Search Results for author:
Found 39 papers, 19 papers with code
PMG : Personalized Multimodal Generation with Large Language Models
Such user preferences are then fed into a generator, such as a multimodal LLM or diffusion model, to produce personalized content.
Understanding the Ranking Loss for Recommendation with Sparse User Feedback
In this paper, we uncover a new challenge associated with BCE loss in scenarios with sparse positive feedback, such as CTR prediction: the gradient vanishing for negative samples.
LocalStyleFool: Regional Video Style Transfer Attack Using Segment Anything Model
Benefiting from the popularity and scalably usability of Segment Anything Model (SAM), we first extract different regions according to semantic information and then track them through the video stream to maintain the temporal consistency.
CLAP: Learning Transferable Binary Code Representations with Natural Language Supervision
At the core, our approach boosts superior transfer learning capabilities by effectively aligning binary code with their semantics explanations (in natural language), resulting a model able to generate better embeddings for binary code.
One Train for Two Tasks: An Encrypted Traffic Classification Framework Using Supervised Contrastive Learning
In particular, we utilize supervised contrastive learning to enhance the packet-level and flow-level representations and perform graph data augmentation on the byte-level traffic graph so that the fine-grained semantic-invariant characteristics between bytes can be captured through contrastive learning.
LogoStyleFool: Vitiating Video Recognition Systems via Logo Style Transfer
We separate the attack into three stages: style reference selection, reinforcement-learning-based logo style transfer, and perturbation optimization.
ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance
To tackle this challenge and enhance the robustness of deep learning models against label noise in graph-based tasks, we propose a method called ERASE (Error-Resilient representation learning on graphs for lAbel noiSe tolerancE).
Beyond Two-Tower Matching: Learning Sparse Retrievable Cross-Interactions for Recommendation
Two-tower models are a prevalent matching framework for recommendation, which have been widely deployed in industrial applications.
STEM: Unleashing the Power of Embeddings for Multi-task Recommendation
Surprisingly, negative transfer still occurs in existing MTL methods on samples that receive comparable feedback across tasks.
TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic Classification
Encrypted traffic classification is receiving widespread attention from researchers and industrial companies.
Time-aware Graph Structure Learning via Sequence Prediction on Temporal Graphs
To address these issues, we propose a Time-aware Graph Structure Learning (TGSL) approach via sequence prediction on temporal graphs, which learns better graph structures for downstream tasks through adding potential temporal edges.
A Simple Hypergraph Kernel Convolution based on Discounted Markov Diffusion Process
This paper proposes a two-phase paradigm to aggregate comprehensive information on discrete structures leading to a Discount Markov Diffusion Learnable Kernel (DMDLK).
GraphTTA: Test Time Adaptation on Graph Neural Networks
In this paper, we present a novel test time adaptation strategy named Graph Adversarial Pseudo Group Contrast (GAPGC), for graph neural networks TTA, to better adapt to the Out Of Distribution (OOD) test data.
Cascade Luminance and Chrominance for Image Retouching: More Like Artist
Photo retouching aims to adjust the luminance, contrast, and saturation of the image to make it more human aesthetically desirable.
BARS: Towards Open Benchmarking for Recommender Systems
Despite significant progress made in both research and practice of recommender systems, to date, there is a lack of a widely-recognized benchmarking standard in this field.
Preventing Over-Smoothing for Hypergraph Neural Networks
In recent years, hypergraph learning has attracted great attention due to its capacity in representing complex and high-order relationships.
Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphs
As a powerful tool for modeling complex relationships, hypergraphs are gaining popularity from the graph learning community.
StyleFool: Fooling Video Classification Systems via Style Transfer
In this paper, we focus on unrestricted perturbations and propose StyleFool, a black-box video adversarial attack via style transfer to fool the video classification system.
PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation
Specifically, PEAR not only captures feature-level and item-level interactions, but also models item contexts from both the initial ranking list and the historical clicked item list.
Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal Transport
In this paper, we present a novel optimal transport-based fine-tuning framework called GTOT-Tuning, namely, Graph Topology induced Optimal Transport fine-Tuning, for GNN style backbones.
