research
∙
09/19/2023
Investigating the Catastrophic Forgetting in Multimodal Large Language Models
Following the success of GPT4, there has been a surge in interest in mul...
share
research
∙
09/18/2023
General In-Hand Object Rotation with Vision and Touch
We introduce RotateIt, a system that enables fingertip-based object rota...
research
∙
08/30/2023
Emergence of Segmentation with Minimalistic White-Box Transformers
Transformer-like models for vision tasks have recently proven effective ...
research
∙
08/29/2023
Canonical Factors for Hybrid Neural Fields
Factored feature volumes offer a simple way to build more compact, effic...
research
∙
08/26/2023
A Spatially Non-stationary Fading Channel Model for Simulation and (Semi-) Analytical Study of ELAA-MIMO
In this paper, a novel spatially non-stationary fading channel model is ...
research
∙
08/22/2023
Alternative Normalized-Preconditioning for Scalable Iterative Large-MIMO Detection
Signal detection in large multiple-input multiple-output (large-MIMO) sy...
research
∙
07/25/2023
Scaff-PD: Communication Efficient Fair and Robust Federated Learning
We present Scaff-PD, a fast and communication-efficient algorithm for di...
research
∙
07/18/2023
Pixel-wise Graph Attention Networks for Person Re-identification
Graph convolutional networks (GCN) is widely used to handle irregular da...
research
∙
06/27/2023
Prioritized Trajectory Replay: A Replay Memory for Data-driven Reinforcement Learning
In recent years, data-driven reinforcement learning (RL), also known as ...
research
∙
06/21/2023
Leveraging User-Wise SVD for Accelerated Convergence in Iterative ELAA-MIMO Detections
Numerous low-complexity iterative algorithms have been proposed to offer...
research
∙
06/15/2023
ViP: A Differentially Private Foundation Model for Computer Vision
Artificial intelligence (AI) has seen a tremendous surge in capabilities...
research
∙
06/12/2023
Ensemble-based Offline-to-Online Reinforcement Learning: From Pessimistic Learning to Optimistic Exploration
Offline reinforcement learning (RL) is a learning paradigm where an agen...
research
∙
06/10/2023
HIPODE: Enhancing Offline Reinforcement Learning with High-Quality Synthetic Data from a Policy-Decoupled Approach
Offline reinforcement learning (ORL) has gained attention as a means of ...
research
∙
06/09/2023
In-Sample Policy Iteration for Offline Reinforcement Learning
Offline reinforcement learning (RL) seeks to derive an effective control...
research
∙
06/08/2023
Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models
The advent of large pre-trained models has brought about a paradigm shif...
research
∙
06/01/2023
White-Box Transformers via Sparse Rate Reduction
In this paper, we contend that the objective of representation learning ...
research
∙
05/30/2023
Space MIMO: Direct Unmodified Handheld to Multi-Satellite Communication
This paper examines the uplink transmission of a single-antenna handshel...
research
∙
05/02/2023
Representation Learning via Manifold Flattening and Reconstruction
This work proposes an algorithm for explicitly constructing a pair of ne...
research
∙
04/27/2023
Achieving Maximum-likelihood Detection Performance with Square-order Complexity in Large Quasi-Symmetric MIMO Systems
We focus on the signal detection for large quasi-symmetric (LQS) multipl...
research
∙
04/08/2023
EMP-SSL: Towards Self-Supervised Learning in One Training Epoch
Recently, self-supervised learning (SSL) has achieved tremendous success...
research
∙
03/09/2023
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning
A compelling use case of offline reinforcement learning (RL) is to obtai...
research
∙
02/18/2023
Closed-Loop Transcription via Convolutional Sparse Coding
Autoencoding has achieved great empirical success as a framework for lea...
research
∙
01/25/2023
Robot Subset Selection for Swarm Lifetime Maximization in Computation Offloading with Correlated Data Sources
Consider robot swarm wireless networks where mobile robots offload their...
research
∙
01/04/2023
Unsupervised Manifold Linearizing and Clustering
Clustering data lying close to a union of low-dimensional manifolds, wit...
