Louis menioned to me this job announcement in French recently:
L’Institut national de l’audiovisuel (http://institut.ina.fr) recrute pour son Département Recherche et Innovation un Ingénieur R&D en CDI, sur les technologies de Machine Learning, et plus particulièrement de Deep Learning, appliquées à la vidéo, l’image, l’audio et/ou le texte, pour la valorisation du patrimoine audiovisuel, et plus généralement pour la conception et l’expérimentation de nouveaux usages dans le domaine des médias. En lien avec les chercheurs et doctorants du Département et avec les services opérationnels de l’INA, vous aurez pour mission de porter cette problématique et de participer aux projets de recherche et d’innovation de l’Institut, dans un cadre à la fois industriel et académique. A terme, vous pourrez être amené à participer à l’encadrement des doctorants. Diplôme requis : doctorat. Compétences :
Machine Learning, Deep Learning : aspects formels (par ex. CNN, RNN, LSTM, GAN) et frameworks état de l’art (par ex. PyTorch, Tensorflow, Keras)
Développement informatique : Python, C/C++, Java
Publications scientifiques
Intérêt pour les applications opérationnelles des résultats de recherche
Intérêt pour le monde de l’audiovisuel et des médias, pour les Sciences Humaines et Sociales et les Humanités Numériques
Tenured and Tenure-Track Faculty positions in robotics at ENSTA ParisTech The Computer Science and System Engineering department (U2IS) at ENSTA ParisTech is opening several tenured and tenure-track faculty positions in the field of robotics.
== Position description == The candidates will be recruited in the Autonomous Systems and Robotics research team, inside the U2IS department. ASR dedicates itself to the development of technological systems with strong autonomy and high dependability, focusing in particular on learning, perception, navigation, human-robot interaction for assistive robotics and intelligent vehicles. The candidates will lead research and innovation activities on one or several of the following themes:
machine learning, developmental and cognitive robotics
navigation, SLAM, localization, planning and control
systems architecture for autonomous vehicles.
These activities should be conducted in the application areas of intelligent vehicles, automation, assistive/service robotics or defense and security, in link with the other members of the team. In particular, candidates could be integrated in the joint ENSTA ParisTech-INRIA FLOWERS team. The candidates are expected to participate in the development of partnerships, collaborations and projects in his/her domain, particularly in partnership with industry. Inquiries about the scientific context of the position can be directed to David Filliat (david.filliat@ensta-paristech.fr) or Bruno Monsuez (bruno.monsuez@ensta-paristech.fr).
Faculty duties include teaching at the graduate and undergraduate levels, research, and supervision of student research. Basic knowledge, or willingness to learn French language are required as part of the teaching will be in French. Candidates must have the ability to develop a leading research program with a focus on technology development and translation into concrete applications such as robotics or intelligent vehicles.
== About ENSTA == ENSTA ParisTech is one of the most renowned French institutes of engineering education and research (Grande Ecole). Located in Palaiseau, it offers graduate level scientific education, excellent research facilities and a broad international network. It is a founding member of Paris-Saclay University, a federal university composed of 19 institutions (Universities, Grandes Ecoles, Research organisms). Large companies have also settled their research center in this area so that by 2020, this scientific cluster, the largest in France, will gather up to 15% of French research.
== Application == Requirements for applying are: possession of a doctoral university degree, excellent skills in teaching and research, a strong publication record, and have experience with conducting research projects. Candidates for tenured position should possess the "Habilitation à diriger des Recherches" or comparable research experience. The candidates will be expected to conduct high quality research, teaching, and secure competitive external funding. The complete application package includes:
a curriculum vitae including a list of publications;
a research statement;
a teaching statement including a list of lectures the candidate could teach;
the names and email addresses of three references.
We have recently opened a PhD position in our group that can be of interest to your subscribers. The details are below. I was wondering whether it would be possible to share it through Nuit Blanche?
