Search Results for author:
Found 88 papers, 40 papers with code
Understand before Answer: Improve Temporal Reading Comprehension via Precise Question Understanding
Precise question understanding is critical for temporal reading comprehension.
Continual Task Allocation in Meta-Policy Network via Sparse Prompting
How to train a generalizable meta-policy by continually learning a sequence of tasks?
Spatial-temporal Prompt Learning for Federated Weather Forecasting
Federated weather forecasting is a promising collaborative learning framework for analyzing meteorological data across participants from different countries and regions, thus embodying a global-scale real-time weather data predictive analytics platform to tackle climate change.
IFedRec: Item-Guided Federated Aggregation for Cold-Start
To recommend cold items, existing federated recommendation models require collecting new interactions from users and retraining the model, which is time-consuming and poses a privacy threat to users' sensitive information.
Graph-guided Personalization for Federated Recommendation
Federated Recommendation is a new service architecture providing recommendations without sharing user data with the server.
Knowledge Refinement via Interaction Between Search Engines and Large Language Models
Information retrieval (IR) plays a crucial role in locating relevant resources from vast amounts of data, and its applications have evolved from traditional knowledge bases to modern search engines (SEs).
Large Language Models are Strong Zero-Shot Retriever
In this work, we propose a simple method that applies a large language model (LLM) to large-scale retrieval in zero-shot scenarios.
Does Continual Learning Equally Forget All Parameters?
In this paper, we study which modules in neural networks are more prone to forgetting by investigating their training dynamics during CL.
Neural Time Series Analysis with Fourier Transform: A Survey
However, although increasing attention has been attracted and research is flourishing in this emerging area, there lacks a systematic review of the variety of existing studies in the area.
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.
Style-Aware Contrastive Learning for Multi-Style Image Captioning
Existing multi-style image captioning methods show promising results in generating a caption with accurate visual content and desired linguistic style.
Multimodal Event Transformer for Image-guided Story Ending Generation
Specifically, we construct visual and semantic event graphs from story plots and ending image, and leverage event-based reasoning to reason and mine implicit information in a single modality.
Improving Cross-modal Alignment for Text-Guided Image Inpainting
Text-guided image inpainting (TGII) aims to restore missing regions based on a given text in a damaged image.
Federated Recommendation with Additive Personalization
To address these challenges, we propose Federated Recommendation with Additive Personalization (FedRAP), which learns a global view of items via FL and a personalized view locally on each user.
Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data
To relieve the data exposure concern across regions, a novel federated learning approach has been proposed to collaboratively learn a brand-new spatio-temporal Transformer-based foundation model across participants with heterogeneous meteorological data.
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.
Fine-Grained Distillation for Long Document Retrieval
Long document retrieval aims to fetch query-relevant documents from a large-scale collection, where knowledge distillation has become de facto to improve a retriever by mimicking a heterogeneous yet powerful cross-encoder.
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.
CCPrompt: Counterfactual Contrastive Prompt-Tuning for Many-Class Classification
The sharing task description is unable to stimulate the unique task-related information in each training sample, especially for tasks with the finite-label space.
Unsupervised Knowledge Graph Construction and Event-centric Knowledge Infusion for Scientific NLI
Therefore, we propose an unsupervised knowledge graph construction method to build a scientific knowledge graph (SKG) without any labeled data.
Federated Learning from Pre-Trained Models: A Contrastive Learning Approach
To prevent these issues from hindering the deployment of FL systems, we propose a lightweight framework where clients jointly learn to fuse the representations generated by multiple fixed pre-trained models rather than training a large-scale model from scratch.
Towards Robust Ranker for Text Retrieval
A ranker plays an indispensable role in the de facto 'retrieval & rerank' pipeline, but its training still lags behind -- learning from moderate negatives or/and serving as an auxiliary module for a retriever.
FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels
Federated learning (FL) aims at training a global model on the server side while the training data are collected and located at the local devices.
ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification
Generating new events given context with correlated ones plays a crucial role in many event-centric reasoning tasks.
Personalized Federated Learning With Graph
We propose a novel structured federated learning (SFL) framework to learn both the global and personalized models simultaneously using client-wise relation graphs and clients' private data.
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).
CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum
Next, a bottom-up traversal of the tree trains the RL agent from easier sub-tasks with denser rewards on bottom layers to harder ones on top layers and collects its cost on each sub-task train the planner in the next episode.
EventBERT: A Pre-Trained Model for Event Correlation Reasoning
Event correlation reasoning infers whether a natural language paragraph containing multiple events conforms to human common sense.
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.
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?
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.
Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision
It consists of (1) a pairwise type-enriched sentence encoding module injecting both context-free and -related backgrounds to alleviate sentence-level wrong labeling, and (2) a hierarchical type-sentence alignment module enriching a sentence with the triple fact's basic attributes to support long-tail relations.
