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Found 61 papers, 19 papers with code
How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective
Our comprehensive theoretical and empirical investigations lead to two core insights: 1) Item popularity is memorized in the principal singular vector of the score matrix predicted by the recommendation model; 2) The dimension collapse phenomenon amplifies the impact of principal singular vector on model predictions, intensifying the popularity bias.
SIGformer: Sign-aware Graph Transformer for Recommendation
Integrating both positive and negative feedback to form a signed graph can lead to a more comprehensive understanding of user preferences.
Infrastructure-Assisted Collaborative Perception in Automated Valet Parking: A Safety Perspective
Environmental perception in Automated Valet Parking (AVP) has been a challenging task due to severe occlusions in parking garages.
Distributionally Robust Graph-based Recommendation System
DR-GNN addresses two core challenges: 1) To enable DRO to cater to graph data intertwined with GNN, we reinterpret GNN as a graph smoothing regularizer, thereby facilitating the nuanced application of DRO; 2) Given the typically sparse nature of recommendation data, which might impede robust optimization, we introduce slight perturbations in the training distribution to expand its support.
Rethinking Propagation for Unsupervised Graph Domain Adaptation
Motivated by our empirical analysis, we reevaluate the role of GNNs in graph domain adaptation and uncover the pivotal role of the propagation process in GNNs for adapting to different graph domains.
A Survey on Graph Condensation
Analytics on large-scale graphs have posed significant challenges to computational efficiency and resource requirements.
Macro Graph Neural Networks for Online Billion-Scale Recommender Systems
Predicting Click-Through Rate (CTR) in billion-scale recommender systems poses a long-standing challenge for Graph Neural Networks (GNNs) due to the overwhelming computational complexity involved in aggregating billions of neighbors.
Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks
Hierarchical federated learning (HFL) enables distributed training of models across multiple devices with the help of several edge servers and a cloud edge server in a privacy-preserving manner.
Exploiting Symmetric Temporally Sparse BPTT for Efficient RNN Training
Implementing online training of RNNs on the edge calls for optimized algorithms for an efficient deployment on hardware.
Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection
To address these issues, we utilize the advantages of reinforcement learning in adaptively learning in complex environments and propose a novel method that incorporates Reinforcement neighborhood selection for unsupervised graph ANomaly Detection (RAND).
Exploring Sparse Spatial Relation in Graph Inference for Text-Based VQA
The associations between these repetitive objects are superfluous for answer reasoning; (2) two spatially distant OCR tokens detected in the image frequently have weak semantic dependencies for answer reasoning; and (3) the co-existence of nearby objects and tokens may be indicative of important visual cues for predicting answers.
Modeling Spatiotemporal Periodicity and Collaborative Signal for Local-Life Service Recommendation
(2) spatiotemporal collaborative signal, which indicates similar users have similar preferences at specific locations and times.
CDR: Conservative Doubly Robust Learning for Debiased Recommendation
In recommendation systems (RS), user behavior data is observational rather than experimental, resulting in widespread bias in the data.
Homophily-enhanced Structure Learning for Graph Clustering
Graph clustering is a fundamental task in graph analysis, and recent advances in utilizing graph neural networks (GNNs) have shown impressive results.
Multi-View Fusion and Distillation for Subgrade Distresses Detection based on 3D-GPR
To address these challenges, we introduce a novel methodology for the subgrade distress detection task by leveraging the multi-view information from 3D-GPR data.
QMNet: Importance-Aware Message Exchange for Decentralized Multi-Agent Reinforcement Learning
To improve the performance of multi-agent reinforcement learning under the constraint of wireless resources, we propose a message importance metric and design an importance-aware scheduling policy to effectively exchange messages.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks
Dynamic graph data mining has gained popularity in recent years due to the rich information contained in dynamic graphs and their widespread use in the real world.
Data-Heterogeneous Hierarchical Federated Learning with Mobility
Federated learning enables distributed training of machine learning (ML) models across multiple devices in a privacy-preserving manner.
OpenGSL: A Comprehensive Benchmark for Graph Structure Learning
Moreover, we observe that the learned graph structure demonstrates a strong generalization ability across different GNN models, despite the high computational and space consumption.
MASS: Mobility-Aware Sensor Scheduling of Cooperative Perception for Connected Automated Driving
Specifically, we design a mobility-aware sensor scheduling (MASS) algorithm based on the restless multi-armed bandit (RMAB) theory to maximize the expected average perception gain.
