#Kmer_Ai : Our next Live Event comming up on Table Representation Learning using Heterogeneous Graph Embedding: https://www.youtube.com/wa...
Date: Sartuday 27 July 2024, Time: 19h (GMT+1)
Host: KmerAI represented by Tassé Geraud
Guest: Willy Carlos Tchuitcheu, PhD student
Event Details:
About the Guest:
Willy Carlos Tchuitcheu is a PhD student at Vrije Universiteit Brussel, working in the Mathematics and Data Science lab under the supervision of Dr. Tan Lu and Professor Ann Dooms. His research areas include: Graph ML, NLP and Joint Visual Language modeling.
Topic: Table Representation Learning using Heterogeneous Graph Embedding
Willy will discuss the challenges of extracting semantic information from tables with complex layouts and will introduce a new framework called Graph-based Table Representation Learning (GTRL). GTRL combines graph-based visual modeling with sequence-based language modeling to learn detailed per-cell embeddings that capture the semantic meaning within the table context. Empirical evaluations on two datasets will show that GTRL achieves competitive performance whilst requiring reduced computational complexity compared to existing table representation models.
Don't miss this insightful discussion!
Checkout the paper: https://www.sciencedirect....
Date: Sartuday 27 July 2024, Time: 19h (GMT+1)
Host: KmerAI represented by Tassé Geraud
Guest: Willy Carlos Tchuitcheu, PhD student
Event Details:
About the Guest:
Willy Carlos Tchuitcheu is a PhD student at Vrije Universiteit Brussel, working in the Mathematics and Data Science lab under the supervision of Dr. Tan Lu and Professor Ann Dooms. His research areas include: Graph ML, NLP and Joint Visual Language modeling.
Topic: Table Representation Learning using Heterogeneous Graph Embedding
Willy will discuss the challenges of extracting semantic information from tables with complex layouts and will introduce a new framework called Graph-based Table Representation Learning (GTRL). GTRL combines graph-based visual modeling with sequence-based language modeling to learn detailed per-cell embeddings that capture the semantic meaning within the table context. Empirical evaluations on two datasets will show that GTRL achieves competitive performance whilst requiring reduced computational complexity compared to existing table representation models.
Don't miss this insightful discussion!
Checkout the paper: https://www.sciencedirect....
5 mois depuis