Sequential Diagnosis Prediction with Transformer and Ontological Representation
Sequential diagnosis prediction on the Electronic Health Record (EHR) has been proven crucial for predictive analytics in the medical domain.
Eliminating Sentiment Bias for Aspect-Level Sentiment Classification with Unsupervised Opinion Extraction
Aspect-level sentiment classification (ALSC) aims at identifying the sentiment polarity of a specified aspect in a sentence.
Aspect-Based Sentiment Analysis (ABSA)
Representation Learning
+1
TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting
This paper aims to unify spatial dependency and temporal dependency in a non-Euclidean space while capturing the inner spatial-temporal dependencies for traffic data.
Federated Learning for Open Banking
In the near future, it is foreseeable to have decentralized data ownership in the finance sector using federated learning.
Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health
Implementing an open innovation framework in the healthcare industry, namely open health, is to enhance innovation and creative capability of health-related organisations by building a next-generation collaborative framework with partner organisations and the research community.
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.
Reasoning over Entity-Action-Location Graph for Procedural Text Understanding
Procedural text understanding aims at tracking the states (e. g., create, move, destroy) and locations of the entities mentioned in a given paragraph.
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.
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).
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation.
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.
Episodic memory governs choices: An RNN-based reinforcement learning model for decision-making task
Typical methods to study cognitive function are to record the electrical activities of animal neurons during the training of animals performing behavioral tasks.
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.
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.
SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning
We also instantiate our framework with different combinations, which set the new state of the art on benchmark-simulated and real-world datasets with noisy labels.
Confusable Learning for Large-class Few-Shot Classification
Specifically, our method maintains a dynamically updating confusion matrix, which analyzes confusable classes in the dataset.
Cooperative Heterogeneous Deep Reinforcement Learning
Numerous deep reinforcement learning agents have been proposed, and each of them has its strengths and flaws.
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.
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.
BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes
Electronic health records (EHRs) are longitudinal records of a patient's interactions with healthcare systems.
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.
A Universal Representation Transformer Layer for Few-Shot Image Classification
We consider the problem of multi-domain few-shot image classification, where unseen classes and examples come from diverse data sources.
Ranked #1 on
Few-Shot Image Classification
on Meta-Dataset Rank
Self-Attention Enhanced Patient Journey Understanding in Healthcare System
The key challenge of patient journey understanding is to design an effective encoding mechanism which can properly tackle the aforementioned multi-level structured patient journey data with temporal sequential visits and a set of medical codes.
Interpretable Time-series Classification on Few-shot Samples
Recent few-shot learning works focus on training a model with prior meta-knowledge to fast adapt to new tasks with unseen classes and samples.
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 #2 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.
Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion
In experiments, we achieve state-of-the-art performance on three benchmarks and a zero-shot dataset for link prediction, with highlights of inference costs reduced by 1-2 orders of magnitude compared to a textual encoding method.
Ranked #4 on
Link Prediction
on UMLS
Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning
In contrast to existing paradigms, our approach uses knowledge graphs implicitly, only during pre-training, to inject language models with structured knowledge via learning from raw text.
Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification
Particularly, it is a set of kernel sizes that can efficiently cover the best RF size across different datasets via consisting of multiple prime numbers according to the length of the time series.
Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction
Distantly supervised relation extraction intrinsically suffers from noisy labels due to the strong assumption of distant supervision.
Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications
Suicide is a critical issue in modern society.
Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base
We consider the problem of conversational question answering over a large-scale knowledge base.
Temporal Self-Attention Network for Medical Concept Embedding
In this paper, we propose a medical concept embedding method based on applying a self-attention mechanism to represent each medical concept.
Learning to Propagate for Graph Meta-Learning
It can significantly improve tasks that suffer from insufficient training data, e. g., few shot learning.
Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering
These two problems lead to a poorly-trained semantic parsing model.
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 #8 on
Traffic Prediction
on PEMS-BAY
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.
DAGCN: Dual Attention Graph Convolutional Networks
In this paper, we propose a novel framework called, dual attention graph convolutional networks (DAGCN) to address these problems.
Ranked #23 on
Graph Classification
on NCI1
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.
Learning Private Neural Language Modeling with Attentive Aggregation
Federated learning (FL) provides a promising approach to learning private language modeling for intelligent personalized keyboard suggestion by training models in distributed clients rather than training in a central server.
Distributionally Robust Semi-Supervised Learning for People-Centric Sensing
Semi-supervised learning is crucial for alleviating labelling burdens in people-centric sensing.
NeuRec: On Nonlinear Transformation for Personalized Ranking
Modeling user-item interaction patterns is an important task for personalized recommendations.
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.
Multi-modality Sensor Data Classification with Selective Attention
Multimodal wearable sensor data classification plays an important role in ubiquitous computing and has a wide range of applications in scenarios from healthcare to entertainment.
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.
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 #4 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
Marginalized graph autoencoder for graph clustering
In this paper, we propose a novel marginalized graph autoencoder (MGAE) algorithm for graph clustering.
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
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.