Ranked #1 on
Graph Classification
on BBBP
Fully-integrated multipurpose microwave frequency identification system on a single chip
We demonstrate a fully-integrated multipurpose microwave frequency identification system on silicon-on-insulator platform.
Neural Architecture Searching for Facial Attributes-based Depression Recognition
In this paper, we propose to extend Neural Architecture Search (NAS) technique for designing an optimal model for multiple facial attributes-based depression recognition, which can be efficiently and robustly implemented in a small dataset.
UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation
In this paper, we take one step further to propose an ultra-simplified formulation of GCNs (dubbed UltraGCN), which skips infinite layers of message passing for efficient recommendation.
Ranked #3 on
Recommendation Systems
on Gowalla
Weakly Supervised Graph Clustering
Graph Clustering, which clusters the nodes of a graph given its collection of node features and edge connections in an unsupervised manner, has long been researched in graph learning and is essential in certain applications.
SimpleX: A Simple and Strong Baseline for Collaborative Filtering
While many existing studies focus on the design of more powerful interaction encoders, the impacts of loss functions and negative sampling ratios have not yet been well explored.
Ranked #3 on
Recommendation Systems
on Yelp2018
Robust Model-based Reinforcement Learning for Autonomous Greenhouse Control
In this paper, we present a model-based robust RL framework for autonomous greenhouse control to meet the sample efficiency and safety challenges.
MBDP: A Model-based Approach to Achieve both Robustness and Sample Efficiency via Double Dropout Planning
MBDP consists of two kinds of dropout mechanisms, where the rollout-dropout aims to improve the robustness with a small cost of sample efficiency, while the model-dropout is designed to compensate for the lost efficiency at a slight expense of robustness.
Learnable Hypergraph Laplacian for Hypergraph Learning
Hypergraph Convolutional Neural Networks (HGCNNs) have demonstrated their potential in modeling high-order relations preserved in graph-structured data.
Learnable Hypergraph Laplacian for Hypergraph Learning
HyperGraph Convolutional Neural Networks (HGCNNs) have demonstrated their potential in modeling high-order relations preserved in graph structured data.
Diversified Multiscale Graph Learning with Graph Self-Correction
Though the multiscale graph learning techniques have enabled advanced feature extraction frameworks, the classic ensemble strategy may show inferior performance while encountering the high homogeneity of the learnt representation, which is caused by the nature of existing graph pooling methods.
Towards a category-extended object detector with limited data
Object detectors are typically learned on fully-annotated training data with fixed predefined categories.
Adversarial Sparse Transformer for Time Series Forecasting
Specifically, AST adopts a Sparse Transformer as the generator to learn a sparse attention map for time series forecasting, and uses a discriminator to improve the prediction performance from sequence level.
Multivariate Time Series Forecasting
Probabilistic Time Series Forecasting
+1
On Self-Distilling Graph Neural Network
Furthermore, the inefficient training process of teacher-student knowledge distillation also impedes its applications in GNN models.
Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach
In this work, we propose to formulate item tagging as a link prediction problem between item nodes and tag nodes.
Inverse Graph Identification: Can We Identify Node Labels Given Graph Labels?
Upon a formal discussion of the variants of IGI, we choose a particular case study of node clustering by making use of the graph labels and node features, with an assistance of a hierarchical graph that further characterizes the connections between different graphs.
Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks
Meanwhile, detecting rumors from such massive information in social media is becoming an arduous challenge.
Maintaining Discrimination and Fairness in Class Incremental Learning
In this paper, we demonstrate it can indeed help the model to output more discriminative results within old classes.
Ranked #2 on
Incremental Learning
on ImageNet100 - 10 steps
(# M Params metric)
Molecular Graph Enhanced Transformer for Retrosynthesis Prediction
With massive possible synthetic routes in chemistry, retrosynthesis prediction is still a challenge for researchers.
Ranked #16 on
Single-step retrosynthesis
on USPTO-50k
Self-Paced Probabilistic Principal Component Analysis for Data with Outliers
There is a probabilistic version of PCA, known as Probabilistic PCA (PPCA).
