Search Results for author:
Found 76 papers, 42 papers with code
Biologically Plausible Brain Graph Transformer
State-of-the-art brain graph analysis methods fail to fully encode the small-world architecture of brain graphs (accompanied by the presence of hubs and functional modules), and therefore lack biological plausibility to some extent.
Federated Foundation Models on Heterogeneous Time Series
Training a general-purpose time series foundation models with robust generalization capabilities across diverse applications from scratch is still an open challenge.
Mind the Gap Between Prototypes and Images in Cross-domain Finetuning
However, in this paper, we find that there naturally exists a gap, which resembles the modality gap, between the prototype and image instance embeddings extracted from the frozen pre-trained backbone, and simply applying the same transformation during the adaptation phase constrains exploring the optimal representations and shrinks the gap between prototype and image representations.
Personalized Item Representations in Federated Multimodal Recommendation
Federated recommendation systems are essential for providing personalized recommendations while protecting user privacy.
Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond
Large language models (LLMs) have not only revolutionized the field of natural language processing (NLP) but also have the potential to bring a paradigm shift in many other fields due to their remarkable abilities of language understanding, as well as impressive generalization capabilities and reasoning skills.
WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents
The resulting world model is composed of the LLM and the learned rules.
Influence-oriented Personalized Federated Learning
Traditional federated learning (FL) methods often rely on fixed weighting for parameter aggregation, neglecting the mutual influence by others.
Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach
Existing FedCF methods typically combine distributed Collaborative Filtering (CF) algorithms with privacy-preserving mechanisms, and then preserve personalized information into a user embedding vector.
Graph Stochastic Neural Process for Inductive Few-shot Knowledge Graph Completion
Due to the long-tailed distribution of relations and the incompleteness of KGs, there is growing interest in few-shot knowledge graph completion (FKGC).
ARC: A Generalist Graph Anomaly Detector with In-Context Learning
Graph anomaly detection (GAD), which aims to identify abnormal nodes that differ from the majority within a graph, has garnered significant attention.
Multi-Level Additive Modeling for Structured Non-IID Federated Learning
For every mid-level, it learns multiple models each assigned to a subgroup of clients, as clustered FL.
Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Models
This paper demonstrates that pre-trained language models (PLMs) are strong foundation models for on-device meteorological variables modeling.
Navigating the Future of Federated Recommendation Systems with Foundation Models
In this study, we conduct a comprehensive review of FRSs with FMs.
Algorithmic Fairness: A Tolerance Perspective
Recent advancements in machine learning and deep learning have brought algorithmic fairness into sharp focus, illuminating concerns over discriminatory decision making that negatively impacts certain individuals or groups.
What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement Learning
In sequential decision-making problems involving sensitive attributes like race and gender, reinforcement learning (RL) agents must carefully consider long-term fairness while maximizing returns.
Seeing Text in the Dark: Algorithm and Benchmark
Localizing text in low-light environments is challenging due to visual degradations.
Transductive Reward Inference on Graph
In this study, we present a transductive inference approach on that reward information propagation graph, which enables the effective estimation of rewards for unlabelled data in offline reinforcement learning.
On-Device Recommender Systems: A Comprehensive Survey
Recently, driven by the advances in storage, communication, and computation capabilities of edge devices, there has been a shift of focus from CloudRSs to on-device recommender systems (DeviceRSs), which leverage the capabilities of edge devices to minimize centralized data storage requirements, reduce the response latency caused by communication overheads, and enhance user privacy and security by localizing data processing and model training.
Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey
Furthermore, in relation to the creation and application of foundation models for weather and climate data understanding, we delve into the field's prevailing challenges, offer crucial insights, and propose detailed avenues for future research.
Causal Reinforcement Learning: A Survey
Causality, however, offers a notable advantage as it can formalize knowledge in a systematic manner and leverage invariance for effective knowledge transfer.
Improving the Robustness of Summarization Systems with Dual Augmentation
The other factor is in the latent space, where the attacked inputs bring more variations to the hidden states.
Federated Prompt Learning for Weather Foundation Models on Devices
On-device intelligence for weather forecasting uses local deep learning models to analyze weather patterns without centralized cloud computing, holds significance for supporting human activates.
Voting from Nearest Tasks: Meta-Vote Pruning of Pre-trained Models for Downstream Tasks
To address these challenges, we create a small model for a new task from the pruned models of similar tasks.
Dual Personalization on Federated Recommendation
Moreover, we provide visualizations and in-depth analysis of the personalization techniques in item embedding, which shed novel insights on the design of recommender systems in federated settings.
