Search Results for author:
Found 26 papers, 8 papers with code
Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond
Developing a universal model that can effectively harness heterogeneous resources and respond to a wide range of personalized needs has been a longstanding community aspiration.
Language Models As Semantic Indexers
Semantic identifier (ID) is an important concept in information retrieval that aims to preserve the semantics of objects such as documents and items inside their IDs.
Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
To test the potential of the dataset, we introduce three tasks in this work: (1) next-product recommendation, (2) next-product recommendation with domain shifts, and (3) next-product title generation.
A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy
Therefore, in this work, we study a novel problem of developing robust and membership privacy-preserving GNNs.
Self-Explainable Graph Neural Networks for Link Prediction
Directly adopting existing post-hoc explainers for explaining link prediction is sub-optimal because: (i) post-hoc explainers usually adopt another strategy or model to explain a target model, which could misinterpret the target model; and (ii) GNN explainers for node classification identify crucial subgraphs around each node for the explanation; while for link prediction, one needs to explain the prediction for each pair of nodes based on graph structure and node attributes.
HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers
Since the teacher model has a significantly larger capacity and stronger representation power than the student model, it is very difficult for the student to produce predictions that match the teacher's over a massive amount of open-domain training data.
Short Text Pre-training with Extended Token Classification for E-commerce Query Understanding
When applying masking to short search queries, most contextual information is lost and the intent of the search queries may be changed.
DiP-GNN: Discriminative Pre-Training of Graph Neural Networks
Specifically, we train a generator to recover identities of the masked edges, and simultaneously, we train a discriminator to distinguish the generated edges from the original graph's edges.
Condensing Graphs via One-Step Gradient Matching
However, existing approaches have their inherent limitations: (1) they are not directly applicable to graphs where the data is discrete; and (2) the condensation process is computationally expensive due to the involved nested optimization.
MUG: Multi-human Graph Network for 3D Mesh Reconstruction from 2D Pose
Our method works like the following: First, to model the multi-human environment, it processes multi-human 2D poses and builds a novel heterogeneous graph, where nodes from different people and within one person are connected to capture inter-human interactions and draw the body geometry (i. e., skeleton and mesh structure).
Ranked #5 on
3D Multi-Person Pose Estimation
on MuPoTS-3D
3D Multi-Person Human Pose Estimation
3D Multi-Person Pose Estimation
Task-Agnostic Graph Explanations
They are also unable to provide explanations in cases where the GNN is trained in a self-supervised manner, and the resulting representations are used in future downstream tasks.
Task-Agnostic Graph Neural Explanations
TAGE enables the explanation of GNN embedding models without downstream tasks and allows efficient explanation of multitask models.
Jointly Attacking Graph Neural Network and its Explanations
Despite the great success, recent studies have shown that GNNs are highly vulnerable to adversarial attacks, where adversaries can mislead the GNNs' prediction by modifying graphs.
Semi-Supervised Graph-to-Graph Translation
For example, we can easily build graphs representing peoples' shared music tastes and those representing co-purchase behavior, but a well paired dataset is much more expensive to obtain.
A Effective Carrier Phase Recovery Method in Tigth Time-Packing Fast than Nyquist Optical Communication System
We propose a new scheme that combines polybinary transformaton and corrected-BPS to compensate noise for PDM-FTN-QPSK when its accelerated factor is 0. 5, which has 3. 3 dB OSNR gain when phase noise is 800 kHz.
Investigating and Mitigating Degree-Related Biases in Graph Convolutional Networks
Pseudo labels increase the chance of connecting to labeled neighbors for low-degree nodes, thus reducing the biases of GCNs from the data perspective.
Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps
In this paper, we study a novel problem of explainable user engagement prediction for social network Apps.
Graph Structure Learning for Robust Graph Neural Networks
A natural idea to defend adversarial attacks is to clean the perturbed graph.
Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values
Thus, jointly modeling local and global temporal dynamics is very promising for MTS forecasting with missing values.
Node Injection Attacks on Graphs via Reinforcement Learning
Real-world graph applications, such as advertisements and product recommendations make profits based on accurately classify the label of the nodes.
Transferring Robustness for Graph Neural Network Against Poisoning Attacks
To optimize PA-GNN for a poisoned graph, we design a meta-optimization algorithm that trains PA-GNN to penalize perturbations using clean graphs and their adversarial counterparts, and transfers such ability to improve the robustness of PA-GNN on the poisoned graph.
Ranked #25 on
Node Classification
on Pubmed
MEGAN: A Generative Adversarial Network for Multi-View Network Embedding
Data from many real-world applications can be naturally represented by multi-view networks where the different views encode different types of relationships (e. g., friendship, shared interests in music, etc.)
Joint Modeling of Dense and Incomplete Trajectories for Citywide Traffic Volume Inference
In this paper, we propose a novel framework for the citywide traffic volume inference using both dense GPS trajectories and incomplete trajectories captured by camera surveillance systems.
Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction
Specifically, our proposed model is designed as a spatial-temporal network with a meta-learning paradigm.
Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction
Although both factors have been considered in modeling, existing works make strong assumptions about spatial dependence and temporal dynamics, i. e., spatial dependence is stationary in time, and temporal dynamics is strictly periodical.
Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction
Traditional demand prediction methods mostly rely on time series forecasting techniques, which fail to model the complex non-linear spatial and temporal relations.
