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Found 26 papers, 11 papers with code
MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection
Recent advancements in anomaly detection have seen the efficacy of CNN- and transformer-based approaches.
Attention Is Not the Only Choice: Counterfactual Reasoning for Path-Based Explainable Recommendation
Unfortunately, these attention weights are intentionally designed for model accuracy but not explainability.
DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection
Reconstruction-based approaches have achieved remarkable outcomes in anomaly detection.
Defense Against Model Extraction Attacks on Recommender Systems
These gradients are used to compute a swap loss, which maximizes the loss of the student model.
Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning
Currently, graph learning models are indispensable tools to help researchers explore graph-structured data.
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning
In this paper, we propose a novel method to utilize \textbf{C}ounterfactual mechanism to generate artificial hard negative samples for \textbf{G}raph \textbf{C}ontrastive learning, namely \textbf{CGC}, which has a different perspective compared to those sampling-based strategies.
DialMed: A Dataset for Dialogue-based Medication Recommendation
Therefore, we make the first attempt to recommend medications with the conversations between doctors and patients.
Graph Masked Autoencoders with Transformers
In this paper, we propose Graph Masked Autoencoders (GMAEs), a self-supervised transformer-based model for learning graph representations.
Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation
Extensive experiments show that our attack outperforms unsupervised baseline attacks and has comparable performance with supervised attacks in multiple downstream tasks including node classification and link prediction.
Dual Space Graph Contrastive Learning
In addition, we conduct extensive experiments to analyze the impact of different graph encoders on DSGC, giving insights about how to better leverage the advantages of contrastive learning between different spaces.
Towards Unsupervised Deep Graph Structure Learning
To solve the unsupervised GSL problem, we propose a novel StrUcture Bootstrapping contrastive LearnIng fraMEwork (SUBLIME for abbreviation) with the aid of self-supervised contrastive learning.
GenLabel: Mixup Relabeling using Generative Models
Mixup is a data augmentation method that generates new data points by mixing a pair of input data.
Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation
Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information provided by knowledge graphs.
Improving Fairness via Federated Learning
We then theoretically and empirically show that the performance tradeoff of FedAvg-based fair learning algorithms is strictly worse than that of a fair classifier trained on centralized data.
Is Attention Better Than Matrix Decomposition?
As an essential ingredient of modern deep learning, attention mechanism, especially self-attention, plays a vital role in the global correlation discovery.
Ranked #7 on
Semantic Segmentation
on PASCAL VOC 2012 test
Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation
They utilize simple and fixed schemes, like neighborhood information aggregation or mathematical calculation of vectors, to fuse the embeddings of different user behaviors to obtain a unified embedding to represent a user's behavioral patterns which will be used in downstream recommendation tasks.
Where are we in embedding spaces? A Comprehensive Analysis on Network Embedding Approaches for Recommender Systems
Hyperbolic space and hyperbolic embeddings are becoming a popular research field for recommender systems.
Hyperbolic Neural Collaborative Recommender
In the second phase, we develop a deep framework based on hyperbolic geometry to integrate constructed neighbor sets into recommendation.
FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection
Obstructive Sleep Apnea (OSA) is a highly prevalent but inconspicuous disease that seriously jeopardizes the health of human beings.
Temporal Meta-path Guided Explainable Recommendation
This paper utilizes well-designed item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendations.
Social and Information Networks
Interpretable Signed Link Prediction with Signed Infomax Hyperbolic Graph
Most of the prior efforts are devoted to learning node embeddings with graph neural networks (GNNs), which preserve the signed network topology by message-passing along edges to facilitate the downstream link prediction task.
Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction
Moreover, to adapt the proposed method to be capable of handling large-scale social networks, we propose a two-phase space reconciliation mechanism to align the embedding spaces in both network partitioning based parallel training and account matching across different social networks.
A Block-based Generative Model for Attributed Networks Embedding
To preserve the attribute information, we assume that each node has a hidden embedding related to its assigned block.
Hyperbolic Node Embedding for Signed Networks
Signed network embedding methods aim to learn vector representations of nodes in signed networks.
DeepHunter: Hunting Deep Neural Network Defects via Coverage-Guided Fuzzing
In company with the data explosion over the past decade, deep neural network (DNN) based software has experienced unprecedented leap and is becoming the key driving force of many novel industrial applications, including many safety-critical scenarios such as autonomous driving.
When Point Process Meets RNNs: Predicting Fine-Grained User Interests with Mutual Behavioral Infectivity
Inspired by point process in modeling temporal point process, in this paper we present a deep prediction method based on two recurrent neural networks (RNNs) to jointly model each user's continuous browsing history and asynchronous event sequences in the context of inter-user behavioral mutual infectivity.
