16 citations · 20 across the 7 of their papers we have counts for
6 papers · 1 filter
Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods
Peru Bhardwaj, John Kelleher, Luca Costabello +1
Despite the widespread use of Knowledge Graph Embeddings (KGE), little is known about the security vulnerabilities that might disrupt their intended behaviour. We study data poison…
Mutual Information Decay Curves and Hyper-Parameter Grid Search Design for Recurrent Neural Architectures
Abhijit Mahalunkar, John D. Kelleher
We present an approach to design the grid searches for hyper-parameter optimization for recurrent neural architectures. The basis for this approach is the use of mutual information…
Multi-Element Long Distance Dependencies: Using SPk Languages to Explore the Characteristics of Long-Distance Dependencies
Abhijit Mahalunkar, John D. Kelleher
In order to successfully model Long Distance Dependencies (LDDs) it is necessary to understand the full-range of the characteristics of the LDDs exhibited in a target dataset. In t…
Persistence pays off: Paying Attention to What the LSTM Gating Mechanism Persists
Giancarlo D. Salton, John D. Kelleher
Language Models (LMs) are important components in several Natural Language Processing systems. Recurrent Neural Network LMs composed of LSTM units, especially those augmented with…
Understanding Recurrent Neural Architectures by Analyzing and Synthesizing Long Distance Dependencies in Benchmark Sequential Datasets
Abhijit Mahalunkar, John D. Kelleher
In order to build efficient deep recurrent neural architectures, it is essential to analyze the complexityof long distance dependencies (LDDs) of the dataset being modeled. In this…
What is not where: the challenge of integrating spatial representations into deep learning architectures
John D. Kelleher, Simon Dobnik
This paper examines to what degree current deep learning architectures for image caption generation capture spatial language. On the basis of the evaluation of examples of generate…