18 results on '"Kelleher, John"'
Search Results
2. Data: the Good, the Bad and the Ethical
- Author
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Kelleher, John D., Pinto, Filipe Cabral, Cortesao, Luis M., and SFI ADAPT Research Centre
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Machine Learning ,Deep Learning ,Artificial Intelligence and Robotics ,Databases and Information Systems ,Ethics and Political Philosophy ,Artificial Intelligence ,Computer Sciences ,Data Privacy ,Information Security ,Data Science ,Data Ethics ,Applied Ethics - Abstract
It is often the case with new technologies that it is very hard to predict their long-term impacts and as a result, although new technology may be beneficial in the short term, it can still cause problems in the longer term. This is what happened with oil by-products in different areas: the use of plastic as a disposable material did not take into account the hundreds of years necessary for its decomposition and its related long-term environmental damage. Data is said to be the new oil. The message to be conveyed is associated with its intrinsic value. But as in the case of real oil, we should take care to ensure that its use does not create harm in the future. We know from recent history that data can be used in harmful ways, but data also has enormous positive potential when applied to the service of communities. In this article, we highlight the opportunities, problems and best practice of using data.
- Published
- 2020
3. Mutual Information Decay Curves and Hyper-parameter Grid Search Design for Recurrent Neural Architectures
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Mahalunkar, Abhijit and Kelleher, John
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Hyper-Parameter Tuning ,Artificial Intelligence and Robotics ,Long Distance Dependencies ,Vanishing Gradients ,Data Science ,Recurrent Neural Architectures - Abstract
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 to analyze long distance dependencies (LDDs) within a dataset. We also report a set of experiments that demonstrate how using this approach, we obtain state-of-the-art results for DilatedRNNs across a range of benchmark datasets.
- Published
- 2020
4. A U-Net Deep Learning Framework for High Performance Vessel Segmentation in Paitents with Cerebrovascular Disease
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Livne, Michelle, Rieger, Jana, Aydin, Orhun Utku, Taha, Abdel Aziz, Akay, Ela Maria, Kossen, Tabea, Sobesky, Jan, Kelleher, John D., Hildebrand, Kristian, Frey, Dietmar, and Madai, Vince I
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Artificial Intelligence and Robotics ,Computer Sciences ,segmentation ,medical imaging ,Neurosciences ,deep learning ,U-net ,cerebrovascular disease - Abstract
Brain vessel status is a promising biomarker for better prevention and treatment in cerebrovascular disease. However, classic rule-based vessel segmentation algorithms need to be hand-crafted and are insufficiently validated. A specialized deep learning method—the U-net—is a promising alternative. Using labeled data from 66 patients with cerebrovascular disease, the U-net framework was optimized and evaluated with three metrics: Dice coefficient, 95% Hausdorff distance (95HD) and average Hausdorff distance (AVD). The model performance was compared with the traditional segmentation method of graph-cuts. Training and reconstruction was performed using 2D patches. A full and a reduced architecture with less parameters were trained. We performed both quantitative and qualitative analyses. The U-net models yielded high performance for both the full and the reduced architecture: A Dice value of ~0.88, a 95HD of ~47 voxels and an AVD of ~0.4 voxels. The visual analysis revealed excellent performance in large vessels and sufficient performance in small vessels. Pathologies like cortical laminar necrosis and a rete mirabile led to limited segmentation performance in few patients. The U-net outperfomed the traditional graph-cuts method (Dice ~0.76, 95HD ~59, AVD ~1.97). Our work highly encourages the development of clinically applicable segmentation tools based on deep learning. Future works should focus on improved segmentation of small vessels and methodologies to deal with specific pathologies
- Published
- 2019
5. Synthetic, Yet Natural: Properties of WordNet Random Walk Corpora and the impact of rare words on embedding performance
- Author
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Klubicka, Filip, Maldonado, Alfredo, Mahalunkar, Abhijit, Kelleher, John D., ADAPT Centre for Digital Content Technology, and SFI Research Centres Pro-gramme
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word embeddings ,evaluation ,Artificial Intelligence and Robotics ,WordNet ,InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL ,representations ,Software Engineering ,corpus ,ComputingMethodologies_ARTIFICIALINTELLIGENCE ,random walk ,Computational Linguistics ,taxonomy ,word similarity ,Numerical Analysis and Scientific Computing - Abstract
Creating word embeddings that reflect semantic relationships encoded in lexical knowledge resources is an open challenge. One approach is to use a random walk over a knowledge graph to generate a pseudo-corpus and use this corpus to train embeddings. However, the effect of the shape of the knowledge graph on the generated pseudo-corpora, and on the resulting word embeddings, has not been studied. To explore this, we use English WordNet, constrained to the taxonomic (tree-like) portion of the graph, as a case study. We investigate the properties of the generated pseudo-corpora, and their impact on the resulting embeddings. We find that the distributions in the psuedo-corpora exhibit properties found in natural corpora, such as Zipf’s and Heaps’ law, and also ob- serve that the proportion of rare words in a pseudo-corpus affects the performance of its embeddings on word similarity.
