research
∙
08/22/2023
Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic Segmentation
Impressive performance on point cloud semantic segmentation has been ach...
share
research
∙
06/23/2023
On a Class of Gibbs Sampling over Networks
We consider the sampling problem from a composite distribution whose pot...
research
∙
05/29/2023
From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework
Textual adversarial attacks can discover models' weaknesses by adding se...
research
∙
05/26/2023
COMCAT: Towards Efficient Compression and Customization of Attention-Based Vision Models
Attention-based vision models, such as Vision Transformer (ViT) and its ...
research
∙
05/09/2023
Guided Focal Stack Refinement Network for Light Field Salient Object Detection
Light field salient object detection (SOD) is an emerging research direc...
research
∙
05/09/2023
DynamicKD: An Effective Knowledge Distillation via Dynamic Entropy Correction-Based Distillation for Gap Optimizing
The knowledge distillation uses a high-performance teacher network to gu...
research
∙
05/02/2023
Efficient Federated Learning with Enhanced Privacy via Lottery Ticket Pruning in Edge Computing
Federated learning (FL) is a collaborative learning paradigm for decentr...
research
∙
05/01/2023
Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy
To defend the inference attacks and mitigate the sensitive information l...
research
∙
02/20/2023
Improved dimension dependence of a proximal algorithm for sampling
We propose a sampling algorithm that achieves superior complexity bounds...
research
∙
02/08/2023
Improving the Model Consistency of Decentralized Federated Learning
To mitigate the privacy leakages and communication burdens of Federated ...
research
∙
01/28/2023
SaFormer: A Conditional Sequence Modeling Approach to Offline Safe Reinforcement Learning
Offline safe RL is of great practical relevance for deploying agents in ...
research
∙
01/20/2023
HALOC: Hardware-Aware Automatic Low-Rank Compression for Compact Neural Networks
Low-rank compression is an important model compression strategy for obta...
research
∙
01/13/2023
GOHSP: A Unified Framework of Graph and Optimization-based Heterogeneous Structured Pruning for Vision Transformer
The recently proposed Vision transformers (ViTs) have shown very impress...
research
∙
01/04/2023
GUAP: Graph Universal Attack Through Adversarial Patching
Graph neural networks (GNNs) are a class of effective deep learning mode...
research
∙
12/12/2022
Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks
Safety comes first in many real-world applications involving autonomous ...
research
∙
12/05/2022
Algorithm and Hardware Co-Design of Energy-Efficient LSTM Networks for Video Recognition with Hierarchical Tucker Tensor Decomposition
Long short-term memory (LSTM) is a type of powerful deep neural network ...
research
∙
11/07/2022
TDC: Towards Extremely Efficient CNNs on GPUs via Hardware-Aware Tucker Decomposition
Tucker decomposition is one of the SOTA CNN model compression techniques...
research
∙
10/28/2022
RoChBert: Towards Robust BERT Fine-tuning for Chinese
Despite of the superb performance on a wide range of tasks, pre-trained ...
research
∙
10/21/2022
Text Editing as Imitation Game
Text editing, such as grammatical error correction, arises naturally fro...
research
∙
10/21/2022
Syntax-guided Localized Self-attention by Constituency Syntactic Distance
Recent works have revealed that Transformers are implicitly learning the...
research
∙
10/10/2022
A Comprehensive Survey of Data Augmentation in Visual Reinforcement Learning
Visual reinforcement learning (RL), which makes decisions directly from ...
research
∙
09/01/2022
Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization
A key challenge of continual reinforcement learning (CRL) in dynamic env...
research
∙
08/24/2022
Robot Motion Planning as Video Prediction: A Spatio-Temporal Neural Network-based Motion Planner
Neural network (NN)-based methods have emerged as an attractive approach...
research
∙
08/16/2022
The least-used key selection method for information retrieval in large-scale Cloud-based service repositories
As the number of devices connected to the Internet of Things (IoT) incre...
research
∙
08/15/2022
Nesterov smoothing for sampling without smoothness
We study the problem of sampling from a target distribution in ℝ^d whose...
