilot Design
This paper studies the massive machine-type communications (mMTC) for th...
research
∙
02/07/2019
Massive MIMO Multicast Beamforming Via Accelerated Random Coordinate Descent
One key feature of massive multiple-input multiple-output systems is the...
research
∙
01/20/2019
