Search Results for author:
Found 60 papers, 29 papers with code
Integrating Semantic Scenario and Word Relations for Abstractive Sentence Summarization
Recently graph-based methods have been adopted for Abstractive Text Summarization.
Public Sentiment Drift Analysis Based on Hierarchical Variational Auto-encoder
Detecting public sentiment drift is a challenging task due to sentiment change over time.
Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization
In this paper, we propose a novel Frame Semantic-Enhanced Sentence Modeling for Extractive Summarization, which leverages Frame semantics to model sentences from both intra-sentence level and inter-sentence level, facilitating the text summarization task.
TSLANet: Rethinking Transformers for Time Series Representation Learning
Time series data, characterized by its intrinsic long and short-range dependencies, poses a unique challenge across analytical applications.
An Experiment with the Use of ChatGPT for LCSH Subject Assignment on Electronic Theses and Dissertations
This study delves into the potential use of Large Language Models (LLMs) for generating Library of Congress Subject Headings (LCSH).
A Survey of Neural Code Intelligence: Paradigms, Advances and Beyond
Building on our examination of the developmental trajectories, we further investigate the emerging synergies between code intelligence and broader machine intelligence, uncovering new cross-domain opportunities and illustrating the substantial influence of code intelligence across various domains.
K-Link: Knowledge-Link Graph from LLMs for Enhanced Representation Learning in Multivariate Time-Series Data
To capture these dependencies, Graph Neural Networks (GNNs) have emerged as powerful tools, yet their effectiveness is restricted by the quality of graph construction from MTS data.
Generative Semi-supervised Graph Anomaly Detection
This work considers a practical semi-supervised graph anomaly detection (GAD) scenario, where part of the nodes in a graph are known to be normal, contrasting to the unsupervised setting in most GAD studies with a fully unlabeled graph.
Self-evolving Autoencoder Embedded Q-Network
During the evolution of the autoencoder architecture, a bias-variance regulatory strategy is employed to elicit the optimal response from the RL agent.
Evolving Restricted Boltzmann Machine-Kohonen Network for Online Clustering
A novel online clustering algorithm is presented where an Evolving Restricted Boltzmann Machine (ERBM) is embedded with a Kohonen Network called ERBM-KNet.
CompeteSMoE - Effective Training of Sparse Mixture of Experts via Competition
Sparse mixture of experts (SMoE) offers an appealing solution to scale up the model complexity beyond the mean of increasing the network's depth or width.
CEL: A Continual Learning Model for Disease Outbreak Prediction by Leveraging Domain Adaptation via Elastic Weight Consolidation
This model aims to mitigate the catastrophic forgetting phenomenon in a domain incremental setting.
A Change Point Detection Integrated Remaining Useful Life Estimation Model under Variable Operating Conditions
During online monitoring, the temporal correlation dynamics of a query device is monitored for breach of the control limit derived in offline training.
HyperRouter: Towards Efficient Training and Inference of Sparse Mixture of Experts
By routing input tokens to only a few split experts, Sparse Mixture-of-Experts has enabled efficient training of large language models.
SEA++: Multi-Graph-based High-Order Sensor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation
In this paper, we propose SEnsor Alignment (SEA) for MTS-UDA, aiming to reduce domain discrepancy at both the local and global sensor levels.
Decomposed Prompt Tuning via Low-Rank Reparameterization
While prompt tuning approaches have achieved competitive performance with high efficiency, we observe that they invariably employ the same initialization process, wherein the soft prompt is either randomly initialized or derived from an existing embedding vocabulary.
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
In this survey, we offer a comprehensive and up-to-date review of large models tailored (or adapted) for time series and spatio-temporal data, spanning four key facets: data types, model categories, model scopes, and application areas/tasks.
Graph-Aware Contrasting for Multivariate Time-Series Classification
As MTS data typically originate from multiple sensors, ensuring spatial consistency becomes essential for the overall performance of contrastive learning on MTS data.
Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data
For graph construction, we design a decay graph to connect sensors across all timestamps based on their temporal distances, enabling us to fully model the ST dependencies by considering the correlations between DEDT.
Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural Networks
On ImageNet, we achieve up to 4. 7% and 4. 6% higher top-1 accuracy compared to other methods for VGG-16 and ResNet-50, respectively.
Unlimited Knowledge Distillation for Action Recognition in the Dark
To address this issue, we propose an unlimited knowledge distillation (UKD) in this paper.
Source-Free Domain Adaptation with Temporal Imputation for Time Series Data
The existing SFDA methods that are mainly designed for visual applications may fail to handle the temporal dynamics in time series, leading to impaired adaptation performance.
Distilling Universal and Joint Knowledge for Cross-Domain Model Compression on Time Series Data
More specifically, a feature-domain discriminator is employed to align teacher's and student's representations for universal knowledge transfer.
MS-DETR: Natural Language Video Localization with Sampling Moment-Moment Interaction
Given a query, the task of Natural Language Video Localization (NLVL) is to localize a temporal moment in an untrimmed video that semantically matches the query.
SemiGNN-PPI: Self-Ensembling Multi-Graph Neural Network for Efficient and Generalizable Protein-Protein Interaction Prediction
Protein-protein interactions (PPIs) are crucial in various biological processes and their study has significant implications for drug development and disease diagnosis.
A Federated Learning-based Industrial Health Prognostics for Heterogeneous Edge Devices using Matched Feature Extraction
The algorithm searches across the heterogeneous locally trained models and matches neurons with probabilistically similar feature extraction functions first, before selectively averaging them to form the federated model parameters.
Shall We Trust All Relational Tuples by Open Information Extraction? A Study on Speculation Detection
We formally define the research problem of tuple-level speculation detection and conduct a detailed data analysis on the LSOIE dataset which contains labels for speculative tuples.
Open Information Extraction via Chunks
Accordingly, we propose a simple BERT-based model for sentence chunking, and propose Chunk-OIE for tuple extraction on top of SaC.
Augmenting and Aligning Snippets for Few-Shot Video Domain Adaptation
We thus consider a more realistic \textit{Few-Shot Video-based Domain Adaptation} (FSVDA) scenario where we adapt video models with only a few target video samples.
Label-efficient Time Series Representation Learning: A Review
The scarcity of labeled data is one of the main challenges of applying deep learning models on time series data in the real world.
Syntactic Multi-view Learning for Open Information Extraction
In this paper, we model both constituency and dependency trees into word-level graphs, and enable neural OpenIE to learn from the syntactic structures.
Ranked #1 on
Open Information Extraction
on LSOIE-wiki
Contrastive Domain Adaptation for Time-Series via Temporal Mixup
Specifically, we propose a novel temporal mixup strategy to generate two intermediate augmented views for the source and target domains.
Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey
To tackle performance degradation and address concerns in high video annotation cost uniformly, the video unsupervised domain adaptation (VUDA) is introduced to adapt video models from the labeled source domain to the unlabeled target domain by alleviating video domain shift, improving the generalizability and portability of video models.
Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation
The past few years have witnessed a remarkable advance in deep learning for EEG-based sleep stage classification (SSC).
Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification
Specifically, we propose time-series specific weak and strong augmentations and use their views to learn robust temporal relations in the proposed temporal contrasting module, besides learning discriminative representations by our proposed contextual contrasting module.
Leveraging Endo- and Exo-Temporal Regularization for Black-box Video Domain Adaptation
To enable video models to be applied seamlessly across video tasks in different environments, various Video Unsupervised Domain Adaptation (VUDA) methods have been proposed to improve the robustness and transferability of video models.
A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges
We believe that this technical review can help to promote a sustainable development of AI R&D activities for the research community.
Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs
Multi-hop reasoning over real-life knowledge graphs (KGs) is a highly challenging problem as traditional subgraph matching methods are not capable to deal with noise and missing information.
Slow-varying Dynamics Assisted Temporal Capsule Network for Machinery Remaining Useful Life Estimation
Similarly, the estimation accuracy of the milling machine has been improved by 23. 57% compared to LSTM and 19. 54% compared to CapsNet.
ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data
Our evaluation includes adapting state-of-the-art visual domain adaptation methods to time series data as well as the recent methods specifically developed for time series data.
