Machine learning interpretability, explainability and trustability
Wednesday 11 Mar 2020
Time: 18:00 - 20:30
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Knowledge representation
Projects
Narrative Understanding with Large Language Models
Explore how Large Language Models (LLMs) are reshaping our comprehension of long narratives.
Neural-Symbolic AI for Digital Twins
Integrating symbolic reasoning with cutting-edge deep learning for trustworthy, interpretable, and explainable next-generation digital twin models.
Understanding the capabilities of foundation models
Evaluating the capabilities of foundation models with a focus on common sense reasoning, theory of mind, and human values.
Network based recommender systems for public procurement
A novel methodology for community detection and link prediction with applications in public procurement
Risk and uncertainty in peatland carbon emissions
Assessing and managing the risk of abrupt greenhouse gas emissions from peatlands
Publications and software
AI Assistants: A Framework for Semi-Automated Data Wrangling
Data wrangling tasks such as obtaining and linking data from various sources, transforming data...
Petricek, Tomas; van den Burg, Gerrit J. J.; Nazábal, Alfredo; Ceritli, Taha; Jiménez-Ruiz, Ernesto; Williams, Christopher K. I. "AI Assistants: A Framework for Semi-Automated Data Wrangling". IEEE Transactions on Knowledge and Data Engineering 2022
Predicting Infectiousness for Proactive Contact Tracing
The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries...
Bengio, Yoshua, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Muller et al. "Predicting Infectiousness for Proactive Contact Tracing." ICLR 2020.
Deriving Equations from Sensor Data Using Dimensional Function Synthesis
We present a new method for deriving functions that model the relationship between multiple...
Youchao Wang, Sam Willis, Vasileios Tsoutsouras, and Phillip Stanley-Marbell. 2019. Deriving Equations from Sensor Data Using Dimensional Function Synthesis. ACM Trans. Embed. Comput. Syst. 18, 5s, Article 84 (October 2019), 22 pages. DOI:https://doi.org/10.1145/3358218
A Deep Learning Framework for Pricing Financial Instruments
We propose an integrated deep learning architecture for the stock movement prediction. Our architecture...
Wu, Qiong; Zhang, Zheng; Pizzoferrato, Andrea; Cucuringu, Mihai; Liu, Zhenming. "A Deep Learning Framework for Pricing Financial Instruments", arXiv:1909.04497 (2019)
Knowledge Graph Embedding for Ecotoxicological Effect Prediction
Exploring the effects a chemical compound has on a species takes a considerable experimental...
Erik B. Myklebust, Ernesto Jiménez-Ruiz, Jiaoyan Chen, Raoul Wolf, Knut Erik Tollefsen. Knowledge Graph Embedding for Ecotoxicological Effect Prediction. International Semantic Web Conference (ISWC-19).
Canonicalizing Knowledge Base Literals
Ontology-based knowledge bases (KBs) like DBpedia are very valuable resources, but their usefulness and...
Jiaoyan Chen, Ernesto Jimenez-Ruiz and Ian Horrocks. Canonicalizing Knowledge Base Literals. International Semantic Web Conference (ISWC-19).
Ontology mapping for semantically enabled applications
In this review, we provide a summary of recent progress in ontology mapping (OM)...
Ian Harrow, Rama Balakrishnan, Ernesto Jimenez-Ruiz, Simon Jupp, Jane Lomax, Jane Reed, Martin Romacker, Christian Senger, Andrea Splendiani, Jabe Wilson, Peter Woollard, Ontology mapping for semantically enabled applications, Drug Discovery Today, 2019, ISSN 1359-6446,
Learning Semantic Annotations for Tabular Data
The usefulness of tabular data such as web tables critically depends on understanding their...
Jiaoyan Chen, Ernesto Jimenez-Ruiz, Ian Horrocks and Charles Sutton (2019). Learning Semantic Annotations for Tabular Data. Accepted for publication in the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019).
ColNet: Embedding the Semantics of Web Tables for Column Type Prediction
Automatically annotating column types with knowledge base (KB) concepts is a critical task to...
Jiaoyan Chen, Ernesto Jimenez-Ruiz, Ian Horrocks and Charles Sutton (2019). ColNet: Embedding the Semantics of Web Tables for Column Type Prediction. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19).
Articles
Events
Machine learning interpretability, explainability and trustability
Wednesday 11 Mar 2020
Time: 18:00 - 20:30
Interest groups
People
Yuhan Wang
Enrichment Student
Wei Nie
Enrichment Student
Trisevgeni Papakonstantinou
Enrichment Student
Shubhr Singh
Enrichment Student
Sanjay Budhdeo
Enrichment Student
Mateo Espinosa Zarlenga
Enrichment Student
Marek Strong
Enrichment Student
Madeleine Waller
Enrichment Student