16 citations · 72 across the 18 of their papers we have counts for
18 papers
Poisoning Knowledge Graph Embeddings via Relation Inference Patterns
Peru Bhardwaj, John Kelleher, Luca Costabello +1
We study the problem of generating data poisoning attacks against Knowledge Graph Embedding (KGE) models for the task of link prediction in knowledge graphs. To poison KGE models,…
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…
Language-Driven Region Pointer Advancement for Controllable Image Captioning
Annika Lindh, Robert J. Ross, John D. Kelleher
Controllable Image Captioning is a recent sub-field in the multi-modal task of Image Captioning wherein constraints are placed on which regions in an image should be described in t…
Semantic Relatedness and Taxonomic Word Embeddings
Magdalena Kacmajor, John D. Kelleher, Filip Klubicka +1
This paper connects a series of papers dealing with taxonomic word embeddings. It begins by noting that there are different types of semantic relatedness and that different lexical…
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…
Referring to the recently seen: reference and perceptual memory in situated dialog
John D. Kelleher, Simon Dobnik
From theoretical linguistic and cognitive perspectives, situated dialog systems are interesting as they provide ideal test-beds for investigating the interaction between language a…
Generating Diverse and Meaningful Captions
Annika Lindh, Robert J. Ross, Abhijit Mahalunkar +2
Image Captioning is a task that requires models to acquire a multi-modal understanding of the world and to express this understanding in natural language text. While the state-of-t…
TEST: A Terminology Extraction System for Technology Related Terms
Murhaf Hossari, Soumyabrata Dev, John D. Kelleher
Tracking developments in the highly dynamic data-technology landscape are vital to keeping up with novel technologies and tools, in the various areas of Artificial Intelligence (AI…
Exploring the Use of Attention within an Neural Machine Translation Decoder States to Translate Idioms
Giancarlo D. Salton, Robert J. Ross, John D. Kelleher
Idioms pose problems to almost all Machine Translation systems. This type of language is very frequent in day-to-day language use and cannot be simply ignored. The recent interest…
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…
On the Inability of Markov Models to Capture Criticality in Human Mobility
Vaibhav Kulkarni, Abhijit Mahalunkar, Benoit Garbinato +1
We examine the non-Markovian nature of human mobility by exposing the inability of Markov models to capture criticality in human mobility. In particular, the assumed Markovian natu…
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…
Is it worth it? Budget-related evaluation metrics for model selection
Filip Klubička, Giancarlo D. Salton, John D. Kelleher
Creating a linguistic resource is often done by using a machine learning model that filters the content that goes through to a human annotator, before going into the final resource…
Modular Mechanistic Networks: On Bridging Mechanistic and Phenomenological Models with Deep Neural Networks in Natural Language Processing
Simon Dobnik, John D. Kelleher
Natural language processing (NLP) can be done using either top-down (theory driven) and bottom-up (data driven) approaches, which we call mechanistic and phenomenological respectiv…
Beef Cattle Instance Segmentation Using Fully Convolutional Neural Network
Aram Ter-Sarkisov, Robert Ross, John Kelleher +2
We present an instance segmentation algorithm trained and applied to a CCTV recording of beef cattle during a winter finishing period. A fully convolutional network was transformed…
Bootstrapping Labelled Dataset Construction for Cow Tracking and Behavior Analysis
Aram Ter-Sarkisov, Robert Ross, John Kelleher
This paper introduces a new approach to the long-term tracking of an object in a challenging environment. The object is a cow and the environment is an enclosure in a cowshed. Some…