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Found 73 papers, 20 papers with code
KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment
Recent legislation of the "right to be forgotten" has led to the interest in machine unlearning, where the learned models are endowed with the function to forget information about specific training instances as if they have never existed in the training set.
Explicit Knowledge Graph Reasoning for Conversational Recommendation
Recent conversational recommender systems (CRSs) tackle those limitations by enabling recommender systems to interact with the user to obtain her/his current preference through a sequence of clarifying questions.
Joint Semantic and Structural Representation Learning for Enhancing User Preference Modelling
Knowledge graphs (KGs) have become important auxiliary information for helping recommender systems obtain a good understanding of user preferences.
Model-Agnostic Decentralized Collaborative Learning for On-Device POI Recommendation
In light of this, We propose a novel on-device POI recommendation framework, namely Model-Agnostic Collaborative learning for on-device POI recommendation (MAC), allowing users to customize their own model structures (e. g., dimension \& number of hidden layers).
DREAM: Adaptive Reinforcement Learning based on Attention Mechanism for Temporal Knowledge Graph Reasoning
Although existing TKG reasoning methods have the ability to predict missing future events, they fail to generate explicit reasoning paths and lack explainability.
Continuous Input Embedding Size Search For Recommender Systems
Latent factor models are the most popular backbones for today's recommender systems owing to their prominent performance.
TinyAD: Memory-efficient anomaly detection for time series data in Industrial IoT
With the prevalent deployment of the Industrial Internet of Things (IIoT), an enormous amount of time series data is collected to facilitate machine learning models for anomaly detection, and it is of the utmost importance to directly deploy the trained models on the IIoT devices.
Semantic-aware Node Synthesis for Imbalanced Heterogeneous Information Networks
To tackle the imbalance of minority classes and supplement their inadequate semantics, we present the first method for the semantic imbalance problem in imbalanced HINs named Semantic-aware Node Synthesis (SNS).
Self-supervised Graph-based Point-of-interest Recommendation
In order to counteract the scarcity and incompleteness of POI check-ins, we propose a novel self-supervised learning paradigm in \ssgrec, where the trajectory representations are contrastively learned from two augmented views on geolocations and temporal transitions.
XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation
Contrastive learning (CL) has recently been demonstrated critical in improving recommendation performance.
Time-aware Dynamic Graph Embedding for Asynchronous Structural Evolution
Meanwhile, we treat each edge sequence as a whole and embed its ToV of the first vertex to further encode the time-sensitive information.
Reinforcement Learning-enhanced Shared-account Cross-domain Sequential Recommendation
Shared-account Cross-domain Sequential Recommendation (SCSR) is an emerging yet challenging task that simultaneously considers the shared-account and cross-domain characteristics in the sequential recommendation.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
Time Interval-enhanced Graph Neural Network for Shared-account Cross-domain Sequential Recommendation
Shared-account Cross-domain Sequential Recommendation (SCSR) task aims to recommend the next item via leveraging the mixed user behaviors in multiple domains.
Spatial-Temporal Meta-path Guided Explainable Crime Prediction
Exposure to crime and violence can harm individuals' quality of life and the economic growth of communities.
Thinking inside The Box: Learning Hypercube Representations for Group Recommendation
Such user and group preferences are commonly represented as points in the vector space (i. e., embeddings), where multiple user embeddings are compressed into one to facilitate ranking for group-item pairs.
Decentralized Collaborative Learning Framework for Next POI Recommendation
On this basis, we propose a novel decentralized collaborative learning framework for POI recommendation (DCLR), which allows users to train their personalized models locally in a collaborative manner.
Self-Supervised Learning for Recommender Systems: A Survey
In this survey, a timely and systematical review of the research efforts on self-supervised recommendation (SSR) is presented.
Unified Question Generation with Continual Lifelong Learning
To solve the problems, we propose a model named Unified-QG based on lifelong learning techniques, which can continually learn QG tasks across different datasets and formats.
DeHIN: A Decentralized Framework for Embedding Large-scale Heterogeneous Information Networks
Such heterogeneous network embedding (HNE) methods effectively harness the heterogeneity of small-scale HINs.
Personalized On-Device E-health Analytics with Decentralized Block Coordinate Descent
Actuated by the growing attention to personal healthcare and the pandemic, the popularity of E-health is proliferating.
Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation
Contrastive learning (CL) recently has spurred a fruitful line of research in the field of recommendation, since its ability to extract self-supervised signals from the raw data is well-aligned with recommender systems' needs for tackling the data sparsity issue.
Towards Robust Neural Image Compression: Adversarial Attack and Model Finetuning
We perform the adversarial attack by injecting a small amount of noise perturbation to original source images, and then encode these adversarial examples using prevailing learnt image compression models.
SurvODE: Extrapolating Gene Expression Distribution for Early Cancer Identification
With the increasingly available large-scale cancer genomics datasets, machine learning approaches have played an important role in revealing novel insights into cancer development.
PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion
Evaluations on two real-world datasets show that 1) our attack model significantly boosts the exposure rate of the target item in a stealthy way, without harming the accuracy of the poisoned recommender; and 2) existing defenses are not effective enough, highlighting the need for new defenses against our local model poisoning attacks to federated recommender systems.
Localization with Sampling-Argmax
In this work, we propose sampling-argmax, a differentiable training method that imposes implicit constraints to the shape of the probability map by minimizing the expectation of the localization error.
Ranked #130 on
3D Human Pose Estimation
on Human3.6M
Secure Your Ride: Real-time Matching Success Rate Prediction for Passenger-Driver Pairs
In recent years, online ride-hailing platforms have become an indispensable part of urban transportation.
Lightweight Self-Attentive Sequential Recommendation
Modern deep neural networks (DNNs) have greatly facilitated the development of sequential recommender systems by achieving state-of-the-art recommendation performance on various sequential recommendation tasks.
Attribute-aware Explainable Complementary Clothing Recommendation
As a result, this creates a severe bottleneck when we are trying to advance the recommendation accuracy and generating fine-grained explanations since the explicit attributes have only loose connections to the actual recommendation process.
Exploiting Positional Information for Session-based Recommendation
According to our analysis, existing positional encoding schemes are generally forward-aware only, which can hardly represent the dynamics of the intention in a session.
Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation
Furthermore, the sparse POI-POI transitions restrict the ability of a model to learn effective sequential patterns for recommendation.
Learning Elastic Embeddings for Customizing On-Device Recommenders
The core idea is to compose elastic embeddings for each item, where an elastic embedding is the concatenation of a set of embedding blocks that are carefully chosen by an automated search function.
Learning to Ask Appropriate Questions in Conversational Recommendation
Hence, the ability to generate suitable clarifying questions is the key to timely tracing users' dynamic preferences and achieving successful recommendations.
DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation
Shared-account Cross-domain Sequential recommendation (SCSR) is the task of recommending the next item based on a sequence of recorded user behaviors, where multiple users share a single account, and their behaviours are available in multiple domains.
Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modelling
As a well-established approach, factorization machine (FM) is capable of automatically learning high-order interactions among features to make predictions without the need for manual feature engineering.
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning
In WIDEN, we propose a novel inductive, meta path-free message passing scheme that packs up heterogeneous node features with their associated edges from both low- and high-order neighbor nodes.
Fast-adapting and Privacy-preserving Federated Recommender System
In the mobile Internet era, the recommender system has become an irreplaceable tool to help users discover useful items, and thus alleviating the information overload problem.
Hierarchical Hyperedge Embedding-based Representation Learning for Group Recommendation
However, the representation learning for a group is most complex beyond the fusion of group member representation, as the personal preferences and group preferences may be in different spaces.
Graph Embedding for Recommendation against Attribute Inference Attacks
Specifically, in GERAI, we bind the information perturbation mechanism in differential privacy with the recommendation capability of graph convolutional networks.
Knowledge Graph Completion with Text-aided Regularization
Knowledge Graph Completion is a task of expanding the knowledge graph/base through estimating possible entities, or proper nouns, that can be connected using a set of predefined relations, or verb/predicates describing interconnections of two things.
A Sublevel Moment-SOS Hierarchy for Polynomial Optimization
We introduce a sublevel Moment-SOS hierarchy where each SDP relaxation can be viewed as an intermediate (or interpolation) between the d-th and (d+1)-th order SDP relaxations of the Moment-SOS hierarchy (dense or sparse version).
Combinatorial Optimization
Optimization and Control
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.
Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graph
Consequently, the spatiotemporal passenger demand records naturally carry dynamic patterns in the constructed graphs, where the edges also encode important information about the directions and volume (i. e., weights) of passenger demands between two connected regions.
Intrinsically Guided Exploration in Meta Reinforcement Learning
Deep reinforcement learning algorithms generally require large amounts of data to solve a single task.
Decoder-side Cross Resolution Synthesis for Video Compression Enhancement
This paper proposes a decoder-side Cross Resolution Synthesis (CRS) module to pursue better compression efficiency beyond the latest Versatile Video Coding (VVC), where we encode intra frames at original high resolution (HR), compress inter frames at a lower resolution (LR), and then super-resolve decoded LR inter frames with the help from preceding HR intra and neighboring LR inter frames.
Recognizing Micro-Expression in Video Clip with Adaptive Key-Frame Mining
As a spontaneous expression of emotion on face, micro-expression reveals the underlying emotion that cannot be controlled by human.
Compiling ONNX Neural Network Models Using MLIR
Deep neural network models are becoming increasingly popular and have been used in various tasks such as computer vision, speech recognition, and natural language processing.
GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation
On one hand, when a new session arrives, a session graph with a global attribute is constructed based on the current session and its associate user.
MetaCURE: Meta Reinforcement Learning with Empowerment-Driven Exploration
Meta reinforcement learning (meta-RL) extracts knowledge from previous tasks and achieves fast adaptation to new tasks.