Going Deeper into Recognizing Actions in Dark Environments: A Comprehensive Benchmark Study
While action recognition (AR) has gained large improvements with the introduction of large-scale video datasets and the development of deep neural networks, AR models robust to challenging environments in real-world scenarios are still under-explored.
Self-supervised Autoregressive Domain Adaptation for Time Series Data
Second, we propose a novel autoregressive domain adaptation technique that incorporates temporal dependency of both source and target features during domain alignment.
A Systematic Evaluation of Domain Adaptation Algorithms On Time Series Data
Our evaluation includes adaptations of state-of-the-art visual domain adaptation methods to time series data in addition to recent methods specifically developed for time series data.
Two Eyes Are Better Than One: Exploiting Binocular Correlation for Diabetic Retinopathy Severity Grading
Diabetic retinopathy (DR) is one of the most common eye conditions among diabetic patients.
A Knowledge-Guided Framework for Frame Identification
In this paper, we propose a Knowledge-Guided Frame Identification framework (KGFI) that integrates three types frame knowledge, including frame definitions, frame elements and frame-to-frame relations, to learn better frame representation, which guides the KGFI to jointly map target words and frames into the same embedding space and subsequently identify the best frame by calculating the dot-product similarity scores between the target word embedding and all of the frame embeddings.
A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guarantee
In addition, we theoretically analyze and prove the convergence of CMFL under different election and selection strategies, which coincides with the experimental results.
ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training
Second, we design an iterative self-training strategy to improve the classification performance on the target domain via target domain pseudo labels.
A Conditional Splitting Framework for Efficient Constituency Parsing
We introduce a generic seq2seq parsing framework that casts constituency parsing problems (syntactic and discourse parsing) into a series of conditional splitting decisions.
Time-Series Representation Learning via Temporal and Contextual Contrasting
In this paper, we propose an unsupervised Time-Series representation learning framework via Temporal and Contextual Contrasting (TS-TCC), to learn time-series representation from unlabeled data.
Ranked #1 on
Recognizing And Localizing Human Actions
on HAR
Automatic Sleep Stage Classification
Contrastive Learning
+9
Multilinear Dirichlet Processes
Dependent Dirichlet processes (DDP) have been widely applied to model data from distributions over collections of measures which are correlated in some way.
CODA: Constructivism Learning for Instance-Dependent Dropout Architecture Construction
To solve this issue, we propose Constructivism learning for instance-dependent Dropout Architecture (CODA), which is inspired from a philosophical theory, constructivism learning.
RST Parsing from Scratch
We introduce a novel top-down end-to-end formulation of document-level discourse parsing in the Rhetorical Structure Theory (RST) framework.
Ranked #1 on
Discourse Parsing
on RST-DT
DocOIE: A Document-level Context-Aware Dataset for OpenIE
Both DocOIE dataset and DocIE model are released for public.
Ranked #1 on
Open Information Extraction
on DocOIE-transportation
An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG
The MRCNN can extract low and high frequency features and the AFR is able to improve the quality of the extracted features by modeling the inter-dependencies between the features.
Ranked #1 on
Automatic Sleep Stage Classification
on Sleep-EDF
DO-GAN: A Double Oracle Framework for Generative Adversarial Networks
In this paper, we propose a new approach to train Generative Adversarial Networks (GANs) where we deploy a double-oracle framework using the generator and discriminator oracles.
FFConv: Fast Factorized Convolutional Neural Network Inference on Encrypted Data
Despite the faster HECNN inference, the mainstream packing schemes Dense Packing (DensePack) and Convolution Packing (ConvPack) introduce expensive rotation overhead, which prolongs the inference latency of HECNN for deeper and wider CNN architectures.
Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension
Machine reading comprehension (MRC) is one of the most critical yet challenging tasks in natural language understanding(NLU), where both syntax and semantics information of text are essential components for text understanding.
Machine Reading Comprehension
Natural Language Understanding
Sea-Net: Squeeze-And-Excitation Attention Net For Diabetic Retinopathy Grading
Diabetes is one of the most common disease in individuals.
Variable-Length Hashing
In particular, we propose a block K-means hashing (B-KMH) method to obtain significantly improved retrieval performance with no increase in storage and marginal increase in computational cost.