research
∙
12/24/2022
Understanding the Complexity Gains of Single-Task RL with a Curriculum
Reinforcement learning (RL) problems can be challenging without well-sha...
research
∙
12/01/2022
Disparity-based Stereo Image Compression with Aligned Cross-View Priors
With the wide application of stereo images in various fields, the resear...
research
∙
10/30/2022
Unsupervised Learning of Structured Representations via Closed-Loop Transcription
This paper proposes an unsupervised method for learning a unified repres...
research
∙
10/24/2022
Revisiting Sparse Convolutional Model for Visual Recognition
Despite strong empirical performance for image classification, deep neur...
research
∙
09/30/2022
Minimalistic Unsupervised Learning with the Sparse Manifold Transform
We describe a minimalistic and interpretable method for unsupervised lea...
research
∙
09/14/2022
Age of Information in Federated Learning over Wireless Networks
In this paper, federated learning (FL) over wireless networks is investi...
research
∙
08/05/2022
A Design of Low-Projection SCMA Codebooks for Downlink Satellite Internet of Things
This paper presents a systematic investigation on codebook design of spa...
research
∙
07/13/2022
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels
State-of-the-art federated learning methods can perform far worse than t...
research
∙
07/11/2022
On the Principles of Parsimony and Self-Consistency for the Emergence of Intelligence
Ten years into the revival of deep networks and artificial intelligence,...
research
∙
06/18/2022
Pursuit of a Discriminative Representation for Multiple Subspaces via Sequential Games
We consider the problem of learning discriminative representations for d...
research
∙
04/06/2022
PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations
Deep Reinforcement Learning (DRL) has been a promising solution to many ...
research
∙
03/31/2022
Efficient Maximal Coding Rate Reduction by Variational Forms
The principle of Maximal Coding Rate Reduction (MCR^2) has recently been...
research
∙
03/28/2022
A Novel Multi-Task Learning Empowered Codebook Design for Downlink SCMA Networks
Sparse code multiple access (SCMA) is a promising code-domain non-orthog...
research
∙
02/11/2022
Predicting Out-of-Distribution Error with the Projection Norm
We propose a metric – Projection Norm – to predict a model's performance...
research
∙
02/11/2022
Incremental Learning of Structured Memory via Closed-Loop Transcription
This work proposes a minimal computational model for learning a structur...
research
∙
01/04/2022
DeepFGS: Fine-Grained Scalable Coding for Learned Image Compression
Scalable coding, which can adapt to channel bandwidth variation, perform...
research
∙
11/12/2021
Closed-Loop Data Transcription to an LDR via Minimaxing Rate Reduction
This work proposes a new computational framework for learning an explici...
research
∙
10/03/2021
PL-EESR: Perceptual Loss Based END-TO-END Robust Speaker Representation Extraction
Speech enhancement aims to improve the perceptual quality of the speech ...
research
∙
09/11/2021
Hermite Expansion Model and LMMSE Analysis for Low-Resolution Quantized MIMO Detection
In this paper, the Hermite polynomials are employed to study linear appr...
research
∙
08/21/2021
Correlation-Based Device Energy-Efficient Dynamic Multi-Task Offloading for Mobile Edge Computing
Task offloading to mobile edge computing (MEC) has emerged as a key tech...
research
∙
08/18/2021
A Non-Stationary Channel Model with Correlated NLoS/LoS States for ELAA-mMIMO
In this paper, a novel spatially non-stationary channel model is propose...
research
∙
07/08/2021
Computational Benefits of Intermediate Rewards for Hierarchical Planning
Many hierarchical reinforcement learning (RL) applications have empirica...
research
∙
06/30/2021
On the Convergence of Stochastic Extragradient for Bilinear Games with Restarted Iteration Averaging
We study the stochastic bilinear minimax optimization problem, presentin...
research
∙
06/18/2021
Learning and Meshing from Deep Implicit Surface Networks Using an Efficient Implementation of Analytic Marching
Reconstruction of object or scene surfaces has tremendous applications i...
research
∙
05/21/2021
ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction
This work attempts to provide a plausible theoretical framework that aim...
research
∙
05/04/2021