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.
Optimal multi-wave sampling for regression modelling in two-phase designs
We show that a two-wave sampling with reasonable informative priors will end up with higher precision for the parameter of interest and be close to the underlying optimal design.
Applications
GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection
Therefore, it is of great practical significance to construct a robust recommender system that is able to generate stable recommendations even in the presence of shilling attacks.
Try This Instead: Personalized and Interpretable Substitute Recommendation
Then, by treating attributes as the bridge between users and items, we can thoroughly model the user-item preferences (i. e., personalization) and item-item relationships (i. e., substitution) for recommendation.
Semialgebraic Optimization for Lipschitz Constants of ReLU Networks
The Lipschitz constant of a network plays an important role in many applications of deep learning, such as robustness certification and Wasserstein Generative Adversarial Network.
Learned Video Compression via Joint Spatial-Temporal Correlation Exploration
Traditional video compression technologies have been developed over decades in pursuit of higher coding efficiency.
Sequence-Aware Factorization Machines for Temporal Predictive Analytics
As a widely adopted solution, models based on Factorization Machines (FMs) are capable of modelling high-order interactions among features for effective sparse predictive analytics.
Neural Image Compression via Non-Local Attention Optimization and Improved Context Modeling
This paper proposes a novel Non-Local Attention optmization and Improved Context modeling-based image compression (NLAIC) algorithm, which is built on top of the deep nerual network (DNN)-based variational auto-encoder (VAE) structure.
Learned Point Cloud Geometry Compression
This paper presents a novel end-to-end Learned Point Cloud Geometry Compression (a. k. a., Learned-PCGC) framework, to efficiently compress the point cloud geometry (PCG) using deep neural networks (DNN) based variational autoencoders (VAE).
Recurrent Event Network : Global Structure Inference Over Temporal Knowledge Graph
We present Recurrent Event Network (RE-Net), a novel autoregressive architecture for modeling temporal sequences of multi-relational graphs (e. g., temporal knowledge graph), which can perform sequential, global structure inference over future time stamps to predict new events.
Collaborative Policy Learning for Open Knowledge Graph Reasoning
We propose a novel reinforcement learning framework to train two collaborative agents jointly, i. e., a multi-hop graph reasoner and a fact extractor.
Bidirectional RNN-based Few-shot Training for Detecting Multi-stage Attack
"Feint Attack" is simulated by the real attack inserted in the normal causal attack chain, and the addition of the real attack destroys the causal relationship of the original attack chain.
Non-local Attention Optimized Deep Image Compression
This paper proposes a novel Non-Local Attention Optimized Deep Image Compression (NLAIC) framework, which is built on top of the popular variational auto-encoder (VAE) structure.
Extreme Image Coding via Multiscale Autoencoders With Generative Adversarial Optimization
We propose a MultiScale AutoEncoder(MSAE) based extreme image compression framework to offer visually pleasing reconstruction at a very low bitrate.
A Novel Apex-Time Network for Cross-Dataset Micro-Expression Recognition
As researchers working on such topics are moving to learn from the nature of micro-expression, the practice of using deep learning techniques has evolved from processing the entire video clip of micro-expression to the recognition on apex frame.
Using Structured Input and Modularity for Improved Learning
Our method for utilizing the known structure of input data includes: (1) pre-processing the input data to expose relevant structures, and (2) constructing neural networks by incorporating the structure of the input data as an integral part of the network design.
Gated Context Model with Embedded Priors for Deep Image Compression
Besides, a field study on perceptual quality is also given via a dedicated subjective assessment, to compare the efficiency of our proposed methods and other conventional image compression methods.
Micro-Attention for Micro-Expression recognition
The existence of micro expression in small-local areas on face and limited size of available databases still constrain the recognition accuracy on such emotional facial behavior.
Micro Expression Recognition
Micro-Expression Recognition
+1
Gradient Band-based Adversarial Training for Generalized Attack Immunity of A3C Path Finding
In addition, we propose Gradient Band-based Adversarial Training, which trained with a single randomly choose dominant adversarial example without taking any modification, to realize the "1:N" attack immunity for generalized dominant adversarial examples.
Deep Sequence Learning with Auxiliary Information for Traffic Prediction
Predicting traffic conditions from online route queries is a challenging task as there are many complicated interactions over the roads and crowds involved.
Ranked #1 on
Traffic Prediction
on Q-Traffic
Deep Image Compression via End-to-End Learning
We present a lossy image compression method based on deep convolutional neural networks (CNNs), which outperforms the existing BPG, WebP, JPEG2000 and JPEG as measured via multi-scale structural similarity (MS-SSIM), at the same bit rate.
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.
Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection
The proposed model delves soft-attention into the recurrence to simultaneously pool out distinct features with particular focus and produce hidden representations that capture contextual variations of relevant posts over time.
A single scale retinex based method for palm vein extraction
Palm vein recognition is a novel biometric identification technology.