Why is the prediction wrong? Towards underfitting case explanation via meta-classification
In this paper we present a heuristic method to provide individual explanations for those elements in a dataset (data points) which are wrongly predicted by a given classifier.
Adap-$τ$: Adaptively Modulating Embedding Magnitude for Recommendation
Recent years have witnessed the great successes of embedding-based methods in recommender systems.
SMDP-Based Dynamic Batching for Efficient Inference on GPU-Based Platforms
In particular, parallel computing resources on the platforms, such as graphics processing units (GPUs), have higher computational and energy efficiency with larger batch sizes.
Robust Sequence Networked Submodular Maximization
The approximation ratio of the algorithm depends both on the number of the removed elements and the network topology.
MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehicles
Federated learning (FL) is a promising approach to enable the future Internet of vehicles consisting of intelligent connected vehicles (ICVs) with powerful sensing, computing and communication capabilities.
Unbiased Knowledge Distillation for Recommendation
Traditional solutions first train a full teacher model from the training data, and then transfer its knowledge (\ie \textit{soft labels}) to supervise the learning of a compact student model.
Hilbert Distillation for Cross-Dimensionality Networks
Since the distilled 2D networks are supervised by the curves converted from dimensionally heterogeneous 3D features, the 2D networks are given an informative view in terms of learning structural information embedded in well-trained high-dimensional representations.
MEET: Mobility-Enhanced Edge inTelligence for Smart and Green 6G Networks
Accordingly, base stations (BSs) and edge servers (ESs) need to be densely deployed, leading to huge deployment and operation costs, in particular the energy costs.
Dynamic Data-Free Knowledge Distillation by Easy-to-Hard Learning Strategy
Besides, CuDFKD adapts the generation target dynamically according to the status of student model.
DOLPHINS: Dataset for Collaborative Perception enabled Harmonious and Interconnected Self-driving
In this work, we release DOLPHINS: Dataset for cOllaborative Perception enabled Harmonious and INterconnected Self-driving, as a new simulated large-scale various-scenario multi-view multi-modality autonomous driving dataset, which provides a ground-breaking benchmark platform for interconnected autonomous driving.
A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions
Motivated by the tremendous success of deep learning in clustering, one of the most fundamental machine learning tasks, and the large number of recent advances in this direction, in this paper we conduct a comprehensive survey on deep clustering by proposing a new taxonomy of different state-of-the-art approaches.
Multi-user Co-inference with Batch Processing Capable Edge Server
To deal with the coupled offloading and scheduling introduced by concurrent batch processing, we first consider an offline problem with a constant edge inference latency and the same latency constraint.
RMGN: A Regional Mask Guided Network for Parser-free Virtual Try-on
Virtual try-on(VTON) aims at fitting target clothes to reference person images, which is widely adopted in e-commerce. Existing VTON approaches can be narrowly categorized into Parser-Based(PB) and Parser-Free(PF) by whether relying on the parser information to mask the persons' clothes and synthesize try-on images.
Time-Correlated Sparsification for Efficient Over-the-Air Model Aggregation in Wireless Federated Learning
In this work, we propose time-correlated sparsification with hybrid aggregation (TCS-H) for communication-efficient FEEL, which exploits jointly the power of model compression and over-the-air computation.
Online V2X Scheduling for Raw-Level Cooperative Perception
Cooperative perception of connected vehicles comes to the rescue when the field of view restricts stand-alone intelligence.
Online Adversarial Distillation for Graph Neural Networks
However, its effect on graph neural networks is less than satisfactory since the graph topology and node attributes are likely to change in a dynamic way and in this case a static teacher model is insufficient in guiding student training.
Coded Computation across Shared Heterogeneous Workers with Communication Delay
In this work, we consider a multi-master heterogeneous-worker distributed computing scenario, where multiple matrix multiplication tasks are encoded and allocated to workers for parallel computation.
Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation
To sufficiently exploit such important information for recommendation, it is essential to disentangle the benign popularity bias caused by item quality from the harmful popularity bias caused by conformity.
Efficient Medical Image Segmentation Based on Knowledge Distillation
To deal with this problem, we propose an efficient architecture by distilling knowledge from well-trained medical image segmentation networks to train another lightweight network.