Federated Learning on Non-IID Graphs via Structural Knowledge Sharing
Inspired by this, we propose FedStar, an FGL framework that extracts and shares the common underlying structure information for inter-graph federated learning tasks.
Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection
Empirically, DERM outperformed the state-of-the-art on the unsupervised AD benchmark consisting of 18 datasets.
Perceiving the World: Question-guided Reinforcement Learning for Text-based Games
Text-based games provide an interactive way to study natural language processing.
On the Convergence of Clustered Federated Learning
Knowledge sharing and model personalization are essential components to tackle the non-IID challenge in federated learning (FL).
EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning
They are complementary in acquiring more informative feedback for RL: the planning policy provides dense reward of finishing easier sub-tasks while the environment policy modifies these sub-tasks to be adequately challenging and diverse so the RL agent can quickly adapt to different tasks/environments.
Vote for Nearest Neighbors Meta-Pruning of Self-Supervised Networks
Can we find a better initialization for a new task, e. g., a much smaller network closer to the final pruned model, by exploiting its similar tasks?
Goal Randomization for Playing Text-based Games without a Reward Function
In a variety of text-based games, we show that this simple method results in competitive performance for agents.
Uncertainty Regularized Policy Learning for Offline Reinforcement Learning
URPL adds an uncertainty regularization term in the policy learning objective to enforce to learn a more stable policy under the offline setting.
Generalization in Text-based Games via Hierarchical Reinforcement Learning
Deep reinforcement learning provides a promising approach for text-based games in studying natural language communication between humans and artificial agents.
Deep Reinforcement Learning
Hierarchical Reinforcement Learning
+3
Federated Learning for Open Banking
In the near future, it is foreseeable to have decentralized data ownership in the finance sector using federated learning.
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.
MIPO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning
Hence, some recent works train healthcare representations by incorporating medical ontology, by self-supervised tasks like diagnosis prediction, but (1) the small-scale, monotonous ontology is insufficient for robust learning, and (2) critical contexts or dependencies underlying patient journeys are barely exploited to enhance ontology learning.
Beyond Low-pass Filtering: Graph Convolutional Networks with Automatic Filtering
Second, the bandwidth of existing graph convolutional filters is fixed.
FedProto: Federated Prototype Learning across Heterogeneous Clients
Heterogeneity across clients in federated learning (FL) usually hinders the optimization convergence and generalization performance when the aggregation of clients' knowledge occurs in the gradient space.
Isometric Propagation Network for Generalized Zero-shot Learning
To resolve this problem, we propose Isometric Propagation Network (IPN), which learns to strengthen the relation between classes within each space and align the class dependency in the two spaces.
MASP: Model-Agnostic Sample Propagation for Few-shot learning
Few-shot learning aims to train a classifier given only a few samples per class that are highly insufficient to describe the whole data distribution.
Extract Local Inference Chains of Deep Neural Nets
In this paper, we introduce an efficient method, \name, to extract the local inference chains by optimizing a differentiable sparse scoring for the filters and layers to preserve the outputs on given data from a local region.
Towards Coarse and Fine-grained Multi-Graph Multi-Label Learning
Multi-graph multi-label learning (\textsc{Mgml}) is a supervised learning framework, which aims to learn a multi-label classifier from a set of labeled bags each containing a number of graphs.
Cooperative Heterogeneous Deep Reinforcement Learning
Numerous deep reinforcement learning agents have been proposed, and each of them has its strengths and flaws.
Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games
We study reinforcement learning (RL) for text-based games, which are interactive simulations in the context of natural language.
RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion
Many graph embedding approaches have been proposed for knowledge graph completion via link prediction.
Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention
Then, facilitated by the proposed base model, we introduce collaborating relation features shared among relations in the hierarchies to promote the relation-augmenting process and balance the training data for long-tail relations.
BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes
Electronic health records (EHRs) are longitudinal records of a patient's interactions with healthcare systems.
Attribute Propagation Network for Graph Zero-shot Learning
To address this challenging task, most ZSL methods relate unseen test classes to seen(training) classes via a pre-defined set of attributes that can describe all classes in the same semantic space, so the knowledge learned on the training classes can be adapted to unseen classes.
Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy
We study many-class few-shot (MCFS) problem in both supervised learning and meta-learning settings.
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic.
Ranked #1 on
Univariate Time Series Forecasting
on Electricity
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.
Learning to Propagate for Graph Meta-Learning
It can significantly improve tasks that suffer from insufficient training data, e. g., few shot learning.
