Search Results for author:
Found 49 papers, 8 papers with code
Memory-Efficient and Secure DNN Inference on TrustZone-enabled Consumer IoT Devices
We design a memory-efficient management method to support memory-demanding inference in TEEs.
PDiT: Interleaving Perception and Decision-making Transformers for Deep Reinforcement Learning
Designing better deep networks and better reinforcement learning (RL) algorithms are both important for deep RL.
Inference of Dependency Knowledge Graph for Electronic Health Records
The effective analysis of high-dimensional Electronic Health Record (EHR) data, with substantial potential for healthcare research, presents notable methodological challenges.
Adaptive parameter sharing for multi-agent reinforcement learning
Parameter sharing, as an important technique in multi-agent systems, can effectively solve the scalability issue in large-scale agent problems.
Mastering Complex Coordination through Attention-based Dynamic Graph
The coordination between agents in multi-agent systems has become a popular topic in many fields.
Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach
The remarkable progress in Large Language Models (LLMs) opens up new avenues for addressing planning and decision-making problems in Multi-Agent Systems (MAS).
IMPUS: Image Morphing with Perceptually-Uniform Sampling Using Diffusion Models
We present a diffusion-based image morphing approach with perceptually-uniform sampling (IMPUS) that produces smooth, direct and realistic interpolations given an image pair.
Towards Generalized Multi-stage Clustering: Multi-view Self-distillation
MVC aims at exploring common semantics and pseudo-labels from multiple views and clustering in a self-supervised manner.
Hierarchical Mutual Information Analysis: Towards Multi-view Clustering in The Wild
Multi-view clustering (MVC) can explore common semantics from unsupervised views generated by different sources, and thus has been extensively used in applications of practical computer vision.
Benign Overfitting and Grokking in ReLU Networks for XOR Cluster Data
Second, they can undergo a period of classical, harmful overfitting -- achieving a perfect fit to training data with near-random performance on test data -- before transitioning ("grokking") to near-optimal generalization later in training.
Self-supervised Multi-view Clustering in Computer Vision: A Survey
However, as contrastive learning continues to evolve within the field of computer vision, self-supervised learning has also made substantial research progress and is progressively becoming dominant in MVC methods.
TPTU: Large Language Model-based AI Agents for Task Planning and Tool Usage
With recent advancements in natural language processing, Large Language Models (LLMs) have emerged as powerful tools for various real-world applications.
Probabilistic and Semantic Descriptions of Image Manifolds and Their Applications
This paper begins with a description of methods for estimating image probability density functions that reflects the observation that such data is usually constrained to lie in restricted regions of the high-dimensional image space-not every pattern of pixels is an image.
PMaF: Deep Declarative Layers for Principal Matrix Features
We explore two differentiable deep declarative layers, namely least squares on sphere (LESS) and implicit eigen decomposition (IED), for learning the principal matrix features (PMaF).
Towards Understanding Gradient Approximation in Equality Constrained Deep Declarative Networks
We explore conditions for when the gradient of a deep declarative node can be approximated by ignoring constraint terms and still result in a descent direction for the global loss function.
Stackelberg Decision Transformer for Asynchronous Action Coordination in Multi-Agent Systems
Our research contributes to the development of an effective and adaptable asynchronous action coordination method that can be widely applied to various task types and environmental configurations in MAS.
From Explicit Communication to Tacit Cooperation:A Novel Paradigm for Cooperative MARL
Centralized training with decentralized execution (CTDE) is a widely-used learning paradigm that has achieved significant success in complex tasks.
SEA: A Spatially Explicit Architecture for Multi-Agent Reinforcement Learning
In addition, our structure can be applied to various existing mainstream reinforcement learning algorithms with minor modifications and can deal with the problem with a variable number of agents.
Inducing Stackelberg Equilibrium through Spatio-Temporal Sequential Decision-Making in Multi-Agent Reinforcement Learning
In multi-agent reinforcement learning (MARL), self-interested agents attempt to establish equilibrium and achieve coordination depending on game structure.
Style Miner: Find Significant and Stable Explanatory Factors in Time Series with Constrained Reinforcement Learning
In high-dimensional time-series analysis, it is essential to have a set of key factors (namely, the style factors) that explain the change of the observed variable.
xASTNN: Improved Code Representations for Industrial Practice
The proposed xASTNN has three advantages.
Dual Self-Awareness Value Decomposition Framework without Individual Global Max for Cooperative Multi-Agent Reinforcement Learning
Value decomposition methods have gained popularity in the field of cooperative multi-agent reinforcement learning.
Adversarial Purification with the Manifold Hypothesis
In this work, we formulate a novel framework for adversarial robustness using the manifold hypothesis.
Clustering-Induced Generative Incomplete Image-Text Clustering (CIGIT-C)
2) For missing data, the representations generated by existing methods are rarely guaranteed to suit clustering tasks.