Distilling Holistic Knowledge with Graph Neural Networks
The holistic knowledge is represented as a unified graph-based embedding by aggregating individual knowledge from relational neighborhood samples with graph neural networks, the student network is learned by distilling the holistic knowledge in a contrastive manner.
Uncertainty-Guided Mixup for Semi-Supervised Domain Adaptation without Source Data
Present domain adaptation methods usually perform explicit representation alignment by simultaneously accessing the source data and target data.
Semi-Supervised Hypothesis Transfer for Source-Free Domain Adaptation
Domain Adaptation has been widely used to deal with the distribution shift in vision, language, multimedia etc.
Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraints
In this work, we consider an over-the-air FEEL system with analog gradient aggregation, and propose an energy-aware dynamic device scheduling algorithm to optimize the training performance under energy constraints of devices, where both communication energy for gradient aggregation and computation energy for local training are included.
SamWalker++: recommendation with informative sampling strategy
However, the social network information may not be available in many recommender systems, which hinders application of SamWalker.
CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation
To deal with these problems, we propose an efficient and effective collaborative sampling method CoSam, which consists of: (1) a collaborative sampler model that explicitly leverages user-item interaction information in sampling probability and exhibits good properties of normalization, adaption, interaction information awareness, and sampling efficiency; and (2) an integrated sampler-recommender framework, leveraging the sampler model in prediction to offset the bias caused by uneven sampling.
Cluster-Based Cooperative Digital Over-the-Air Aggregation for Wireless Federated Edge Learning
To mitigate wireless fading, we further propose a cluster-based system and design the relay selection scheme based on the normalized detection SNR.
Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning
Then, a greedy device scheduling algorithm is introduced, which in each step selects the device consuming the least updating time obtained by the optimal bandwidth allocation, until the lower bound begins to increase, meaning that scheduling more devices will degrade the model accuracy.
Dynamic Compression Ratio Selection for Edge Inference Systems with Hard Deadlines
To reduce the communication cost, lossy data compression can be exploited for inference tasks, but may bring more erroneous inference results.
Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification
The shared sub-structures between training classes and test classes are essential in few-shot graph classification.
Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback
A popular and effective approach for implicit recommendation is to treat unobserved data as negative but downweight their confidence.
How Does BN Increase Collapsed Neural Network Filters?
This phenomenon is caused by the normalization effect of BN, which induces a non-trainable region in the parameter space and reduces the network capacity as a result.
Device Scheduling with Fast Convergence for Wireless Federated Learning
In each iteration of FL (called round), the edge devices update local models based on their own data and contribute to the global training by uploading the model updates via wireless channels.
Energy-Aware Analog Aggregation for Federated Learning with Redundant Data
In this work, we consider analog aggregation to scale down the communication cost with respect to the number of workers, and introduce data redundancy to the system to deal with non-i. i. d.
Improving Device-Edge Cooperative Inference of Deep Learning via 2-Step Pruning
Since the output data size of a DNN layer can be larger than that of the raw data, offloading intermediate data between layers can suffer from high transmission latency under limited wireless bandwidth.
Distributed Policy Learning Based Random Access for Diversified QoS Requirements
Future wireless access networks need to support diversified quality of service (QoS) metrics required by various types of Internet-of-Things (IoT) devices, e. g., age of information (AoI) for status generating sources and ultra low latency for safety information in vehicular networks.
HAHE: Hierarchical Attentive Heterogeneous Information Network Embedding
Second, given a meta path, nodes in HIN are connected by path instances while existing works fail to fully explore the differences between path instances that reflect nodes' preferences in the semantic space.
A Two-Step Learning and Interpolation Method for Location-Based Channel Database
Timely and accurate knowledge of channel state information (CSI) is necessary to support scheduling operations at both physical and network layers.
Time-Sequence Channel Inference for Beam Alignment in Vehicular Networks
In this paper, we propose a learning-based low-overhead beam alignment method for vehicle-to-infrastructure communication in vehicular networks.
Inferring Remote Channel State Information: Cramér-Rao Lower Bound and Deep Learning Implementation
Channel state information (CSI) is of vital importance in wireless communication systems.
Exploiting Wireless Channel State Information Structures Beyond Linear Correlations: A Deep Learning Approach
Knowledge of information about the propagation channel in which a wireless system operates enables better, more efficient approaches for signal transmissions.
A Block Regression Model for Short-Term Mobile Traffic Forecasting
Based on these characteristics, we propose a \emph{Block Regression} ({BR}) model for mobile traffic forecasting.
