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Editing may seem like a distortion of the reality of the light capture in the camera, and the work of editing as kind of a cheating.However, cameras are capturing a reality quite different from the one the human eye catches, and editing is a way to recreate what the eye of the photographer saw.
Data-driven AI and machine-learning technologies have become pervasive, and even able to outperform humans on specific tasks. However, it has been shown that they suffer from hallucinations known as adversarial examples, i.e., imperceptible, adversarial perturbations to images, text and audio that fool these systems into perceiving things that are not there.In this talk, I will quickly describe threats against machine learning, and identify possible countermeasures.
The lack of explainability of machine learning techniques poses operational, legal and ethical operational problems, in particular for healthcare applications.Our research project, presented in this talk, consists of providing and evaluating explanations of machine learning methods considered as a black box.The application should not become a radical monopoly restricting the users choice and freedom, in particular to manage individually some therapeutic dilemmas.Francis Bacon and Ivan Illich are back !
We observe that many entreprises don’t pass the POC stage of their Machine Learning projects, leading to frustration for both technical and business teams.In this talk we will briefly present the hidden part of the Iceberg of Machine Learning projects (organisation, process and technology), and then show an example of a CI/CD deployment of a ML project on the cloud.
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7PM - 9PM Talks
9PM - 10PM Cocktail - Networking
The rapid pace of research in Deep Reinforcement Learning has been driven by the presence of fast and challenging simulation environments. These environments often take the form of games; with tasks ranging from simple board games, to classic home console games, to modern strategy games. We propose a new benchmark called Obstacle Tower: a high visual fidelity, 3D, 3rd person, procedurally generated game environment. An agent in the Obstacle Tower must learn to solve both low-level control and high-level planning problems in tandem while learning from pixels and a sparse reward signal. Unlike other similar benchmarks such as the ALE, evaluation of agent performance in Obstacle Tower is based on an agent's ability to perform well on unseen instances of the environment.
Machine learning interpretability is becoming an integral part of the data scientist workflow and can no longer be an afterthought. This talk will explore the vibrant area of machine learning interpretability and explain how to understand black-box models. Thanks to an interpretability technique based on colitional game theory: SHAP.
When it comes to actually leverage AI in production and especially in an environment where it interacts with humans, auditability and trust are not optional. That's why Explainable AI becomes a new R&D space. This talks will show why and where explainability in AI is needed, what it actually means and compare some of the techniques that falls into this category.
Impact AI is a think and Do tank that aims to deal with the ethical and societal challenges of AI. We develop an ethical framework for responsible use of Artificial Intelligence respecting principles easy to understand and apply at a large scale. This talk is about the Governance part of this tool box
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6:45 PM : door opening
7:00 PM : intro + speakers
9:00 PM : networking - cocktail
10:30 PM : door closing
La controverse entre IA symbolique et connexioniste
Recent advances in artificial intelligence present a high energy cost, which poses a problem both for the environment and for their integration into connected objects.
This energy cost is due to the fact that AI algorithms are implemented on conventional computers, which are poorly adapted to them.
A promising approach is to use the brain as a model for developing new types of computers that use less energy. In this presentation I explore some key ideas, including the proximity of computation and memory as well as the management of errors and randomness.
Data scientists from Sancare will provide an overview of some of the challenges faced when training deep learning models on electronic health records (EHR), such as robustness and ability to provide explanations
Training deep learning models on electronic health records (EHR) can be prohibitively expensive in terms of computational cost.
Datasets typically include millions of records, each containing several thousands of words.
Moreover, due to the sensitive nature of EHR, all computations must be performed on-premise, i.e., on the campus of the hospital, where GPU resources are usually rare or non-existent.
Chatbots and intelligent personal assistants (such as Siri, Cortana, the Google Assistant, and Amazon Alexa) are being used increasingly more for different purposes, including information access and retrieval.
These dialog systems enable naturalistic human-like interactions where the information needs are expressed in natural language.
Unlike in traditional search engines, where a user-issued query is answered with a search result page, conversational agents can respond in a variety of ways, for example, asking questions back to the user for clarification.