PhD Position for Signal Processing for Energy Harvesting Sensor Networks
Project Description
In recent years, energy harvesting (EH) solutions have become an emerging paradigm for powering up future wireless sensing systems. As such, energy harvesting constitutes a key enabling technology for Internet-of-Things (IoT) applications and Wireless Sensor Networks (WSN) including smart homes and smart factories. This position focuses on providing signal processing solutions for such energy harvesting wireless sensor networks. Modern machine learning techniques and optimization approaches will be important ingredients of the work.
Position Description
This is a full-time position where you will be employed by Uppsala University. Your main responsibility will be to pursue your own doctoral studies. The PhD position is for four years, extendable to a maximum of five years, including departmental duties at a level of at most 20% (typically teaching).
Application deadline: 15 February 2018
More information and application instructions: https://www.uu.se/en/about-uu/join-us/details/?positionId=186914
Training deep neural networks results in strong learned representations that show good generalization capabilities. In most cases, training involves iterative modification of all weights inside the network via back-propagation. In this paper, we propose to take an extreme approach and fix \emph{almost all weights} of a deep convolutional neural network in their randomly initialized values, allowing only a small portion to be learned. As our experiments show, this often results in performance which is on par with the performance of learning all weights. The implications of this intriguing property or deep neural networks are discussed and we suggest ways to harness it to create more robust representations.
Scortex deploys artificial intelligence in the heart of factories. We help our customers take the next big leap in smart automation thanks to our Quality Intelligence Solution. Our platform enables manufacturing companies to take control of their quality:
Automate visual inspection tasks
Monitor key quality data in real time through our intuitive platform
Improve production process by consolidating production knowledge.
Thanks to our proprietary deep learning platform, we provide a state-of-the-art performance and robust vision solution for quality intelligence. What you will do
As a proactive member of the machine learning and computer vision team, your work will include a varied range of challenges: explore various state of the art techniques to help solve tasks currently unbeaten by computers;
stay on the bleeding edge of research and participate actively in the community;
design, develop and implement supervised and unsupervised models with extremely constraining requirements not only on accuracy, but also on real-time execution, fast and scalable training processes and minimal annotation levels;
help improve our pipelines of data acquisition, training and inference.
What we are looking for
In-depth knowledge of deep learning techniques applied to computer vision: deep convolutional networks, autoencoders, image (pre)processing, regularization;
Proficient knowledge of both supervised and unsupervised machine learning techniques : clustering, object detection, generative models, dimensionality reduction;
Understanding of standard computer vision techniques : filtering, transformations, descriptors and detectors;
Knowledge and understanding of the mathematics underlying all of the above : probability and statistics, optimization, linear algebra, numerical computation;
Proven experience with at least one machine learning framework (bonus points for Keras or Tensorflow);
Research engineer position in the DREAM project at ISIR, Sorbonne-Université, Paris, France.
Job position available immediately and for 1 year (may be extended).
The DREAM European project (http://www.robotsthatdream.eu/) is focused on the bootstrap of a developmental process allowing a robot to learn about its environment and the objects it contains.
We are looking for highly motivated candidates with a strong experience in developing software for robotics, in particular on the ROS middleware. The recruited engineer will be in charge of the development and deployment of the ROS modules supporting the DREAM cognitive architecture. He/she will also help the partners to integrate their work into the cognitive architecture and will work on the validation experiments of the project. Programming skills in modern C++ and python are expected. The position involves robotics experiments that will be done on Baxter, PR2 and Pepper robots. The position may be extended later on to more than one year.
The position is located in the Institute of Intelligent Systems and Robotics (ISIR, http://www.isir.upmc.fr), Paris, France. ISIR belongs to Sorbonne Université which is among the top ranked French universities (http://sorbonne-universite.fr/en).
Speaking or understanding french is not required.
