When: Wed, 20 Feb 2019, 2pm
Where: AG21, College Building
Who: Dr. Alberto Ferreira De Souza, Universidade Federal do Espírito Santo (UFES), Brazil.
Title: Building IARA – The Intelligent Autonomous Robotic Automobile
The Intelligent Autonomous Robotic Automobile (IARA) is one of the most advanced self-driving cars in world, figuring in eighth place according to the metric of number of interventions per 1000 miles in 2017. We start building IARA in 2009 and, since then, more than 30 students, including Ph.D., M.Sc., and undergraduates, have completed their courses working on the project. IARA is based on the precise localization paradigm where the self-driving car must have a detailed map of the environment to operate autonomously. In this talk, we will present some of the history behind the 10 years of research that led to the current status of development of IARA and will describe how some of its main software modules work, including the modules responsible for mapping, localization and autonomous navigation. A demonstration video can be found here.
Dr. Alberto Ferreira De Souza is a Professor of Computer Science and Coordinator of the Laboratório de Computação de Alto Desempenho (LCAD – High Performance Computing Laboratory) at the Universidade Federal do Espírito Santo (UFES), Brazil. He received B. Eng. (Cum Laude) in electronics engineering and M. Sc. in systems engineering and computer science from Universidade Federal do Rio de Janeiro (COPPE/UFRJ), Brazil, in 1988 and 1993, respectively; and Doctor of Philosophy (Ph.D.) in computer science from the University College London, United Kingdom in 1999. He has authored/co-authored one USA patent and over 130 publications. He has edited proceedings of four conferences (two IEEE sponsored conferences), and is a Standing Member of the Steering Committee of the International Conference in Computer Architecture and High Performance Computing (SBAC-PAD).
Abstract: With the inclusion of renewable energy into power systems, traditional power system face new challenges. Due to their inherent fluctuations and variability, the introduction of renewable energies in power systems poses new challenges in modeling uncertainty. Controlling and optimizing the operation cost by adjusting the output generation of renewable energy resources make power systems operation more reliable and secure. In this work, we aim to solve one optimal microgrid management problem in deterministic and probabilistic framework. This microgrid is connected to the utility and comprises of different renewable energy generators such as photovoltaic (PV), wind generators, batteries, hydroelectric plants, and microturbine.
The objective is to minimize the cost of generation and the voltage deviation from the reference.
The optimization problem is nonlinear since the AC load flow which is a constraint in the optimization problem is nonlinear. we linearize AC load flow in the first step. Secondly, we model the problem in a deterministic framework without considering the impact of uncertainties on power system. The other physical constraints which are taken into account in this work are the equality constraint of load generation balance, output power limitation and voltage limitation. Finally, we will model the nonlinear problem in probabilistic framework to see how uncertainties can affect the system. The first two steps have been done before transferring to PhD and the final step will be done in the next following two years.
Tillman Weyde, Rahda Kopparti, Dan Philps and Artur Garcez attended and presented papers at NeurIPS 2018 in Montreal, Canada, during the week of 3 Dec 2018. Dan and Artur provided an informal overview of their impressions of NeurIPS to the ML group’s End-of-Year meeting on 14 Dec 2018. Thanks to Benedikt Wagner for organising the meeting.
Luciano Serafini (FBK, Trento, Italy) and Michael Spranger (Sony CSL, Tokyo, Japan) visited City’s Research Centre for Machine Learning during the week of 10 Dec 2018. The main focus of the visit was to continue research collaborations on Logic Tensor Networks. LTNs are a deep learning system implemented in Tensorflow, capable of reasoning with first-order many-valued logic. For more information, please check the webpage of the IJCAI’2018 tutorial on LTNs: https://sites.google.com/fbk.eu/ltn/tutorial-ijcai-2018.
Artur Garcez gave a lecture on Relational Neuro-Symbolic AI at the EurAI Advanced Course on AI, 2018, which took place in beautiful Ferrara, Italy.
All the lectures, with overarching theme Statistical Relational AI, are available from the University of Ferrara’s YouTube channel: https://youtu.be/KeFhKi-tOTs?list=PLJPXEH0boeNDWTNwWTWnVffXi5XwAj1mb
Artur Garcez gave two talks: Part 1 gives an overview of two decades of research on neuro-symbolic AI. Part 2 describes in some detail two neuro-symbolic systems for relational learning: Connectionist ILP and the Logic Tensor Networks framework.
Prof Paul Smolensky, Johns Hopkins University and Microsoft Research, will be visiting the Research Centre for Machine Learning for a conversation on neuro-symbolic computing, his book (with Legendre), The Harmonic Mind, MIT Press, and recent papers and results using Tensor Product Representations (TPR) and learning such as A neural-symbolic approach to design of captcha, https://arxiv.org/abs/1710.11475, as well as applications of TPRs to natural language tasks and data sets such as the Stanford question answering dataset SQuAD.
When: Wed, 11 Apr 2018, 2pm
Where: AG24a, College Building
Who: Sebastian Riedel, University College London
Title: Reading and Reasoning with Neural Program Interpreters
Abstract: We are getting better at teaching end-to-end neural models how to answer questions about content in natural language text. However, progress has been mostly restricted to extracting answers that are directly stated in the text. In this talk, I will present our work towards teaching machines not only to read but also to reason with what was read and to do this in an interpretable and controlled fashion. Our main hypothesis is that this can be achieved by the development of neural abstract machines that follow the blueprint of program interpreters for real-world programming languages. We test this idea using two languages: an imperative (Forth) and a declarative (Prolog/Datalog) one. In both cases, we implement differentiable interpreters that can be used for learning reasoning patterns. Crucially, because they are based on interpretable host languages, the interpreters also allow users to easily inject prior knowledge and inspect the learnt patterns. Moreover, on tasks such as math word problems and relational reasoning, our approach compares favourably to state-of-the-art methods.
Sebastian Riedel is a reader in Natural Language Processing and Machine Learning at the University College London (UCL), where he is leading the Machine Reading lab. He is also the head of research at Bloomsbury AI and an Allen Distinguished Investigator. He works in the intersection of Natural Language Processing and Machine Learning, and focuses on teaching machines how to read and reason. He was educated in Hamburg-Harburg (Dipl. Ing) and Edinburgh (MSc., PhD), and worked at the University of Massachusetts Amherst and Tokyo University before joining UCL.
Title: Compact Rule Extraction from Probabilistic Neural Networks
Abstract: I will discuss new techniques for extracting M-of-N rules from restricted Boltzmann machines. I will begin by discussing rule extraction and its importance for explainable AI before describing a method for extending the extraction of conjunctive confidence rules to the more compact M-of-N rules. Comparative results between extraction algorithms will be presented and several possible experimental applications will be discussed. I will also discuss methods of assigning confidence values to extracted rules in a logical way and how to factor in compactness/interpretability in the extraction process.
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