activity
20162022
most citedNeural-Symbolic Learning and Reasoning: A Survey and Interpretation

241 citations · 442 across the 12 of their papers we have counts for

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Showing cs.LGShow all

8 papers · 1 filter

cs.LG20221 cited

Graph-based Neural Modules to Inspect Attention-based Architectures: A Position Paper

Breno W. Carvalho, Artur D'Avilla Garcez, Luis C. Lamb

Encoder-decoder architectures are prominent building blocks of state-of-the-art solutions for tasks across multiple fields where deep learning (DL) or foundation models play a key…

cs.LG202115 cited

Synthetic Data Generation for Fraud Detection using GANs

Charitos Charitou, Simo Dragicevic, Artur d'Avila Garcez

Detecting money laundering in gambling is becoming increasingly challenging for the gambling industry as consumers migrate to online channels. Whilst increasingly stringent regulat…

cs.LG20202 cited

On the Transferability of VAE Embeddings using Relational Knowledge with Semi-Supervision

Harald Strömfelt, Luke Dickens, Artur d'Avila Garcez +1

We propose a new model for relational VAE semi-supervision capable of balancing disentanglement and low complexity modelling of relations with different symbolic properties. We com…

cs.LG2020

Neural-Symbolic Relational Reasoning on Graph Models: Effective Link Inference and Computation from Knowledge Bases

Henrique Lemos, Pedro Avelar, Marcelo Prates +2

The recent developments and growing interest in neural-symbolic models has shown that hybrid approaches can offer richer models for Artificial Intelligence. The integration of effe…

cs.LG20195 cited

Making Good on LSTMs' Unfulfilled Promise

Daniel Philps, Artur d'Avila Garcez, Tillman Weyde

LSTMs promise much to financial time-series analysis, temporal and cross-sectional inference, but we find that they do not deliver in a real-world financial management task. We exa…

cs.LG20191 cited

Continual Learning Augmented Investment Decisions

Daniel Philps, Tillman Weyde, Artur d'Avila Garcez +1

Investment decisions can benefit from incorporating an accumulated knowledge of the past to drive future decision making. We introduce Continual Learning Augmentation (CLA) which i…