Organisers: Laure Daviaud and Giacomo Tarroni
Term 1 2019-20
- Week 1, 25-09-2019 (AG02): UKRI Funding procedures and opportunities, by Peter Aggar (Research & Enterprise Office) (Core members).
- Week 2, 02-10-2019 (A227): Seminar, by Ed Alonso, Esther Mondragón, Constantino Carlos Reyes-Aldasoro and Giacomo Tarroni.
- Week 3, 09-10-2019 (A227): Seminar, by Ernesto Jiménez-Ruiz, Michael Garcia Ortiz, Mark Broom, Laure Daviaud and Essy Mulwa.
- Week 4, 16-10-2019 (E214): Webpage development session, by Esther Mondragón (Core members).
- Week 5, 23-10-2019 (ELG14): Knowledge Transfer Partnerships (KTPs), by Ian Gibbs (Research & Enterprise Office) (Core members).
- Week 7, 06-11-2019 (AG11): Seminar, by Fatima Najibi, Tom Chen, Alex Ter-Sarkisov, and Atif Riaz.
- Week 8, 13-11-2019 (E214): Lecture on Deep Learning I, by Alex Ter-Sarkisov (Core members).
- Week 9, 20-11-2019 (A227): Seminar, by Kizito Salako, Sarah Scott, Johann Bauer and Nathan Olliverre.
- Week 10, 27-11-2019 (AG04): Lecture on Deep Learning II, by Alex Ter-Sarkisov (Core members).
- Week 11, 04-12-2019 (E214): EIT and other R&E opportunities, by Brigita Jurisic (Research & Enterprise Office) (Core members).
Term 2 2019-20
- Week 1, 22-01-2020 (ELG14; 16:10): CitAI funding strategy 2020 (Core members).
- Week 2, 29-01-2020 (C321; 15:00): Worktribe session, by Claudia Kalay (Research & Enterprise Office) (Core members).
- Week 3, 05-02-2020 (C300; 16:30): Seminar, by Vincenzo Cutrona (INSID&S Lab, Università di Milano-Bicocca).
Semantic Data Enrichment meets Neural-Symbolic ReasoningData enrichment is a critical task in the data preparation process of many data science projects where a data set has to be extended with additional information from different sources in order to perform insightful analyses. The most crucial pipeline step is the table reconciliation, where values in cells are mapped to objects described in the external data sources. State-of-the-art approaches for table reconciliation perform well, but they do not scale to huge datasets and they are mostly focused on a single external source (e.g., a specific Knowledge Graph). Thus, the investigation of the problem of scalable table enrichment has recently gained attention. The focus of this talk will be on an experimental approach for reconciling values in tables, which relies on the neural-symbolic reasoning paradigm and that is potentially able to both scale and adapt itself to new sources of information. Preliminary results will be discussed in the last part of the talk.
Slides (.pdf) available here
- Week 4, 12-02-2020 (ELG14; 16:30): CitAI planning events 2020 (Core members).
- Week 5, 19-02-2020 (C300; 16:30): Seminar, by Lee Harris (Computational Intelligence Group, University of Kent).
Comparing Explanations Between Random Forests And Artificial Neural NetworksThe decisions made by machines are increasingly comparable in predictive performance to those made by humans, but these decision making processes are often concealed as black boxes. Additional techniques are required to extract understanding, and one such category are explanation methods. This research compares the explanations of two popular forms of artificial intelligence; neural networks and random forests. Researchers in either field often have divided opinions on transparency, and similarity can help to encourage trust in predictive accuracy alongside transparent structure. This research explores a variety of simulated and real-world datasets that ensure fair applicability to both learning algorithms. A new heuristic explanation method that extends an existing technique is introduced, and our results show that this is somewhat similar to the other methods examined whilst also offering an alternative perspective towards least-important features.
Slides (.pdf) available here
- Week 7, 04-03-2020 (AG03; 16:30): Seminar, by Hugo Caselles-Dupré (ENSTA ParisTech and Softbank Robotics Europe; ObviousArt).
Re-defining disentanglement in Representation Learning for artificial agentsFinding a generally accepted formal definition of a disentangled representation in the context of an agent behaving in an environment is an important challenge towards the construction of data-efficient autonomous agents. The idea of disentanglement is often associated to the idea that sensory data is generated by a few explanatory factors of variation. Higgins et al. recently proposed Symmetry-Based Disentangled Representation Learning, an alternative definition of disentanglement based on a characterization of symmetries in the environment using group theory. In our latest NeurIPS paper we build on their work and make observations, theoretical and empirical, that lead us to argue that Symmetry-Based Disentangled Representation Learning cannot only be based on static observations: agents should interact with the environment to discover its symmetries.
- Week 8, 11-03-2020 (AG11; 16:30): Seminar, by Mehdi Keramati (Department of Psychology, City, University of London).
Optimal Planning under Cognitive ConstraintsWhen deciding their next move (e.g. in a chess game, or a cheese maze), a superhuman or a super-mouse would think infinitely deep into the future and consider all the possible sequences of actions and their outcomes. A terrestrial human or mouse, however, has limited time-consuming computational resources and is thus compelled to restrict its contemplation. A key theoretical question is how an agent can make the best out of her limited time and cognitive resources in order to make up her mind. I will review several strategies, some borrowed from the artificial intelligence literature, that we and others have demonstrated that animals/humans use in the face of different cognitive limitations. These strategies include: acting based on habits, limiting the planning horizon, forward/backward planning, hierarchical planning, and successor-representation learning.
- Week 9, 18-03-2020 (C103; 16:30): CitAI ARQM publications review (Core members).
- Week 10, 25-03-2020 (A227; 16:30): Seminars, joint session with the Department of Mathematics, by Alessandro Betti (SAILab, Siena Artificial Intelligence Lab, at Università di Siena).
A Variational Framework or Laws of LearningMany problems in learning naturally present themselves as a coherent stream of information which has its proper dynamics and temporal scales; one emblematic example is that of visual information. However nowadays most of the approaches to learning completely disregard, at all, or in first approximation, this property of the information on which the learning should be performed. As a result, the problem is typically formulated as a “static” optimization problem on the parameters that define a learning model. Formulating a learning theory in terms of an evolution laws instead shifts the attention to the dynamical behaviour of the learner. This gives us the opportunity, for those agents that live in streams of data to couple their dynamics with the information that flows from the environment and to incorporate into the temporal laws of learning dynamical constraints that we know will enhance the quality of learning. We will discuss how we can consistently frame learning processes using variational principles.
- Week 11, 01-04-2020 (A227; 16:30): Seminar, by Jonathan Passerat-Palmbach (ConsenSys, and BioMedIA at Imperial College London).
Convergence of Blockchain and Privacy-Enhancing Technologies fostering decentralised AIWeb3 provides us with the bricks to build decentralised AI marketplaces where data and models could be monetised. However, this stack does not provide the privacy guarantees required to engage the actors of this decentralised AI economy. Once a data or a model has been exposed in plaintext, any mechanism controlling access to this piece of information becomes irrelevant since it cannot guarantee that the data has not leaked. In this talk, we'll explore the state-of-the-art in Secure/Blind Computing that will guarantee the privacy of data or models and enable a decentralised AI vision. Typically, we will describe an Ethereum orchestrated architecture for a technique known as Federated Learning that enables training AI models on sensitive data while respecting their owners' privacy.