IEEE

Extractive Document Summarization with Graph Neural Networks and Topic Modeling in PyTorch

Abstract: Algorithmic text summarization task in natural language processing aims to represent a given text in a shorter and suitable form for a human reader by locating sentences of interest while ...
IEEE

PyTorch Ecosystem for Graph Neural Networks: A Comprehensive Review

Abstract: Deep learning has witnessed rapid progress through frameworks such as PyTorch, which has become the dominant choice for researchers and practitioners due to its dynamic computation, ...
GitHub

Multi-View Conditional Information Bottleneck

Multi-View Conditional Information Bottleneck (MVCIB) is a novel architecture for pre-training Graph Neural Networks on 2D and 3D molecular structures and developed by NS Lab, CUK based on pure ...
Hosted on MSN

Network in Network (NiN) Explained – Deep Neural Network Tutorial with PyTorch

Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...
Frontiers

Inferred global dense residue transition graphs from primary structure sequences enable protein interaction prediction via directed graph convolutional neural networks

Accurate prediction of protein-protein interactions (PPIs) is crucial for understanding cellular functions and advancing the development of drugs. While existing in-silico methods leverage direct ...
Scientific Research Publishing

Lin, J., Zhao, Y., Huang, W., Liu, C. and Pu, H. (2020) Domain Knowledge Graph-Based Research Progress of Knowledge Representation. Neural Computing and Applications, 33, 681-690.

ABSTRACT: Knowledge Graph (KG) and neural network (NN) based Question-answering (QA) systems have evolved into the realm of intelligent information retrieval as they have been able to reach a high ...