Search Results for author:
Found 21 papers, 3 papers with code
Unsupervised Relation Extraction: A Variational Autoencoder Approach
We propose a VAE-based unsupervised relation extraction technique that overcomes this limitation by using the classifications as an intermediate variable instead of a latent variable.
Few-Shot Relation Extraction with Hybrid Visual Evidence
The MFS-HVE semantic feature extractors are developed to extract both textual and visual features.
Multi-Label Zero-Shot Product Attribute-Value Extraction
We propose HyperPAVE, a multi-label zero-shot attribute value extraction model that leverages inductive inference in heterogeneous hypergraphs.
GradXKG: A Universal Explain-per-use Temporal Knowledge Graph Explainer
This helps address the lack of interpretability in existing TKGR models and provides a universal explanation approach applicable across various models.
REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments
FL emerges as a privacy-enforcing sub-domain of machine learning that enables model training on client devices, eliminating the necessity to share private data with a central server.
Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value Extraction
Existing attribute-value extraction (AVE) models require large quantities of labeled data for training.
Hard Negative Sampling Strategies for Contrastive Representation Learning
One of the challenges in contrastive learning is the selection of appropriate \textit{hard negative} examples, in the absence of label information.
Prompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation
We learn the representations of both seen and unseen relations with augmented instances and prompts.
Singular Perturbation-based Reinforcement Learning of Two-Point Boundary Optimal Control Systems
This work presents a technique for learning systems, where the learning process is guided by knowledge of the physics of the system.
Zero-shot Relation Classification from Side Information
We propose a zero-shot learning relation classification (ZSLRC) framework that improves on state-of-the-art by its ability to recognize novel relations that were not present in training data.
Graph Deep Factors for Forecasting
GraphDF is a hybrid forecasting framework that consists of a relational global and relational local model.
Reinforcement Learning-based N-ary Cross-Sentence Relation Extraction
In this paper, we relax this strong assumption by a weaker distant supervision assumption to address the second issue and propose a novel sentence distribution estimator model to address the first problem.
Clustering-based Unsupervised Generative Relation Extraction
To address this issue, we propose a Clustering-based Unsupervised generative Relation Extraction (CURE) framework that leverages an "Encoder-Decoder" architecture to perform self-supervised learning so the encoder can extract relation information.
Investigating Misinformation in Online Marketplaces: An Audit Study on Amazon
Our audit study investigates (a) factors that might influence the search algorithms of Amazon and (b) personalization attributes that contribute to amplifying the amount of misinformation recommended to users in their search results and recommendations.
A Context Integrated Relational Spatio-Temporal Model for Demand and Supply Forecasting
We propose a novel context integrated relational model, Context Integrated Graph Neural Network (CIGNN), which leverages the temporal, relational, spatial, and dynamic contextual dependencies for multi-step ahead demand forecasting.
Predicting Coordinated Actuated Traffic Signal Change Times using LSTM Neural Networks
Vehicle acceleration and deceleration maneuvers at traffic signals results in significant fuel and energy consumption levels.
Two-stage building energy consumption clustering based on temporal and peak demand patterns
In the first stage, load shapes are clustered by allowing a large number of clusters to accurately capture variations in energy use patterns and cluster centroids are extracted by accounting for shape misalignments.
Learning Role-based Graph Embeddings
Random walks are at the heart of many existing network embedding methods.
Similarity-based Multi-label Learning
Multi-label classification is an important learning problem with many applications.
Inductive Representation Learning in Large Attributed Graphs
To make these methods more generally applicable, we propose a framework for inductive network representation learning based on the notion of attributed random walk that is not tied to node identity and is instead based on learning a function $\Phi : \mathrm{\rm \bf x} \rightarrow w$ that maps a node attribute vector $\mathrm{\rm \bf x}$ to a type $w$.
A Framework for Generalizing Graph-based Representation Learning Methods
Random walks are at the heart of many existing deep learning algorithms for graph data.
