LEPISME

We present a first version of a software dedicated to an application of a classical nonlinear control theory problem to the study of compartmental models in biology. The software is being developed over a new free computer algebra library dedicated to differential and algebraic elimination

Preventing premature convergence and proving the optimality in evolutionary algorithms

http://ea2013.inria.fr//proceedings.pdfInternational audienceEvolutionary Algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their inability to quickly compute a good approximation of the global minimum and their exponential complexity. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a Branch and Bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and outperforms both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality

Advancing probabilistic and causal deep learning in medical image analysis

The power and flexibility of deep learning have made it an indispensable tool for tackling modern machine learning problems. However, this flexibility comes at the cost of robustness and interpretability, which can lead to undesirable or even harmful outcomes. Deep learning models often fail to generalise to real-world conditions and produce unforeseen errors that hinder wide adoption in safety-critical critical domains such as healthcare. This thesis presents multiple works that address the reliability problems of deep learning in safety-critical domains by being aware of its vulnerabilities and incorporating more domain knowledge when designing and evaluating our algorithms. We start by showing how close collaboration with domain experts is necessary to achieve good results in a real-world clinical task - the multiclass semantic segmentation of traumatic brain injuries (TBI) lesions in head CT. We continue by proposing an algorithm that models spatially coherent aleatoric uncertainty in segmentation tasks by considering the dependencies between pixels. The lack of proper uncertainty quantification is a robustness issue which is ubiquitous in deep learning. Tackling this issue is of the utmost importance if we want to deploy these systems in the real world. Lastly, we present a general framework for evaluating image counterfactual inference models in the absence of ground-truth counterfactuals. Counterfactuals are extremely useful to reason about models and data and to probe models for explanations or mistakes. As a result, their evaluation is critical for improving the interpretability of deep learning models.Open Acces

Neuromorphic Systems for Pattern Recognition and Uav Trajectory Planning

Detection and control are two essential components in an intelligent system. This thesis investigates novel techniques in both areas with a focus on the applications of handwritten text recognition and UAV flight control. Recognizing handwritten texts is a challenging task due to many different writing styles and lack of clear boundary between adjacent characters. The difficulty is greatly increased if the detection algorithms is solely based on pattern matching without information of dynamics of handwriting trajectories. Motivated by the aforementioned challenges, this thesis first investigates the pattern recognition problem. We use offline handwritten texts recognition as a case study to explore the performance of a recurrent belief propagation model. We first develop a probabilistic inference network to post process the recognition results of deep Convolutional Neural Network (CNN) (e.g. LeNet) and collect individual characters to form words. The output of the inference network is a set of words and their probability. A series of post processing and improvement techniques are then introduced to further increase the recognition accuracy. We study the performance of proposed model through various comparisons. The results show that it significantly improves the accuracy by correcting deletion, insertion and replacement errors, which are the main sources of invalid candidate words. Deep Reinforcement Learning (DRL) has widely been applied to control the autonomous systems because it provides solutions for various complex decision-making tasks that previously could not be solved solely with deep learning. To enable autonomous Unmanned Aerial Vehicles (UAV), this thesis presents a two-level trajectory planning framework for UAVs in an indoor environment. A sequence of waypoints is selected at the higher-level, which leads the UAV from its current position to the destination. At the lower-level, an optimal trajectory is generated analytically between each pair of adjacent waypoints. The goal of trajectory generation is to maintain the stability of the UAV, and the goal of the waypoints planning is to select waypoints with the lowest control thrust throughout the entire trip while avoiding collisions with obstacles. The entire framework is implemented using DRL, which learns the highly complicated and nonlinear interaction between those two levels, and the impact from the environment. Given the pre-planned trajectory, this thesis further presents an actor-critic reinforcement learning framework that realizes continuous trajectory control of the UAV through a set of desired waypoints. We construct a deep neural network and develop reinforcement learning for better trajectory tracking. In addition, Field Programmable Gate Arrays (FPGA) based hardware acceleration is designed for energy efficient real-time control. If we are to integrate the trajectory planning model onto a UAV system for real-time on-board planning, a key challenge is how to deliver required performance under strict memory and computational constraints. Techniques that compress Deep Neural Network (DNN) models attract our attention because they allow optimized neural network models to be efficiently deployed on platforms with limited energy and storage capacity. However, conventional model compression techniques prune the DNN after it is fully trained, which is very time-consuming especially when the model is trained using DRL. To overcome the limitation, we present an early phase integrated neural network weight compression system for DRL based waypoints planning. By applying pruning at an early phase, the compression of the DRL model can be realized without significant overhead in training. By tightly integrating pruning and retraining at the early phase, we achieve a higher model compression rate, reduce more memory and computing complexity, and improve the success rate compared to the original work

