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SinFra 2019 - Symposium on Artificial Intelligence

Program SinFra 2019 (PDF: Click here) Dec 12th (Thursday) @ InFuse, Connexis South, Level 141 Fusionopolis WayConnexis TowerSingapore 138632 0830 - 0900 Registration 0900 - 0915 Opening and Welcome Remarks by Professor Lye Kin Mun, Executive Director, Astar-I2R0915 - 0930 Introduction to School of Computing and NUS by Mohan Kankanhalli, Dean, School of Computing, National University of Singapore0930 - 0950 Introduction to IPAL and SinFra 2019 by Professor Christophe Jouffrais, Director of IPAL.

0950 - 1030 SESSION 10950 - 1010 Trust in AI Blaise Genest (CNRS, IRISA)1010 - 1030 Trustworthy and Accountable Machine Learning: Privacy, Robustness, and Interpretability Challenges Reza Shokri (NUS, SoC) 1030 - 1100 Coffee Break 1100 - 1220 SESSION 21100 - 1120 Positivity Certificates for Analyzing Robustness in DNN Jean-Bernard Lasserre (CNRS, ANITI &

LPN)1200 - 1220 Event-based Visual Sensor for Robotic Navigation  Fabien Colonnier (A*star, I2R) 1220 - 1350 Lunch1350 - 1450 Demos and Tour of I2R (Meet at InFuse) 1450 - 1610 SESSION 31450 - 1510 Introduction to MIAI Eric Gaussier, Director of MIAI 1510 - 1530 Towards Real-time Lifelong Learning Savitha Ramasamy (A*STAR - I2R) 1530 - 1550 Deep Learning Research Inspired by Biomedical Applications Hwee Kuan Lee (A*STAR - BII) 1550 – 1610 Improving Energy-efficiency for Security and AI Techniques for the IoT Compute Hierarchy Trevor Carlson (NUS, SoC)

Dec 13th (Friday) @NUS (iCube, Seminar Room 1)i3 Building (I-CUBE)Seminar Room 1 @ Level 121 Heng Mui Keng TerraceSingapore 119613 0915 - 0930 Welcome by the Ambassador of France, HE Marc Abensour0930 - 0950 Introduction to ANITI Professor Nicholas Asher, Director of ANITI0950 - 1010 Introduction to AI.SG Ma Su Su (Head, Research Management, AI.SG) 1010 - 1050 SESSION 51010 - 1030 Explainability for Learning Systems using Logic Nicolas Asher (CNRS, ANITI &

LIG)1140 - 1200 Semantic and Sentiment Analysis for Knowledge Graph Construction Su Jian  (A*star, I2R)1200 - 1220 Natural Language Processing for e-Health Pierre Zweigenbaum (CNRS, Limsi)1220 - 1240 Language Translation: Technologies, Challenges and Applications Aw Ai Ti (A*star, I2R) 1240 - 1400 Lunch1400 - 1500 SoC Tour (hosted by David Hsu, NUS, SoC) COM1 Robotics Living Lab 1500 - 1600 SESSION 71500 - 1520 TheoremKB: Towards a Knowledge Base of Mathematical Results Pierre Senellart (ENS, DI ENS)1520 - 1540 Nonnegative Matrix Factorisation for Data Processing Cédric Févotte ( CNRS, ANITI &

IRIT)1540 - 1600 A Ranking Model Motivated by NMF with Applications to Tennis Analytics (and other research activities in my group) Vincent Tan (NUS, Mathematics/ECE) 1600 - 1630 Coffee Break 1630 - 1710 SESSION 81630 - 1650 Transparency in AI and Ranked Retrieval Philippe Mulhem (CNRs, MIAI &

As they are increasingly being deployed in large scale critical applications for processing various types of data, new questions related to their trustworthiness would arise.

In this talk, I will go over the challenges of building trustworthy and accountable machine learning algorithms in centralized and collaborative (federated) settings, and will discuss the inter-relation between privacy, robustness, and interpretability, and whether they are preserved in a fair manner.

His research focuses on trustworthy machine learning, quantitative analysis of data privacy, and design of privacy-preserving algorithms for practical applications, ranging from data synthesis to collaborative machine learning.