Self-supervised Image Clustering from Multiple Incomplete Views via Constrastive Complementary Generation
Incomplete Multi-View Clustering aims to enhance clustering performance by using data from multiple modalities.
Enhanced Decentralized Federated Learning based on Consensus in Connected Vehicles
Federated learning (FL) is emerging as a new paradigm to train machine learning (ML) models in distributed systems, including vehicles in V2X networks.
Consensus Learning for Cooperative Multi-Agent Reinforcement Learning
Almost all multi-agent reinforcement learning algorithms without communication follow the principle of centralized training with decentralized execution.
Mingling Foresight with Imagination: Model-Based Cooperative Multi-Agent Reinforcement Learning
Recently, model-based agents have achieved better performance than model-free ones using the same computational budget and training time in single-agent environments.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Efficient Policy Generation in Multi-Agent Systems via Hypergraph Neural Network
The application of deep reinforcement learning in multi-agent systems introduces extra challenges.
Exploiting Problem Structure in Deep Declarative Networks: Two Case Studies
Deep declarative networks and other recent related works have shown how to differentiate the solution map of a (continuous) parametrized optimization problem, opening up the possibility of embedding mathematical optimization problems into end-to-end learnable models.
MDS-Net: A Multi-scale Depth Stratification Based Monocular 3D Object Detection Algorithm
Monocular 3D object detection is very challenging in autonomous driving due to the lack of depth information.
A Discrete-event-based Simulator for Distributed Deep Learning
New intelligence applications are driving increasing interest in deploying deep neural networks (DNN) in a distributed way.
HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism
Recently, some challenging tasks in multi-agent systems have been solved by some hierarchical reinforcement learning methods.
Hierarchical Reinforcement Learning
Multi-agent Reinforcement Learning
+4
MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning
In the real world, many tasks require multiple agents to cooperate with each other under the condition of local observations.
Distributional Reinforcement Learning
reinforcement-learning
+3
SIDE: State Inference for Partially Observable Cooperative Multi-Agent Reinforcement Learning
As one of the solutions to the decentralized partially observable Markov decision process (Dec-POMDP) problems, the value decomposition method has achieved significant results recently.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs
In this work, we introduce a Resource Aware Neuron Pruning (RANP) algorithm that prunes 3D CNNs at initialization to high sparsity levels.
Newtonalized Orthogonal Matching Pursuit for Linear Frequency Modulated Pulse Frequency Agile Radar
The linear frequency modulated (LFM) frequency agile radar (FAR) can synthesize a wide signal bandwidth through coherent processing while keeping the bandwidth of each pulse narrow.
Refining Semantic Segmentation with Superpixel by Transparent Initialization and Sparse Encoder
We achieve it with fully-connected layers with Transparent Initialization (TI) and efficient logit consistency using a sparse encoder.
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs
Specifically, the core idea is to obtain an importance score for each neuron based on their sensitivity to the loss function.
Grid-less Variational Direction of Arrival Estimation in Heteroscedastic Noise Environment
This paper studies DOA in heteroscedastic noise (HN) environment, where the variance of noise is varied across the snapshots and the antennas.
Fast and Differentiable Message Passing on Pairwise Markov Random Fields
In addition to differentiability, the two main aspects that enable learning these model parameters are the forward and backward propagation time of the MRF optimization algorithm and its inference capabilities.
Semi-Supervised Self-Growing Generative Adversarial Networks for Image Recognition
There are two main problems in label inference: how to measure the confidence of the unlabeled data and how to generalize the classifier.
Gridless Multisnapshot Variational Line Spectral Estimation from Coarsely Quantized Samples
Due to the increasing demand for low power and higher sampling rates, low resolution quantization for data acquisition has drawn great attention recently.
One-bit LFMCW Radar: Spectrum Analysis and Target Detection
It is shown that compared to the conventional radar applying linear processing methods, one-bit LFMCW radar has about $1. 3$ dB performance gain when the input signal-to-noise ratios (SNRs) of targets are low.
Vector Approximate Message Passing Algorithm for Structured Perturbed Sensing Matrix
In this paper, we consider a general form of noisy compressive sensing (CS) where the sensing matrix is not precisely known.
Signal Processing
Peeking the Impact of Points of Interests on Didi
Recently, the online car-hailing service, Didi, has emerged as a leader in the sharing economy.
Variational Bayesian Line Spectral Estimation with Multiple Measurement Vectors
In this paper, the line spectral estimation (LSE) problem with multiple measurement vectors (MMVs) is studied utilizing the Bayesian methods.
Information Theory Information Theory
Towards Accurate Deceptive Opinion Spam Detection based on Word Order-preserving CNN
In order to effectively identify deceptive opinions, we need to comprehensively study the characteristics of deceptive opinions, and explore novel characteristics besides the textual semantics and emotional polarity that have been widely used in text analysis.
Founding Digital Currency on Imprecise Commodity
Current digital currency schemes provide instantaneous exchange on precise commodity, in which "precise" means a buyer can possibly verify the function of the commodity without error.
