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Found 50 papers, 10 papers with code
GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks
We build GDSG as a multi-task diffusion model utilizing a Graph Neural Network (GNN) to acquire the distribution of high-quality solutions.
A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System
Time series anomaly detection (TSAD) has been a research hotspot in both academia and industry in recent years.
AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices
However, the \textit{dynamic} and \textit{diverse} deployment contexts of mobile devices pose significant challenges.
Towards Robust Cross-Domain Recommendation with Joint Identifiability of User Preference
To achieve this, we introduce a hierarchical user preference modeling framework that organizes user representations by the neural network encoder's depth, allowing separate treatment of shallow and deeper subspaces.
Diffusion Models as Network Optimizers: Explorations and Analysis
Next, we provide a concise theoretical proof and intuitive demonstration of the advantages of generative models over discriminative models in network optimization.
AdaFlow: Opportunistic Inference on Asynchronous Mobile Data with Generalized Affinity Control
In response, we present a shift to \textit{opportunistic} inference for asynchronous distributed multi-modal data, enabling inference as soon as partial data arrives.
UbiHR: Resource-efficient Long-range Heart Rate Sensing on Ubiquitous Devices
Ubiquitous on-device heart rate sensing is vital for high-stress individuals and chronic patients.
AdaShadow: Responsive Test-time Model Adaptation in Non-stationary Mobile Environments
AdaShadow addresses these challenges with a backpropagation-free assessor to rapidly identify critical layers, a unit-based runtime predictor to account for resource dynamics in latency estimation, and an online scheduler for prompt layer update planning.
DiffSG: A Generative Solver for Network Optimization with Diffusion Model
In summary, we demonstrate the potential of generative diffusion models in tackling complex network optimization problems and outline a promising path for their broader application in the communication community.
CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community
Knowledge transfer is an effective method to enhance learning performance by avoiding the exorbitant costs associated with data recollection and model retraining.
SimAD: A Simple Dissimilarity-based Approach for Time Series Anomaly Detection
Despite the prevalence of reconstruction-based deep learning methods, time series anomaly detection remains challenging.
Transformer-based RGB-T Tracking with Channel and Spatial Feature Fusion
The former does not fully exploit the potential of using only RGB and TIR information of the template or search region for channel and spatial feature fusion, and the latter lacks direct interaction between the template and search area, which limits the model's ability to fully exploit the original semantic information of both modalities.
Ranked #8 on
Rgb-T Tracking
on RGBT210
Deep Learning Inference on Heterogeneous Mobile Processors: Potentials and Pitfalls
There is a growing demand to deploy computation-intensive deep learning (DL) models on resource-constrained mobile devices for real-time intelligent applications.
Joint Identifiability of Cross-Domain Recommendation via Hierarchical Subspace Disentanglement
Such a failure may overlook the conditionality between two domains and how it contributes to latent factor disentanglement, leading to negative transfer when domains are weakly correlated.
Computation Offloading for Multi-server Multi-access Edge Vehicular Networks: A DDQN-based Method
In this paper, we investigate a multi-user offloading problem in the overlapping domain of a multi-server mobile edge computing system.
The Future of Cognitive Strategy-enhanced Persuasive Dialogue Agents: New Perspectives and Trends
To depict the research trends of CogAgent, in this paper, we first present several fundamental cognitive psychology theories and give the formalized definition of three typical cognitive strategies, including the persuasion strategy, the topic path planning strategy, and the argument structure prediction strategy.
PatchAD: A Lightweight Patch-based MLP-Mixer for Time Series Anomaly Detection
Anomaly detection in time series analysis is a pivotal task, yet it poses the challenge of discerning normal and abnormal patterns in label-deficient scenarios.
Learning Decentralized Traffic Signal Controllers with Multi-Agent Graph Reinforcement Learning
Nevertheless, existing MARL algorithms ignore effective information aggregation which is fundamental for improving the learning capacity of decentralized agents.
RaftFed: A Lightweight Federated Learning Framework for Vehicular Crowd Intelligence
Although federated learning (FL) is a promising tool to solve the problem, adapting conventional FL frameworks to VCI is nontrivial.
Enabling Resource-efficient AIoT System with Cross-level Optimization: A survey
Accordingly, the accuracy and responsiveness of DL models are bounded by resource availability.
Distributional Domain-Invariant Preference Matching for Cross-Domain Recommendation
Next, we aim to build distributional implicit matchings between the domain-level preferences of two domains.
A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading
To improve the MEC performance, it is required to design an optimal offloading strategy that includes offloading decision (i. e., whether offloading or not) and computational resource allocation of MEC.
Collaborative Route Planning of UAVs, Workers and Cars for Crowdsensing in Disaster Response
In this paper, we explicitly address the route planning for a group of agents, including UAVs, workers, and cars, with the goal of maximizing the task completion rate.
Limits of Predictability in Top-N Recommendation
This work investigates such predictability by studying the degree of regularity from a specific set of user behavior data.
Blemish-Aware and Progressive Face Retouching With Limited Paired Data
It is worth noting that we reduce the dependence of BPFRe on paired training samples by imposing effective regularization on unpaired ones.
