Research Centre for Machine Learning meeting on Explainable AI When: Fri, 29 November 2019, 4:00pm Where: AG01, College Building SHAP is an increasingly popular method for providing local explanations of AI system predictions. SHAP is based on the game-theory concept of Shapley Values. Shapley Values are the unique solution for fairly attributing the benefits of a cooperative game between players, when subject to a set of local accuracy and consistency constraints (an excellent introduction to Shapley Values is provided at https://www.youtube.com/watch?v=qcLZMYPdpH4&t=437s) We will be disc ..read more

Data Bites seminar When: Mon, 11 November 2019, 5:00pm Where: A130, College Building Who: Kevin Ryan; City, University of London Title: Deep Learning and Computer Vision in the Property Market – Making the ‘Right’ Move Abstract: Rightmove is the UK’s largest online real estate portal. The company was started in 2000 by the top four corporate estate agents Countrywide, Connells, Halifax and Royal and Sun Alliance. In 2006 it was floated on the London Stock Exchange and today its boasts a revenue of £267m with an operating profit of £198.6m. Rightmove offers an Automated Valuation Model (AV ..read more

Department of Psychology seminar When: Wed, 23 October 2019, 1:00pm Where: D427, Rhind Building Who: Bert Kappen; Donder Institute, Radboud University Nijmegen (Netherlands) Title: Path Integral Control Theory Abstract: Stochastic optimal control theory deals with the problem of computing an optimal set of actions to attain some future goal. Examples are found in many contexts such as motor control tasks for robotics, planning and scheduling tasks or managing a financial portfolio. The computation of the optimal control is typically very difficult due to the size of the state space and th ..read more

MPhil-PhD transfer presentation When: Wed, 16th Oct 2019, 12.00 noon Where: A108 (1st Floor, College Building) Who: Benedikt Wagner; City, University of London Title: Reasoning about what has been learned: Knowledge Extraction from Neural Networks Abstract: Machine Learning-based systems, including Neural Networks, are experiencing greater popularity in recent years. A weakness of these model that rely on complex representations is that they are considered black boxes with respect to explanatory power. In the context of current initiatives on the side of the regulatory authorities an ..read more

Machine Learning seminar When: Wed, 07 August 2019, 3:00pm Where: AG22, College Building Who: Alessandro Daniele; Fondazione Bruno Kessler (Trento, Italy) Title: Knowledge Enhanced Neural Networks Abstract: We propose Knowledge Enhanced Neural Networks (KENN), an architecture for injecting prior knowledge, codified by a set of logical clauses, into a neural network. In KENN clauses are directly incorporated in the structure of the neural network as a new layer that includes a set of additional learnable parameters, called clause weights. As a consequence, KENN can learn the level of satis ..read more

Machine Learning seminar When: Wed, 19 June 2019, 2:00pm Where: A225, College Building Who: Adam White; City, University of London. Title: Measurable Counterfactual Explanations for Any Classifier Abstract: The predictions of machine learning systems need to be explainable to the individuals they affect. Yet the inner workings of many machine learning systems seem unavoidably opaque. In this talk we will introduce a new system Counterfactual Local Explanations viA Regression (CLEAR). CLEAR is based on the view that a satisfactory explanation of a prediction needs to both explain the value ..read more

Machine Learning seminar When: Tue, 28 May 2019, 3:30pm Where: AG07b, College Building Who: Marco Gori, University of Siena, Italy. Title: The Principle of Least Cognitive Action Abstract: In this talk we introduce the principle of Least Cognitive Action with the purpose of understanding perceptual learning processes. The principle closely parallels related approaches in physics, and suggests to regard neural networks as systems whose weights are Lagrangian variables, namely functions depending on time. Interestingly, neural networks “conquer their own life” and there is no neat distincti ..read more

Machine Learning seminar When: Fri, 17 May 2019, 2pm Where: AG03, College Building Who: Wang-Zhou Dai, Imperial College London. Title: Bridging Machine Learning and Logical Reasoning by Abductive Learning Abstract: Perception and reasoning are two representative abilities of intelligence that are integrated seamlessly during problem-solving processes. In the area of artificial intelligence (AI), perception is usually realised by machine learning and reasoning is often formalised by logic programming. However, the two categories of techniques were developed separately throughout most of th ..read more

Machine Learning seminar When: Wed, 3 Apr 2019, 2pm Where: A226, College Building Who: Derek Doran,Wright State University. Title: Mappers and Manifolds Matter! Abstract: Topological Data Analysis (TDA) is a branch of data science that estimates and then exploits the “shape” of a dataset for downstream characterization and inference. TDA methods arerising in popularity in the ML community as a tool to theoretically understand the actions of deep neural nets and other algorithms by connections to the Manifold Hypothesis. TDA methods, and in ..read more

Machine Learning seminar When: Wed, 13 Mar 2019, 2pm Where: A226, College Building Who: Robin Manhaeve, Katholieke Universiteit Leuven, Belgium. Title: DeepProbLog: Neural Probabilistic Logic Programming Abstract: We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques can be adapted for the new language. Our experiments demonstrate that DeepProbLog supports both symbolic and subsymbolic representations and inference, 1) program induction, 2) probabilisti ..read more