Attributed Graph Clustering: A Deep Attentional Embedding Approach
Graph clustering is a fundamental task which discovers communities or groups in networks.
Ranked #8 on
Node Clustering
on Cora
Graph WaveNet for Deep Spatial-Temporal Graph Modeling
Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system.
Ranked #2 on
Traffic Prediction
on LargeST
Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph
The resulting graph of prototypes can be continually re-used and updated for new tasks and classes.
MahiNet: A Neural Network for Many-Class Few-Shot Learning with Class Hierarchy
It addresses the ``many-class'' problem by exploring the class hierarchy, e. g., the coarse-class label that covers a subset of fine classes, which helps to narrow down the candidates for the fine class and is cheaper to obtain.
Attributed Network Embedding via Subspace Discovery
In this paper, we propose a unified framework for attributed network embedding-attri2vec-that learns node embeddings by discovering a latent node attribute subspace via a network structure guided transformation performed on the original attribute space.
Ranked #1 on
Node Clustering
on Facebook
Search Efficient Binary Network Embedding
In this paper, we propose a search efficient binary network embedding algorithm called BinaryNE to learn a binary code for each node, by simultaneously modeling node context relations and node attribute relations through a three-layer neural network.
Learning Graph Embedding with Adversarial Training Methods
Based on this framework, we derive two variants of adversarial models, the adversarially regularized graph autoencoder (ARGA) and its variational version, adversarially regularized variational graph autoencoder (ARVGA), to learn the graph embedding effectively.
Ranked #7 on
Node Clustering
on Cora
A Comprehensive Survey on Graph Neural Networks
In this survey, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields.
A Review for Weighted MinHash Algorithms
In this review, we mainly categorize the Weighted MinHash algorithms into quantization-based approaches, "active index"-based ones and others, and show the evolution and inherent connection of the weighted MinHash algorithms, from the integer weighted MinHash algorithms to real-valued weighted MinHash ones (particularly the Consistent Weighted Sampling scheme).
Data Structures and Algorithms
Binarized Attributed Network Embedding
To this end, we present a Binarized Attributed Network Embedding model (BANE for short) to learn binary node representation.
Ranked #1 on
Link Prediction
on Wiki
SINE: Scalable Incomplete Network Embedding
In this paper, we propose a Scalable Incomplete Network Embedding (SINE) algorithm for learning node representations from incomplete graphs.
Social and Information Networks
Tensorized Self-Attention: Efficiently Modeling Pairwise and Global Dependencies Together
Neural networks equipped with self-attention have parallelizable computation, light-weight structure, and the ability to capture both long-range and local dependencies.
Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling
In this paper, we propose a model, called "bi-directional block self-attention network (Bi-BloSAN)", for RNN/CNN-free sequence encoding.
MetaGraph2Vec: Complex Semantic Path Augmented Heterogeneous Network Embedding
Network embedding in heterogeneous information networks (HINs) is a challenging task, due to complications of different node types and rich relationships between nodes.
Social and Information Networks
Adversarially Regularized Graph Autoencoder for Graph Embedding
Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics.
Ranked #5 on
Link Prediction
on Pubmed
Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling
In this paper, we integrate both soft and hard attention into one context fusion model, "reinforced self-attention (ReSA)", for the mutual benefit of each other.
Ranked #56 on
Natural Language Inference
on SNLI
Network Representation Learning: A Survey
Network representation learning has been recently proposed as a new learning paradigm to embed network vertices into a low-dimensional vector space, by preserving network topology structure, vertex content, and other side information.
Deep Learning from Noisy Image Labels with Quality Embedding
However, the label noise among the datasets severely degenerates the \mbox{performance of deep} learning approaches.
DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding
Recurrent neural nets (RNN) and convolutional neural nets (CNN) are widely used on NLP tasks to capture the long-term and local dependencies, respectively.
Ranked #69 on
Natural Language Inference
on SNLI
Improved Consistent Weighted Sampling Revisited
Min-Hash is a popular technique for efficiently estimating the Jaccard similarity of binary sets.
Data Structures and Algorithms
Temporal Feature Selection on Networked Time Series
For example, social network users are considered to be social sensors that continuously generate social signals (tweets) represented as a time series.
Dynamic Concept Composition for Zero-Example Event Detection
In this paper, we focus on automatically detecting events in unconstrained videos without the use of any visual training exemplars.
Compound Rank-k Projections for Bilinear Analysis
In many real-world applications, data are represented by matrices or high-order tensors.
