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08/25/2023
Resource-Efficient Federated Learning for Heterogenous and Resource-Constrained Environments
Federated Learning (FL) is a privacy-enforcing sub-domain of machine lea...
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08/16/2023
Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value Extraction
Existing attribute-value extraction (AVE) models require large quantitie...
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06/02/2022
Hard Negative Sampling Strategies for Contrastive Representation Learning
One of the challenges in contrastive learning is the selection of approp...
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12/08/2021
Prompt-based Zero-shot Relation Classification with Semantic Knowledge Augmentation
Recognizing unseen relations with no training instances is a challenging...
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11/13/2020
Zero-shot Learning for Relation Extraction
Most existing supervised and few-shot learning relation extraction metho...
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10/14/2020
Graph Deep Factors for Forecasting
Deep probabilistic forecasting techniques have recently been proposed fo...
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09/26/2020
Reinforcement Learning-based N-ary Cross-Sentence Relation Extraction
The models of n-ary cross sentence relation extraction based on distant ...
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09/26/2020
Clustering-based Unsupervised Generative Relation Extraction
This paper focuses on the problem of unsupervised relation extraction. E...
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09/25/2020
A Context Integrated Relational Spatio-Temporal Model for Demand and Supply Forecasting
Traditional methods for demand forecasting only focus on modeling the te...
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09/25/2020
Investigating Misinformation in Online Marketplaces: An Audit Study on Amazon
Search and recommendation systems are ubiquitous and irreplaceable tools...
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08/10/2020
Two-Stage Clustering of Household Electricity Load Shapes based on Temporal Pattern Peak Demand
Analyzing smart meter data to understand energy consumption patterns hel...
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08/10/2020
Predicting Coordinated Actuated Traffic Signal Change Times using LSTM Neural Networks
Vehicle acceleration and deceleration maneuvers at traffic signals resul...
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01/03/2019
Secure Two-Party Feature Selection
In this work, we study how to securely evaluate the value of trading dat...
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02/07/2018
Learning Role-based Graph Embeddings
Random walks are at the heart of many existing network embedding methods...
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10/27/2017
Similarity-based Multi-label Learning
Multi-label classification is an important learning problem with many ap...
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10/25/2017
Inductive Representation Learning in Large Attributed Graphs
Graphs (networks) are ubiquitous and allow us to model entities (nodes) ...
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09/14/2017