- Published
- 2019
- Full Text
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6. Investigating Variable Dependencies in Dialogue States
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Trinh, Anh Duong, Ross, Robert J., Kelleher, John D., Science Foundation Ireland, European Regional Development Fund (ERDF), and SFI Research Centres Programme
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dialogue state ,Artificial Intelligence and Robotics ,spoken dialogue system ,dialogue state tracking ,variable dependencies - Abstract
Dialogue State Tracking is arguably one of the most challenging tasks among dialogue processing problems due to the uncertainties of language and complexity of dialogue contexts. We argue that this problem is made more challenging by variable dependencies in the dialogue states that must be accounted for in processing. In this paper we give details on our motivation for this argument through statistical tests on a number of dialogue datasets. We also propose a machine learning-based approach called energy-based learning that tackles variable dependencies while performing prediction on the dialogue state tracking tasks.
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- 2019
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7. A Multi-Task Approach to Incremental Dialogue State Tracking
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Trinh, Anh Duong, Ross, Robert J., Kelleher, John D., ADAPT Centre, Science Foundation Ireland, and SFI Research Centres Programme
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Spoken dialogue system ,Deep Learning ,Artificial Intelligence and Robotics ,Theory and Algorithms ,Computational Engineering ,Dialogue state tracking ,Multi-Task Learning ,Numerical Analysis and Scientific Computing ,Other Computer Sciences ,Incrementality phenomenom - Abstract
Incrementality is a fundamental feature of language in real world use. To this point, however, the vast majority of work in automated dialogue processing has focused on language as turn based. In this paper we explore the challenge of incremental dialogue state tracking through the development and analysis of a multi-task approach to incremental dialogue state tracking. We present the design of our incremental dialogue state tracker in detail and provide evaluation against the well known Dialogue State Tracking Challenge 2 (DSTC2) dataset. In addition to a standard evaluation of the tracker, we also provide an analysis of the Incrementality phenomenon in our model’s performance by analyzing how early our models can produce correct predictions and how stable those predictions are. We find that the Multi-Task Learning-based model achieves state-of-the-art results for incremental processing.
- Published
- 2018
8. On the Inability of Markov Models to Capture Criticality in Human Mobility
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Kulkarni, Vaibhav, Mahalunkar, Abhijit, Garbinato, Benoit, and Kelleher, John D.
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Criticality ,FOS: Computer and information sciences ,Physics - Physics and Society ,Artificial Intelligence and Robotics ,Computer Science - Information Theory ,Information Theory (cs.IT) ,Data Science ,FOS: Physical sciences ,Physics and Society (physics.soc-ph) ,Human-mobility ,Predictability limits - Abstract
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 nature of mobility was used to establish an upper bound on the predictability of human mobility, based on the temporal entropy. Since its inception, this bound has been widely used for validating the performance of mobility prediction models. We show that the variants of recurrent neural network architectures can achieve significantly higher prediction accuracy surpassing this upper bound. The central objective of our work is to show that human-mobility dynamics exhibit criticality characteristics which contributes to this discrepancy. In order to explain this anomaly, we shed light on the underlying assumption that human mobility characteristics follow an exponential decay that has resulted in this bias. By evaluating the predictability on real-world datasets, we show that human mobility exhibits scale-invariant long-distance dependencies, bearing resemblance to power-law decay, contrasting with the initial Markovian assumption. We experimentally validate that this assumption inflates the estimated mobility entropy, consequently lowering the upper bound on predictability. We demonstrate that the existing approach of entropy computation tends to overlook the presence of long-distance dependencies and structural correlations in human mobility. We justify why recurrent-neural network architectures that are designed to handle long-distance dependencies surpass the previously computed upper bound on mobility predictability.