He received the Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies in 2018, for his work on analyzing the privacy risks of machine learning models, and was a runner-up in 2012, for his work on quantifying location privacy.

Kim Chuan Toh (NUS, Mathematics) Abstract: In this talk, we shall demonstrate how second order sparsity (SOS) in important optimization problems such as sparse optimization models in machine learning, semidefinite programming, and many others can be exploited to design highly efficient algorithms.   The SOS property appears naturally when one applies a semismooth Newton (SSN) method to solve the subproblems in an augmented Lagrangian method (ALM) designed for certain classes of structured convex optimization problems.

For lasso problems with sparse solutions, the cost of solving a single ALM subproblem by our second order method is comparable or even lower than that in a single iteration of many first order methods.Consequently, with the fast convergence of the SSN based ALM, we are able to solve many challenging large scale convex optimization problems in big data applications efficiently and robustly.

He works extensively on convex programming, particularly large-scale matrix optimization problems such as semidefinite programming, and structured convex problems arising from machine learning and statistics.

LPN) Abstract:  In this talk, I will present the bio-inspired origins of convolutional neural networks (CNN), what part of the brain CNN is the formal analog but also some important differences with the human visual system.

Then, by means of bio-inspiration from the human brain and cognitive sciences, I will present how we can improve the reliability of neural networks with regards to (1) anticipation and classification of visual events (2) incremental learning and (3) resilience to adversarial attacks.

Grenoble Alpes) is a scientific researcher in the fields of neural computation, psychological sciences, cognitive neurosciences applied to the field of visual perception and visual cognition.

He has published more than 80 articles in highly ranked journals (including Neural Networks, NeuroComputing, Connection Science, Cognition, Psychological Science, Behavioural and Brain Science, Nature Scientific Reports, etc.) Details are provided here: https://scholar.google.fr/citations?user=DCitfSsAAAAJ&hl=fr) He has successfully participated in 12 multicentre research projects (LABEX, ANR, PHRC, etc.) Event-based Visual Sensor for Robotic Navigation

He investigates bio-inspired solutions to solve localization and navigation problematics using artificial compound eyes, event-based sensors and neuromorphic computation.

Eric Gaussier (UGA, Scientific Director of MIAI) Abstract: MIAI @ Grenoble Alpes: MIAI, the Grenoble Multidisciplinary Institute in Artificial Intelligence, develops the new generation of AI models and systems, from hardware architectures to software systems, with a focus on three application domains related to human beings and the environment: Health, Environment &

I am interested in the general problem of accessing, mining and learning from large (text) collections, through machine learning models and methods and work on both fundamental problems (through the development of new models that explain different characteristics of large-scale collections/networks) and applications related to computational linguistics and information retrieval.

however, when solving clinical problems, one starts to realise that many mainstream deep learning paradigms no longer fit into very specialized demands of clinical applications.

In hardware security, many challenges remain to protect future systems from intrusion, monitoring and hardware trojans, while doing so at an extremely low cost.

Our work aims to move to a higher level, above the circuit-design level, to the architecture level, to allow us to physically protect systems without the expensive overheads currently seen.

For the future of accelerators and always-on machine learning, many designers have been producing innovative solutions to apply deep neural networks in a fast and energy efficient manner.

What has been overlooked recently, is the ability to transform modern networks, while still maintaining accuracy, into lighter-weight solutions that change the formula with respect to processing demands currently seen.

Future designs, such as Binary and Ternary Neural Networks (BNN/TNN) and Neuromorphic computing (or Spiking Neural Networks (SNN) in this case) show promise to reduce latency while improving overall efficiency, allowing us to target future highly efficient system design goals.

He has also spent a number of years working in industry, at IBM in Poughkeepsie, NY from 2003 to 2007, at the imec research lab in Leuven, Belgium, from 2007 to 2009, and collaborated with the Intel ExaScience Lab in Belgium from 2009 to 2014.

Trevor Carlson’s research interests are in computer architecture targeting highly-efficient microarchitectures, secure processor designs, hardware/software co-design for energy efficiency, performance modeling and fast and scalable simulation methodologies.