Text-Guided Unsupervised Latent Transformation for Multi-Attribute Image Manipulation
To address this limitation, we propose a Text-guided Unsupervised StyleGAN Latent Transformation (TUSLT) model, which adaptively infers a single transformation step in the latent space of StyleGAN to simultaneously manipulate multiple attributes on a given input image.
AdaEnlight: Energy-aware Low-light Video Stream Enhancement on Mobile Devices
We report extensive experiments on diverse datasets, scenarios, and platforms and demonstrate the superiority of AdaEnlight compared with state-of-the-art low-light image and video enhancement solutions.
IDNP: Interest Dynamics Modeling using Generative Neural Processes for Sequential Recommendation
In this study, we address these concerns by learning (1) multi-scale representations of short-term interests; and (2) dynamics-aware representations of long-term interests.
Enabling Harmonious Human-Machine Interaction with Visual-Context Augmented Dialogue System: A Review
The intelligent dialogue system, aiming at communicating with humans harmoniously with natural language, is brilliant for promoting the advancement of human-machine interaction in the era of artificial intelligence.
AskMe: Joint Individual-level and Community-level Behavior Interaction for Question Recommendation
On the one hand, we model the rich correlations between the users' diverse behaviors (e. g., answer, follow, vote) to obtain the individual-level behavior interaction.
DeepExpress: Heterogeneous and Coupled Sequence Modeling for Express Delivery Prediction
To address these issues, we propose DeepExpress - a deep-learning based express delivery sequence prediction model, which extends the classic seq2seq framework to learning complex coupling between sequence and features.
TL-SDD: A Transfer Learning-Based Method for Surface Defect Detection with Few Samples
To solve the imbalanced distribution problem, in this paper we propose TL-SDD: a novel Transfer Learning-based method for Surface Defect Detection.
Mask-Embedded Discriminator With Region-Based Semantic Regularization for Semi-Supervised Class-Conditional Image Synthesis
Semi-supervised generative learning (SSGL) makes use of unlabeled data to achieve a trade-off between the data collection/annotation effort and generation performance, when adequate labeled data are not available.
AdaSpring: Context-adaptive and Runtime-evolutionary Deep Model Compression for Mobile Applications
There are many deep learning (e. g., DNN) powered mobile and wearable applications today continuously and unobtrusively sensing the ambient surroundings to enhance all aspects of human lives.
Object Tracking by Least Spatiotemporal Searches
This paper proposes a strategy named IHMs (Intermediate Searching at Heuristic Moments): each step we figure out which moment is the best to search according to a heuristic indicator, then at that moment search locations one by one in descending order of predicted appearing probabilities, until a search hits; iterate this step until we get the object's current location.
Towards information-rich, logical text generation with knowledge-enhanced neural models
Text generation system has made massive promising progress contributed by deep learning techniques and has been widely applied in our life.
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
In this study, we present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition.
The Future of Misinformation Detection: New Perspectives and Trends
We first give a brief review of the literature history of MID, based on which we present several new research challenges and techniques of it, including early detection, detection by multimodal data fusion, and explanatory detection.
Conditional Text Generation for Harmonious Human-Machine Interaction
In recent years, with the development of deep learning, text generation technology has undergone great changes and provided many kinds of services for human beings, such as restaurant reservation and daily communication.
From Crowdsourcing to Crowdmining: Using Implicit Human Intelligence for Better Understanding of Crowdsourced Data
A generic model for CrowdMining is further proposed based on a set of existing studies.
Learning Cross-Domain Representation with Multi-Graph Neural Network
In fact, user's behaviors from different domains regarding the same items are usually relevant.
Multi-agent Attentional Activity Recognition
And the multiple agents in the proposed model represent activities with collective motions across body parts by independently selecting modalities associated with single motions.
AI-Powered Text Generation for Harmonious Human-Machine Interaction: Current State and Future Directions
In the last two decades, the landscape of text generation has undergone tremendous changes and is being reshaped by the success of deep learning.
Inner-Imaging Networks: Put Lenses into Convolutional Structure
To deal with these problems, a novel Inner-Imaging architecture is proposed in this paper, which allows relationships between channels to meet the above requirement.
Metabolize Neural Network
It is self-evident that the significance of metabolize neuronal network(MetaNet) in model construction.
Competitive Inner-Imaging Squeeze and Excitation for Residual Network
In this work, we propose a competitive squeeze-excitation (SE) mechanism for the residual network.
Interpretable Parallel Recurrent Neural Networks with Convolutional Attentions for Multi-Modality Activity Modeling
Multimodal features play a key role in wearable sensor-based human activity recognition (HAR).
Automatic construction of Chinese herbal prescription from tongue image via CNNs and auxiliary latent therapy topics
In order to adapt to the tongue image in a variety of photographic environments and construct herbal prescriptions, a neural network framework for prescription construction is designed.
CityTransfer: Transferring Inter- and Intra-City Knowledge for Chain Store Site Recommendation based on Multi-Source Urban Data
To solve the cold-start problem, we propose CityTransfer, which transfers chain store knowledge from semantically-relevant domains (e. g., other cities with rich knowledge, similar chain enterprises in the target city) for chain store placement recommendation in a new city.
Fullie and Wiselie: A Dual-Stream Recurrent Convolutional Attention Model for Activity Recognition
Multimodal features play a key role in wearable sensor based Human Activity Recognition (HAR).