In this talk, I will present our paper "A Reinforcement Learning-driven Translation Model for Search-Oriented Conversational Systems" where we focus on the understanding of natural language expressions for building keyword-based queries. We proposed a reinforcement learning-driven translation model framework able to
1) learn the translation from NL expressions to queries in a supervised way, and,
2) to overcome the lack of large-scale dataset by framing the translation model as a word selection approach and injecting relevance feedback in the learning process. Experiments are carried out on two TREC datasets and outline the effectiveness of our approach.
We use open-data and machine learning to compute and forecast where and how has been produced the electricity you consume
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Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.
From my recent paper published in Brain: https://doi.org/10.1093/brain/awy267] Determining the state of consciousness in patients with disorders of consciousness is a challenging practical and theoretical problem. Recent findings suggest that multiple markers of brain activity extracted from the EEG may index the state of consciousness in the human brain. Furthermore, machine learning has been found to optimize their capacity to discriminate different states of consciousness in clinical practice. ... Our findings demonstrate that EEG markers of consciousness can be reliably, economically and automatically identified with machine learning in various clinical and acquisition contexts.
During this talk Eiso Kant demonstrates how different machine learning techniques can be used to learn from source code and provide developers with novel insights into their code. This talk includes several demos that show the power of MLonCode.
Résumé: Current approaches to artificial intelligence and machine learning are still fundamentally limited in comparison with autonomous learning capabilities of children. Even impressive systems like AlphaGo require huge amounts of trial and error and the help of an engineer to deal with other games or tasks. On the contrary, children learn fast and robustly a wide and open-ended repertoire of skills, without needing any form of intervention by an engineer. I will present a research program that has studied computational modeling of child development and learning mechanisms in the last decade. I will explain approaches to model curiosity-driven autonomous learning, with algorithmic models enabling machines to sample and explore their own goals, self-organizing a learning curriculum without any external supervision. I will show how this has helped scientists understand better aspects of human development, and how this has opened novel approaches to address the current limits of machine learning. I will illustrate this research with experiments where robots learn autonomously repertoires of complex tasks. I will then conclude by illustrating how these approaches can be applied successfully in the domain of educational technologies, enabling to personalize sequences of exercises for human learners, while maximizing both learning efficiency and intrinsic motivation.
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.In this talk we will reveal what's been happening within the Data Science Team at Vente Privee this year...
At vente-privee we customize a lot of things, and this talk would describe the mechanism behind catalog customization. When a customer enters a sales, we create a section filled with items recommended for this specific customer, based on its previous purchases, and other criteria.
Quantum computing paradigm applied to automated machine learning. an efficient alternative to hyperparamter search.
Le rôle de la Fab Big Data et de l'équipe data science et engineering pour le groupe SNCF.. Quelques projets en cours représentatifs :
- Adhérence : mieux connaître, localiser et comprendre les phénomènes de perte d'adhérence (c'est le contact entre la roue et le rail).
- Energie : analyse des consommations d'energie électrique et prévision de consommation.
- Projets prospectifs :
- Active learning
- Lisibilité des modèles de ML
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.Hi Igor,
I would like to ask you a favor. We are looking for a Postdoctoral Researcher interested in small data deep learning and explainable machine learning. I was wondering whether it is possible to list the opening on your blog. Information on the available position is below.
We are looking for a Postdoctoral Researcher with expertise in statistics, machine learning, convex/non-convex optimization and/or uncertainty quantification. Postdoctoral Researcher will support ongoing efforts concerned with small-data deep learning and related topics, such as, transfer learning, generative modeling, self-supervised or unsupervised learning, and explainable ML. This position is in the Computation Directorate within the Center for Applied Scientific Computing (CASC) Division at Lawrence Livermore National Lab, Livermore, CA.
Essential Duties
- Research, design, implement and apply a variety of advanced data science methods in multiple application areas (such as material science, high energy physics, predictive medicine, cybersecurity) in a collaborative scientific environment.
- Document research by publishing papers at conferences/journals such as NIPS, ICML, ICLR, IJCAI, AAAI, AISTATS, ACL, CVPR, JMLR or similar.