To apply, please send a CV, letter of motivation (max 2 pages), and a list of three references via e-mail to stephane.doncieux@upmc.fr. Please put [DREAM engineer application] in the subject of the mail. Review of applicants will begin immediately, and will continue until the position is filled.
A number of recent papers have provided evidence that practical design questions about neural networks may be tackled theoretically by studying the behavior of random networks. However, until now the tools available for analyzing random neural networks have been relatively ad-hoc. In this work, we show that the distribution of pre-activations in random neural networks can be exactly mapped onto lattice models in statistical physics. We argue that several previous investigations of stochastic networks actually studied a particular factorial approximation to the full lattice model. For random linear networks and random rectified linear networks we show that the corresponding lattice models in the wide network limit may be systematically approximated by a Gaussian distribution with covariance between the layers of the network. In each case, the approximate distribution can be diagonalized by Fourier transformation. We show that this approximation accurately describes the results of numerical simulations of wide random neural networks. Finally, we demonstrate that in each case the large scale behavior of the random networks can be approximated by an effective field theory.
Neural network configurations with random weights play an important role in the analysis of deep learning. They define the initial loss landscape and are closely related to kernel and random feature methods. Despite the fact that these networks are built out of random matrices, the vast and powerful machinery of random matrix theory has so far found limited success in studying them. A main obstacle in this direction is that neural networks are nonlinear, which prevents the straightforward utilization of many of the existing mathematical results. In this work, we open the door for direct applications of random matrix theory to deep learning by demonstrating that the pointwise nonlinearities typically applied in neural networks can be incorporated into a standard method of proof in random matrix theory known as the moments method. The test case for our study is the Gram matrix Y^TY, Y=f(WX), where W is a random weight matrix, X is a random data matrix, and f is a pointwise nonlinear activation function. We derive an explicit representation for the trace of the resolvent of this matrix, which defines its limiting spectral distribution. We apply these results to the computation of the asymptotic performance of single-layer random feature methods on a memorization task and to the analysis of the eigenvalues of the data covariance matrix as it propagates through a neural network. As a byproduct of our analysis, we identify an intriguing new class of activation functions with favorable properties.
Understanding the geometry of neural network loss surfaces is important for the development of improved optimization algorithms and for building a theoretical understanding of why deep learning works. In this paper, we study the geometry in terms of the distribution of eigenvalues of the Hessian matrix at critical points of varying energy. We introduce an analytical framework and a set of tools from random matrix theory that allow us to compute an approximation of this distribution under a set of simplifying assumptions. The shape of the spectrum depends strongly on the energy and another key parameter, $\phi $, which measures the ratio of parameters to data points. Our analysis predicts and numerical simulations support that for critical points of small index, the number of negative eigenvalues scales like the 3/2 power of the energy. We leave as an open problem an explanation for ur observation that, in the context of a certain memorization task, the energy of minimizers is well-approximated by the function 1/2(1−ϕ)21/2(1−ϕ)2.
A deep fully-connected neural network with an i.i.d. prior over its parameters is equivalent to a Gaussian process (GP) in the limit of infinite network width. This correspondence enables exact Bayesian inference for neural networks on regression tasks by means of straightforward matrix computations. For single hidden-layer networks, the covariance function of this GP has long been known. Recently, kernel functions for multi-layer random neural networks have been developed, but only outside of a Bayesian framework. As such, previous work has not identified the correspondence between using these kernels as the covariance function for a GP and performing fully Bayesian prediction with a deep neural network. In this work, we derive this correspondence and develop a computationally efficient pipeline to compute the covariance functions. We then use the resulting GP to perform Bayesian inference for deep neural networks on MNIST and CIFAR-10. We find that the GP-based predictions are competitive and can outperform neural networks trained with stochastic gradient descent. We observe that the trained neural network accuracy approaches that of the corresponding GP-based computation with increasing layer width, and that the GP uncertainty is strongly correlated with prediction error. We connect our observations to the recent development of signal propagation in random neural networks.
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