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Found 8 papers, 5 papers with code
LogGPT: Log Anomaly Detection via GPT
However, there is a gap between language modeling and anomaly detection as the objective of training a sequential model via a language modeling loss is not directly related to anomaly detection.
Absformer: Transformer-based Model for Unsupervised Multi-Document Abstractive Summarization
In this paper, we consider the unsupervised abstractive MDS setting where there are only documents with no groundtruh summaries provided, and we propose Absformer, a new Transformer-based method for unsupervised abstractive summary generation.
Time-Frequency Distributions of Heart Sound Signals: A Comparative Study using Convolutional Neural Networks
The findings of this study provided the knowledge for selecting TFDs as CNN input and designing CNN architecture for heart sound classification.
DAME: Domain Adaptation for Matching Entities
We study the zero-shot learning case on the target domain, and demonstrate that our method learns the EM task and transfers knowledge to the target domain.
StruBERT: Structure-aware BERT for Table Search and Matching
In this paper, we propose StruBERT, a structure-aware BERT model that fuses the textual and structural information of a data table to produce context-aware representations for both textual and tabular content of a data table.
Neural ranking models for document retrieval
A variety of deep learning models have been proposed, and each model presents a set of neural network components to extract features that are used for ranking.
Semantic Labeling Using a Deep Contextualized Language Model
Generating schema labels automatically for column values of data tables has many data science applications such as schema matching, and data discovery and linking.
Table Search Using a Deep Contextualized Language Model
Pretrained contextualized language models such as BERT have achieved impressive results on various natural language processing benchmarks.