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- 2018
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9. On the Exactitude of Big Data: La Bêtise and Artificial Intelligence
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Fitzpatrick, Noel, Kelleher, John D, MSCA, and RISE
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Big Data ,Philosophy ,Artificial Intelligence and Robotics ,Bêtise ,Data Science ,artificial intelligence - Abstract
This article revisits the question of ‘la bêtise’ or stupidity in the era of Artificial Intelligence driven by Big Data, it extends on the questions posed by Gille Deleuze and more recently by Bernard Stiegler. However, the framework for revisiting the question of la bêtise will be through the lens of contemporary computer science, in particular the development of data science as a mode of analysis, sometimes, misinterpreted as a mode of intelligence. In particular, this article will argue that with the advent of forms of hype (sometimes referred to as the hype cycle) in relation to big data and modalities of data analytics there is a form of computational stupidity or functional stupidity at work. The exaggerated promises of big data to solve everything are overblown expectations which will lead ultimately to a form of disillusionment with data science. This can be seen in a number of domains, for example smart city technologies, the internet of things, and machine translation. In addition to the negative effects of exaggerated claims of Big Data is the possibility that societal norms will facilitate Big Data technological change by incorporating the bêtise of Big Data, thus leading to a change in our relationship to technology, examples of this would be privacy standards and ownership of data. This paper will conclude by setting out the analysis some of the limitations of Artificial intelligence and Big Data in order to allow a re-examination of the claims made.
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- 2018
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10. Reformulation Strategies of Repeated References in the Context of Robot Perception Errors in Situated Dialogue
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Schütte, Niels, Kelleher, John D., Mac Namee, Brian, and John D. Kelleher
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robotics ,reference reformulation ,Artificial Intelligence and Robotics ,spatial language ,reference resolution ,situated dialogue - Abstract
We performed an experiment in which human participants interacted through a natural language dialogue interface with a simulated robot to fulfil a series of object manipulation tasks. We introduced errors into the robot’s perception, and observed the resulting problems in the dialogues and their resolutions. We then introduced different methods for the user to request information about the robot’s understanding of the environment. In this work, we describe the effects that the robot’s perceptual errors and the information request options available to the participant had on the reformulation of the referring expressions the participants used when resolving a unsuccessful reference.
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- 2015
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11. Automatic Annotation of Referring Expression in Situated Dialogues
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Schütte, Niels, Kelleher, John D., and Mac Namee, Brian
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Artificial Intelligence and Robotics ,reference resolution ,situated dialogue - Abstract
To apply machine learning techniques to the production and interpretation of natural language, we need large amounts of annotated language data. Manual annotation, however, is an expensive and time consuming process since it involves human annotators looking at the data and explicitly adding information that is implicitly contained in the data, based on their judgment. This work presents an approach to automatically annotating referring expressions in situated dialogues by exploiting the interpretation of language by the participants in the dia- logue. We associate instructions concerning objects in the environment with automatically detected events involving these objects and predict the referents of referring expressions in the instructions on the basis of the objects aected by the events. We judge the reliability of these pre- dictions based on the temporal and textual distance between instruction and event. We apply our approach to an annotated corpus and evalu- ate the results against human annotation. The evaluation shows that the approach can be used to accurately annotate a large proportion of the utterances in the corpus dialogues and highlight those utterances for which human annotation is required, thus reducing the amount of human annotation required.
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- 2011
12. Proceedings of the Sixth International Natural Language Generation Conference (INLG 2010)
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Kelleher, John D., Mac Namee, Brian, and van der Sluis, Ielka
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Computational Linguistics ,Natural Language Generation ,Artificial Intelligence and Robotics ,Cognition and Perception ,Psycholinguistics and Neurolinguistics ,Semantics and Pragmatics ,Syntax ,Discourse and Text Linguistics - Published
- 2010
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13. Stepping Off the Stage
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Mac Namee, Brian and Kelleher, John D.
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Artificial Intelligence and Robotics ,Graphics and Human Computer Interfaces ,augmented reality ,intelligent virtual agents - Abstract
Mixed-reality virtual agents are an attractive solution to the problems associated with human-robot interaction, allowing all the expressiveness of virtual characters to be married with the advantages of a physical artifact which exists in a shared environment with the user. However, common approaches to achieving this restrict the virtual characters appearing on top of, or encompassing the robot. This paper describes the Stepping Off the Stage system in which mixed-reality agents are allowed to step off the robot stage and move to other parts of the environment, offering compelling new interaction possibilities.