research
∙
06/17/2022
SafeRL-Kit: Evaluating Efficient Reinforcement Learning Methods for Safe Autonomous Driving
Safe reinforcement learning (RL) has achieved significant success on ris...
research
∙
05/24/2022
Penalized Proximal Policy Optimization for Safe Reinforcement Learning
Safe reinforcement learning aims to learn the optimal policy while satis...
research
∙
04/05/2022
Birds of A Feather Flock Together: Category-Divergence Guidance for Domain Adaptive Segmentation
Unsupervised domain adaptation (UDA) aims to enhance the generalization ...
research
∙
02/21/2022
Don't Touch What Matters: Task-Aware Lipschitz Data Augmentation for Visual Reinforcement Learning
One of the key challenges in visual Reinforcement Learning (RL) is to le...
research
∙
01/24/2022
BTPK-based learning: An Interpretable Method for Named Entity Recognition
Named entity recognition (NER) is an essential task in natural language ...
research
∙
01/03/2022
Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanism
The dynamic job-shop scheduling problem (DJSP) is a class of scheduling ...
research
∙
01/01/2022
A Surrogate-Assisted Controller for Expensive Evolutionary Reinforcement Learning
The integration of Reinforcement Learning (RL) and Evolutionary Algorith...
research
∙
09/06/2021
Method for making multi-attribute decisions in wargames by combining intuitionistic fuzzy numbers with reinforcement learning
Researchers are increasingly focusing on intelligent games as a hot rese...
research
∙
09/01/2021
Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge Distillation
The powerful learning ability of deep neural networks enables reinforcem...
research
∙
07/26/2021
Towards Efficient Tensor Decomposition-Based DNN Model Compression with Optimization Framework
Advanced tensor decomposition, such as Tensor train (TT) and Tensor ring...
research
∙
06/19/2021
Boosting Offline Reinforcement Learning with Residual Generative Modeling
Offline reinforcement learning (RL) tries to learn the near-optimal poli...
research
∙
06/08/2021
Optimization of Service Addition in Multilevel Index Model for Edge Computing
With the development of Edge Computing and Artificial Intelligence (AI) ...
research
∙
05/01/2021
Multi-view Clustering with Deep Matrix Factorization and Global Graph Refinement
Multi-view clustering is an important yet challenging task in machine le...
research
∙
04/13/2021
Inference of cell dynamics on perturbation data using adjoint sensitivity
Data-driven dynamic models of cell biology can be used to predict cell r...
research
∙
03/28/2021
Noise Injection-based Regularization for Point Cloud Processing
Noise injection-based regularization, such as Dropout, has been widely u...
research
∙
02/08/2021
Doubly Residual Neural Decoder: Towards Low-Complexity High-Performance Channel Decoding
Recently deep neural networks have been successfully applied in channel ...
research
∙
01/08/2021
NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection
In recent studies, Lots of work has been done to solve time series anoma...
research
∙
12/30/2020
How does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches?
Unsupervised domain adaptation (UDA) aims to train a target classifier w...
research
∙
11/25/2020
Towards Playing Full MOBA Games with Deep Reinforcement Learning
MOBA games, e.g., Honor of Kings, League of Legends, and Dota 2, pose gr...
research
∙
11/25/2020
Supervised Learning Achieves Human-Level Performance in MOBA Games: A Case Study of Honor of Kings
We present JueWu-SL, the first supervised-learning-based artificial inte...
research
∙
08/04/2020
Learning from a Complementary-label Source Domain: Theory and Algorithms
In unsupervised domain adaptation (UDA), a classifier for the target dom...
research
∙
07/29/2020
Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation
In unsupervised domain adaptation (UDA), classifiers for the target doma...
research
∙
06/27/2020
Local Causal Structure Learning and its Discovery Between Type 2 Diabetes and Bone Mineral Density
Type 2 diabetes (T2DM), one of the most prevalent chronic diseases, affe...
research
∙
06/23/2020
Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation
In the unsupervised open set domain adaptation (UOSDA), the target domai...
research
∙
04/26/2020
