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Found 17 papers, 3 papers with code
Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes
We investigate the effectiveness of DACD method in diverse scenarios and show it outperforms other active learning change-point detection approaches.
Online Auction-Based Incentive Mechanism Design for Horizontal Federated Learning with Budget Constraint
To obtain a high-quality model, an incentive mechanism is necessary to motivate more high-quality workers with data and computing power.
Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurement
Federated learning trains models across devices with distributed data, while protecting the privacy and obtaining a model similar to that of centralized ML.
Boost-R: Gradient Boosted Trees for Recurrence Data
Boost-R constructs an ensemble of gradient boosted additive trees to estimate the cumulative intensity function of the recurrent event process, where a new tree is added to the ensemble by minimizing the regularized L2 distance between the observed and predicted cumulative intensity.
An Adversarial Transfer Network for Knowledge Representation Learning
Specifically, we add soft constraints on aligned entity pairs and neighbours to the existing knowledge representation learning methods.
Neural Image Compression via Attentional Multi-Scale Back Projection and Frequency Decomposition
In recent years, neural image compression emerges as a rapidly developing topic in computer vision, where the state-of-the-art approaches now exhibit superior compression performance than their conventional counterparts.
Incorporating Graph Attention Mechanism into Knowledge Graph Reasoning Based on Deep Reinforcement Learning
Meanwhile, a novel mechanism of reinforcement learning is proposed by forcing an agent to walk forward every step to avoid the agent stalling at the same entity node constantly.
An Encoder with non-Sequential Dependency for Neural Data-to-Text Generation
Data-to-text generation aims to generate descriptions given a structured input data (i. e., a table with multiple records).
A Simple Recipe towards Reducing Hallucination in Neural Surface Realisation
Recent neural language generation systems often \textit{hallucinate} contents (i. e., producing irrelevant or contradicted facts), especially when trained on loosely corresponding pairs of the input structure and text.
Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding
Recent efforts on this issue suggest training a neighborhood aggregator in conjunction with the conventional entity and relation embeddings, which may help embed new entities inductively via their existing neighbors.
Aggregated Semantic Matching for Short Text Entity Linking
The task of entity linking aims to identify concepts mentioned in a text fragments and link them to a reference knowledge base.
Operation-guided Neural Networks for High Fidelity Data-To-Text Generation
Even though the generated texts are mostly fluent and informative, they often generate descriptions that are not consistent with the input structured data.
Incorporating GAN for Negative Sampling in Knowledge Representation Learning
In this GAN-based framework, we take advantage of a generator to obtain high-quality negative samples.
Operations Guided Neural Networks for High Fidelity Data-To-Text Generation
Even though the generated texts are mostly fluent and informative, they often generate descriptions that are not consistent with the input structured data.
Incorporating Consistency Verification into Neural Data-to-Document Generation
Recent neural models for data-to-document generation have achieved remarkable progress in producing fluent and informative texts.
Chinese/English mixed Character Segmentation as Semantic Segmentation
We take advantage of the successful architecture called fully convolutional networks (FCN) in the field of semantic segmentation.
Tag-Weighted Topic Model For Large-scale Semi-Structured Documents
We propose a novel method to model the SSDs by a so-called Tag-Weighted Topic Model (TWTM).
