research
∙
06/29/2023
A Hybrid System for Systematic Generalization in Simple Arithmetic Problems
Solving symbolic reasoning problems that require compositionality and sy...
share
research
∙
05/19/2023
RGCVAE: Relational Graph Conditioned Variational Autoencoder for Molecule Design
Identifying molecules that exhibit some pre-specified properties is a di...
research
∙
05/18/2023
Weakly-Supervised Visual-Textual Grounding with Semantic Prior Refinement
Using only image-sentence pairs, weakly-supervised visual-textual ground...
research
∙
08/11/2021
A Better Loss for Visual-Textual Grounding
Given a textual phrase and an image, the visual grounding problem is def...
research
∙
06/10/2021
Simple Graph Convolutional Networks
Many neural networks for graphs are based on the graph convolution opera...
research
∙
04/19/2021
Conditional Variational Capsule Network for Open Set Recognition
In open set recognition, a classifier has to detect unknown classes that...
research
∙
11/05/2020
Short-Term Memory Optimization in Recurrent Neural Networks by Autoencoder-based Initialization
Training RNNs to learn long-term dependencies is difficult due to vanish...
research
∙
09/01/2020
Conditional Constrained Graph Variational Autoencoders for Molecule Design
In recent years, deep generative models for graphs have been used to gen...
research
∙
08/20/2020
A Systematic Assessment of Deep Learning Models for Molecule Generation
In recent years the scientific community has devoted much effort in the ...
research
∙
06/29/2020
Incremental Training of a Recurrent Neural Network Exploiting a Multi-Scale Dynamic Memory
The effectiveness of recurrent neural networks can be largely influenced...
research
∙
01/31/2020
Encoding-based Memory Modules for Recurrent Neural Networks
Learning to solve sequential tasks with recurrent models requires the ab...
research
∙
05/15/2019
Embeddings and Representation Learning for Structured Data
Performing machine learning on structured data is complicated by the fac...
research
∙
11/23/2018
On Filter Size in Graph Convolutional Networks
Recently, many researchers have been focusing on the definition of neura...
research
∙
11/16/2018
Pre-training Graph Neural Networks with Kernels
Many machine learning techniques have been proposed in the last few year...
research
∙
11/08/2018
Linear Memory Networks
Recurrent neural networks can learn complex transduction problems that r...
research
∙
11/10/2017
LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances
Predicting the completion time of business process instances would be a ...
research
∙
02/24/2016
Time and Activity Sequence Prediction of Business Process Instances
The ability to know in advance the trend of running process instances, w...
research
∙
09/22/2015