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- 2009
14. Referring Expression Generation Challenge 2008 DIT System Descriptions (DIT-FBI, DIT-TVAS, DIT-CBSR, DIT-RBR, DIT-FBI-CBSR, DIT-TVAS-RBR)
- Author
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Kelleher, John D. and Mac Namee, Brian
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Referring Expression Generation ,Computational Linguistics ,Natural Language Generation ,Artificial Intelligence and Robotics - Abstract
This papers desibes a set of systems developed at DIT for the Referring Expression Generation challenage at INLG 2008.In Proceedings of the 5th International Natural Language Generation Conference (INLG-08)
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- 2008
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15. Medical Language Processing for Patient Diagnosis Using Text Classification and Negation Labelling
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Mac Namee, Brian, Kelleher, John, and Delany, Sarah Jane
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Artificial Intelligence and Robotics ,Databases and Information Systems ,ComputingMilieux_PERSONALCOMPUTING ,bioinformatics ,natural language processing - Abstract
This paper describes the approach of the DIT AIGroup to the i2b2 Obesity Challenge to build a system to diagnose obesity and related co-morbidities from narrative, unstructured patient records. Based on experimental results a system was developed which used knowledge-light text classification using decision trees, and negation labelling.
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- 2008
16. Frequency Based Incremental Attribute Selection for GRE
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Kelleher, John D.
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Referring Expression Generation ,Computational Linguistics ,Natural Language Generation ,Artificial Intelligence and Robotics - Abstract
The DIT system uses an incremental greedy search to generate descriptions, similar to the incremental algorithm described in (Dale and Reiter, 1995). The selection of the next attribute to be tested for inclusion in the description is ordered by the absolute frequency of each attribute in the training corpus. Attributes are selected in descending order of frequency (i.e. the attribute that occurred most frequently in the training corpus is selected first). Where two or more attributes have the same frequency of occurrence the first attribute found with that frequency is selected. The type attribute is always included in the description. Other attributes are included in the description if they exclude at least 1 distractor from the set of distractors that fulfil the description generated prior that attribute’s selection.The algorithm terminates when a distinguishing description has been generated (i.e., all the distractors have been excluded) or when all the target’s attributes have been tested for inclusion in the description.
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- 2007
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17. Proceedings of the 4th ACL-SIGSEM Workshop on Prepositions at ACL-2007
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Costello, Fintan, Kelleher, John D., and Volk, Martin
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Computational Linguistics ,surgical procedures, operative ,Artificial Intelligence and Robotics ,Psycholinguistics and Neurolinguistics ,Semantics and Pragmatics ,musculoskeletal, neural, and ocular physiology ,education ,Computation ,Prepositions ,musculoskeletal system ,human activities ,Semantics ,Discourse and Text Linguistics - Abstract
This volume contains the papers presented at the Fourth ACL-SIGSEM Workshop on Prepositions. This workshop is endorsed by the ACL Special Interest Group on Semantics (ACL-SIGSEM), and is hosted in conjunction with ACL 2007, taking place on 28th June, 2007 in Prague, the Czech Republic.
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- 2007
18. A Context-Dependent Model of Proximity in Physically Situated Environments
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Kelleher, John and Kruijff, Geert-Jan M.
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Artificial Intelligence and Robotics ,Cognition and Perception ,Artificial Intelligence ,Computer Sciences ,Spatial Cognition ,Spatial Language ,Human-Robot Interaction - Abstract
The paper presents a computational model for a context-dependent analysis of a physical environment in terms of spatial proximity. The model provides a basis for grounding linguistic analyses of spatial expressions in visual perception. The model uses potential fields to model spatial proximity. It has been implemented, and when combined with a handcrafted grammar, is used to enable a conversational robot to carry out a situated dialogue with a human. The key concept in our approach is defining the region that is proximal to a landmark based on the spatial configuration of other objects in the scene. The model extends existing approaches to proximity by including object salience (visual, discourse) and interference effects between multiple objects that could act as landmarks. Theoretically, the model can help motivate the choice between topological and projective prepositions, and provides a basis for defining regions with vague spatial extent.
- Published
- 2005
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