Search Results for author:
Found 34 papers, 6 papers with code
Graph and Sequential Neural Networks in Session-based Recommendation: A Survey
Recent years have witnessed the remarkable success of recommendation systems (RSs) in alleviating the information overload problem.
A Temporally Disentangled Contrastive Diffusion Model for Spatiotemporal Imputation
Spatiotemporal data analysis is pivotal across various domains, such as transportation, meteorology, and healthcare.
Attention-aware Social Graph Transformer Networks for Stochastic Trajectory Prediction
We propose Attention-aware Social Graph Transformer Networks for multi-modal trajectory prediction.
GATGPT: A Pre-trained Large Language Model with Graph Attention Network for Spatiotemporal Imputation
The objective of spatiotemporal imputation is to estimate these missing values by understanding the inherent spatial and temporal relationships in the observed multivariate time series.
Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems
Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods.
An Entropy-guided Reinforced Partial Convolutional Network for Zero-Shot Learning
Zero-Shot Learning (ZSL) aims to transfer learned knowledge from observed classes to unseen classes via semantic correlations.
Locality-Sensitive Experience Replay for Online Recommendation
Existing studies encourage the agent to learn from past experience via experience replay (ER).
A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions
In light of the emergence of deep reinforcement learning (DRL) in recommender systems research and several fruitful results in recent years, this survey aims to provide a timely and comprehensive overview of the recent trends of deep reinforcement learning in recommender systems.
Global Convolutional Neural Processes
Latent neural process, a member of NPF, is believed to be capable of modelling the uncertainty on certain points (local uncertainty) as well as the general function priors (global uncertainties).
Multi-Center Federated Learning: Clients Clustering for Better Personalization
By comparison, a mixture of multiple global models could capture the heterogeneity across various clients if assigning the client to different global models (i. e., centers) in FL.
Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference
Recent advances in reinforcement learning have inspired increasing interest in learning user modeling adaptively through dynamic interactions, e. g., in reinforcement learning based recommender systems.
Task Aligned Generative Meta-learning for Zero-shot Learning
Zero-shot learning (ZSL) refers to the problem of learning to classify instances from the novel classes (unseen) that are absent in the training set (seen).
Meta Gradient Boosting Neural Networks
A key challenge for meta-optimization based approaches is to determine whether an initialization condition can be generalized to tasks with diverse distributions to accelerate learning.
Generative Inverse Deep Reinforcement Learning for Online Recommendation
Deep reinforcement learning uses a reward function to learn user's interest and to control the learning process.
Recommender Systems for the Internet of Things: A Survey
Recommendation represents a vital stage in developing and promoting the benefits of the Internet of Things (IoT).
Spectrum-Guided Adversarial Disparity Learning
It has been a significant challenge to portray intraclass disparity precisely in the area of activity recognition, as it requires a robust representation of the correlation between subject-specific variation for each activity class.
Face to Purchase: Predicting Consumer Choices with Structured Facial and Behavioral Traits Embedding
We design a semi-supervised model based on a hierarchical embedding network to extract high-level features of consumers and to predict the top-$N$ purchase destinations of a consumer.
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
We further propose an Adaptive Graph Convolutional Recurrent Network (AGCRN) to capture fine-grained spatial and temporal correlations in traffic series automatically based on the two modules and recurrent networks.
Ranked #2 on
Weather Forecasting
on SD
Explainable Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media
Model interpretability has become important to engenders appropriate user trust by providing the insight into the model prediction.
Causality Learning: A New Perspective for Interpretable Machine Learning
Recent years have witnessed the rapid growth of machine learning in a wide range of fields such as image recognition, text classification, credit scoring prediction, recommendation system, etc.
BIG-bench Machine Learning
Interpretable Machine Learning
+2
NP-PROV: Neural Processes with Position-Relevant-Only Variances
Neural Processes (NPs) families encode distributions over functions to a latent representation, given context data, and decode posterior mean and variance at unknown locations.
Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems
Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods.
Agglomerative Neural Networks for Multi-view Clustering
Conventional multi-view clustering methods seek for a view consensus through minimizing the pairwise discrepancy between the consensus and subviews.
Multi-Center Federated Learning: Clients Clustering for Better Personalization
However, due to the diverse nature of user behaviors, assigning users' gradients to different global models (i. e., centers) can better capture the heterogeneity of data distributions across users.
Are You A Risk Taker? Adversarial Learning of Asymmetric Cross-Domain Alignment for Risk Tolerance Prediction
Most current studies on survey analysis and risk tolerance modelling lack professional knowledge and domain-specific models.
Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation
Interactive recommendation aims to learn from dynamic interactions between items and users to achieve responsiveness and accuracy.
Survey for Trust-aware Recommender Systems: A Deep Learning Perspective
A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation results.
STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting
Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services.
A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers
Brain-Computer Interface (BCI) bridges the human's neural world and the outer physical world by decoding individuals' brain signals into commands recognizable by computer devices.
GrCAN: Gradient Boost Convolutional Autoencoder with Neural Decision Forest
We develop a gradient boost module and embed it into the proposed convolutional autoencoder with neural decision forest to improve the performance.
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).
Opinion Fraud Detection via Neural Autoencoder Decision Forest
Online reviews play an important role in influencing buyers' daily purchase decisions.
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).
Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis
In this paper, we attempt to solve the above challenges by proposing an approach which has better EEG interpretation ability via raw Electroencephalography (EEG) signal analysis for multi-person and multi-class brain activity recognition.