During his PhD, in collaboration with the Intel ExaScience Lab, he co-developed the Sniper Multi-core Simulator which is being used by hundreds of researchers to evaluate the performance and power-efficiency of next generation systems, and continues to be used to explore next-generation processor design at Intel today.

Starting as a researcher at imec, and as a postdoctoral researcher at Uppsala University, and continuing to his current work at NUS, he investigates processor architectures to more efficiently handle long-latency memory accesses.

We need a new style of interaction that can better support human activities in nature and with other people as well as reducing cognitive load by blending reactive operations with appropriately designed proactive initiatives that can offer just-in-time assistance.

Dr. Zhao has a wealth of experience in developing new interface tools and applications (i.e., Draco, won best iPad App of the year in 2016), and publishes regularly in top HCI conferences and journals.

First, while there has been much work in natural language processing (NLP) on developing computational models to process documents, such approaches might not always be directly portable to dialogue processing, as the linguistic characteristics of documents and spoken conversations are intrinsically distinct: documents are one-way communications between the writer and the reader, whereas spoken conversations are spontaneous, dynamic information exchanges between at least two speakers.

Second, the scarcity of large-scale well-annotated linguistic resources for modeling spoken conversations makes it difficult to develop scalable solutions that are readily extendible or generalizable to different scenario setups and domains.

In addition, we show how proposed neural architectures that consider dialogue turns and topic segments in spoken conversations can enhance dialogue comprehension and summarization capabilities, enabling targeted applications in healthcare, journalism, and education.  Bio: Nancy F.

In addition to her academic endeavors, Dr. Chen has also consulted for various companies ranging from startups to multinational corporations in the areas of emotional intelligence (Cogito Health), speech recognition (Vlingo, acquired by Nuance), and defense and aerospace (BAE Systems).

We have used the camera of phone to localize the user by analyzing the museum artifact in front of him and interpret their gesture in order to interact with the recorded guided tour.

I’ll also introduce some large scale deployments of these technologies on search, personal assistant, information gathering and standard enforcement for commercial entities as well as government organisations.

I will illustrate how Natural Language Processing helps to unlock information and knowledge from text found in patient records, in patient forums, in the scientific biomedical literature, and in doctor-patient dialogues.

Current natural language processing methods heavily rely on self-trained word representations, known as word embeddings, whose quality depends on the availability of very large text corpora: in a specialized domain however, text corpora are necessarily smaller than in unrestricted domains.

I will therefore present methods that aim to increase the quality of word embeddings in a specialized domain by exploiting large out-of-domain corpora and a priori domain knowledge.

In this work, we investigate machine translation for local languages with language varieties and under different conditions of language resources and demonstrate the customised machine translation engines that we have developed with benchmark results as compared to Google and Microsoft.

Prior to her current appointment, Ms Aw spent most of her career spearheading the research and development of Southeast Asian Language Processing and Machine Translation capabilities in Singapore and has successfully established the team as a locally renowned Machine Translation technology provider in Southeast Asian languages.

But the actual unit of information of use by scientists in mathematical sciences is not the scientific article per se, but the mathematical results (theorems, lemmas, etc.) it contains: their statements, proofs, and possible other metadata.

In this talk, we present a vision and preliminary work on TheoremKB, a project to turn the scientific literature in these fields from a collection of PDF articles to an open knowledge base of theorems where mathematical results are the object of interest, that can be explored in new ways.

Nonnegative matrix factorisation (NMF) has become a popular technique for analysing data with nonnegative values, with applications in many areas such as in text information retrieval, user recommendation, hyperspectral imaging or audio signal processing.

In particular, I will introduce a new framework--based on sum-of-squares optimization---for unifying and systematizing the performance analysis of first-order black-box optimization algorithms for unconstrained convex minimization [2].

will present some works achieved in the MRIM team of grenoble regarding explainabiity in image classification using CNNs and works toward transparency in ranked retrieval (Web and job search).

The main idea behind this work is to go toward explaining why a learning algorithm gives one results, or at least how large are the impact of some input elements on ranking systems.

Its activities focus mainly on the modeling of multimedia data (text/image/video) for Information Retrieval and Information Filtering, personalization, machine learning models and methods for information retrieval, system experimentation and evaluation.

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