An investigation of lateral support systems by the finite element method

Bibliography: p. 82-83.The design of lateral support systems, in the context of surface excavations, are usually done using conventional (classical) methods of analysis. For these design procedures limit state assumptions are made concerning the lateral earth pressures acting on the structure to determine the support system characteristics. No information with regard to the deformation of the soil adjacent to the structure can be provided. The objective of this thesis is to examine the finite element method of analysis as an alternative design tool which is adaptable to a wide range of situations. Finite element models are developed to investigate the influence of the plastic flow rule, wall friction and the soil type on the behaviour of a cantilever support system. Subsequently, the effect of wall stiffness, prop stiffness and the application of prop loads on the performance of a multiple level support system is examined. The results from these studies focus on wall displacements, lateral earth pressures, bending moments, plastic strain patterns and surface settlements behind the wall. The investigation provides extensive information about the entire soil-structure interaction of the system. This potential of the finite element method can be used in the optimization of support system design

Adaptive algorithms for history matching and uncertainty quantification

Numerical reservoir simulation models are the basis for many decisions in regard to predicting, optimising, and improving production performance of oil and gas reservoirs. History matching is required to calibrate models to the dynamic behaviour of the reservoir, due to the existence of uncertainty in model parameters. Finally a set of history matched models are used for reservoir performance prediction and economic and risk assessment of different development scenarios. Various algorithms are employed to search and sample parameter space in history matching and uncertainty quantification problems. The algorithm choice and implementation, as done through a number of control parameters, have a significant impact on effectiveness and efficiency of the algorithm and thus, the quality of results and the speed of the process. This thesis is concerned with investigation, development, and implementation of improved and adaptive algorithms for reservoir history matching and uncertainty quantification problems. A set of evolutionary algorithms are considered and applied to history matching. The shared characteristic of applied algorithms is adaptation by balancing exploration and exploitation of the search space, which can lead to improved convergence and diversity. This includes the use of estimation of distribution algorithms, which implicitly adapt their search mechanism to the characteristics of the problem. Hybridising them with genetic algorithms, multiobjective sorting algorithms, and real-coded, multi-model and multivariate Gaussian-based models can help these algorithms to adapt even more and improve their performance. Finally diversity measures are used to develop an explicit, adaptive algorithm and control the algorithm’s performance, based on the structure of the problem. Uncertainty quantification in a Bayesian framework can be carried out by resampling of the search space using Markov chain Monte-Carlo sampling algorithms. Common critiques of these are low efficiency and their need for control parameter tuning. A Metropolis-Hastings sampling algorithm with an adaptive multivariate Gaussian proposal distribution and a K-nearest neighbour approximation has been developed and applied

Reparametrization in deep learning

L'apprentissage profond est une approche connectioniste à l'apprentissage automatique. Elle a pu exploiter la récente production massive de données numériques et l'explosion de la quantité de ressources computationelles qu'a amené ces dernières décennies. La conception d'algorithmes d'apprentissage profond repose sur trois facteurs essentiels: l'expressivité, la recherche efficace de solution, et la généralisation des solutions apprises. Nous explorerons dans cette thèse ces thèmes du point de vue de la reparamétrisation. Plus précisement, le chapitre 3 s'attaque à une conjecture populaire, selon laquelle les énormes réseaux de neurones ont pu apprendre, parmi tant de solutions possibles, celle qui généralise parce que les minima atteints sont plats. Nous démontrons les lacunes profondes de cette conjecture par reparamétrisation sur des exemples simples de modèles populaires, ce qui nous amène à nous interroger sur les interprétations qu'ont superposées précédents chercheurs sur plusieurs phénomènes précédemment observés. Enfin, le chapitre 5 enquête sur le principe d'analyse non-linéaire en composantes indépendantes permettant une formulation analytique de la densité d'un modèle par changement de variable. En particulier, nous proposons l'architecture Real NVP qui utilise de puissantes fonctions paramétriques et aisément inversible que nous pouvons simplement entraîner par descente de gradient. Nous indiquons les points forts et les points faibles de ce genre d'approches et expliquons les algorithmes développés durant ce travail.Deep learning is a connectionist approach to machine learning that successfully harnessed our massive production of data and recent increase in computational resources. In designing efficient deep learning algorithms come three principal themes: expressivity, trainability, and generalizability. We will explore in this thesis these questions through the point of view of reparametrization. In particular, chapter 3 confronts a popular conjecture in deep learning attempting to explain why large neural network are learning among many plausible hypotheses one that generalize: flat minima reached through learning generalize better. We demonstrate the serious limitations this conjecture encounters by reparametrization on several simple and popular models and interrogate the interpretations put on experimental observations. Chapter 5 explores the framework of nonlinear independent components enabling closed form density evaluation through change of variable. More precisely, this work proposes Real NVP, an architecture using expressive and easily invertible computational layers trainable by standard gradient descent algorithms. We showcase its successes and shortcomings in modelling high dimensional data, and explain the techniques developed in that design