Qualifications
- Ph.D. in statistics or computer science or a related field.
- Experience in modern machine learning environments (TensorFlow, PyTorch, etc.).
- Proficiency in one or more of the following machine learning areas: deep learning, reinforcement learning, and Bayesian nonparametric.
- Knowledge of C/C++, Python.
If interested, please contact me directly at kailkhura1@llnl.gov.
Regards,
Bhavya
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Dear Igor,
I'm a long-time reader of your blog and wanted to share our recent paper on a relation between compressed sensing and neural network architectures. The paper introduces a new network construction based on the Laplace transform that results in activations such as ReLU and gating/threshold functions. It would be great if you could distribute the link on nuit-blanche.
The paper/preprint is here:
https://ieeexplore.ieee.org/document/8478823
https://www.netit.tu-berlin.de/fileadmin/fg314/limmer/LimSta18.pdf
Many thanks and best regards,
Steffen
Dipl.-Ing. Univ. Steffen Limmer
Raum HFT-TA 412
Technische Universität Berlin
Institut für Telekommunikationssysteme
Fachgebiet Netzwerk-Informationstheorie
Einsteinufer 25, 10587 Berlin
This paper presents a theoretical and conceptual framework to design neural architectures for Bayesian compressive sensing of simplex-constrained sparse stochastic vectors. First we recast the problem of MMSE estimation (w.r.t. a pre-defined uniform input distribution over the simplex) as the problem of computing the centroid of a polytope that is equal to the intersection of the simplex and an affine subspace determined by compressive measurements. Then we use multidimensional Laplace techniques to obtain a closed-form solution to this computation problem, and we show how to map this solution to a neural architecture comprising threshold functions, rectified linear (ReLU) and rectified polynomial (ReP) activation functions. In the proposed architecture, the number of layers is equal to the number of measurements which allows for faster solutions in the low-measurement regime when compared to the integration by domain decomposition or Monte-Carlo approximation. We also show by simulation that the proposed solution is robust to small model mismatches; furthermore, the proposed architecture yields superior approximations with less parameters when compared to a standard ReLU architecture in a supervised learning setting.
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.La thèse de Grothendieck et son article ultérieur intitulé "Résumé de la théorie métrique des produits tensoriels topologiques" (1956) a eu un énorme impact sur le développement de la géométrie des espaces de Banach pendant les 60 dernières années. Nous passerons en revue ce "Résumé" en nous concentrant sur le résultat que Grothendieck lui-même a appelé le théorème fondamental de la théorie métrique des produits tensoriels, maintenant devenu "l'inégalité de Grothendieck" ou "le théorème de Grothendieck". Ce résultat a récemment fait une apparition pour le moins inattendue dans plusieurs domaines a priori fort éloignés des préoccupations de Grothendieck. L'une a trait aux C ∗ -algèbres et aux espaces d'opérateurs (ou "espaces de Banach non-commutatifs"), une autre aux inégalités de Bell et à leur "violation" en mécanique quantique, une dernière relie la constante de Grothendieck au problème P=NP et à la théorie des graphes.
Probably the most famous of Grothendieck's contributions to Banach space theory is the result that he himself described as "the fundamental theorem in the metric theory of tensor products". That is now commonly referred to as "Grothendieck's theorem" (GT in short), or sometimes as "Grothendieck's inequality". This had a major impact first in Banach space theory (roughly after 1968), then, later on, in C∗-algebra theory, (roughly after 1978). More recently, in this millennium, a new version of GT has been successfully developed in the framework of "operator spaces" or non-commutative Banach spaces. In addition, GT independently surfaced in several quite unrelated fields:\ in connection with Bell's inequality in quantum mechanics, in graph theory where the Grothendieck constant of a graph has been introduced and in computer science where the Grothendieck inequality is invoked to replace certain NP hard problems by others that can be treated by "semidefinite programming" and hence solved in polynomial time. In this expository paper, we present a review of all these topics, starting from the original GT. We concentrate on the more recent developments and merely outline those of the first Banach space period since detailed accounts of that are already available, for instance the author's 1986 CBMS notes.
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