Search results for " Machine Learning"
showing 10 items of 300 documents
Model identification and local linear convergence of coordinate descent
2020
For composite nonsmooth optimization problems, Forward-Backward algorithm achieves model identification (e.g., support identification for the Lasso) after a finite number of iterations, provided the objective function is regular enough. Results concerning coordinate descent are scarcer and model identification has only been shown for specific estimators, the support-vector machine for instance. In this work, we show that cyclic coordinate descent achieves model identification in finite time for a wide class of functions. In addition, we prove explicit local linear convergence rates for coordinate descent. Extensive experiments on various estimators and on real datasets demonstrate that thes…
Global Sensitivity Analysis of Leaf-Canopy-Atmosphere RTMs: Implications for Biophysical Variables Retrieval from Top-of-Atmosphere Radiance Data.
2019
Knowledge of key variables driving the top of the atmosphere (TOA) radiance over a vegetated surface is an important step to derive biophysical variables from TOA radiance data, e.g., as observed by an optical satellite. Coupled leaf-canopy-atmosphere Radiative Transfer Models (RTMs) allow linking vegetation variables directly to the at-sensor TOA radiance measured. Global Sensitivity Analysis (GSA) of RTMs enables the computation of the total contribution of each input variable to the output variance. We determined the impacts of the leaf-canopy-atmosphere variables into TOA radiance using the GSA to gain insights into retrievable variables. The leaf and canopy RTM PROSAIL was coupled with…
Anomaly detection in wireless sensor networks
2016
Wireless Sensor Network can be defined as a network of integrated sensors responsible for environmental sensing, data processing and communication with other sensors and the base station while consuming low power. Today, WSNs are being used in almost every part of life. The cost effective nature of WSNs is beneficial for environmental monitoring, production facilities and security monitoring. At the same time WSNs are vulnerable to security breaches, attacks and information leakage. Anomaly detection techniques are used to detect such activities over the network that do not conform to the normal behavior of the network communication. Supervised Machine learning approach is one way to detect…
Langage et Apprentissage en Interaction pour des Assistants Numériques Autonomes - Une Approche Développementale
2021
The rapid development of digital assistants (DA) opens the way to new modes of interaction. Some DA allows users to personalise the way they respond to queries, in particular by teaching them new procedures. This work proposes to use machine learning methods to enrich the linguistic and procedural generalisation capabilities of these systems. The challenge is to reconcile rapid learning skills, necessary for a smooth user experience, with a sufficiently large generalisation capacity. Though this is a natural human ability, it remains out-of-reach for artificial systems and this leads us to approach these issues from the perspective of developmental Artificial Intelligence. This work is thus…
Adaptive sparse representation of continuous input for tsetlin machines based on stochastic searching on the line
2021
This paper introduces a novel approach to representing continuous inputs in Tsetlin Machines (TMs). Instead of using one Tsetlin Automaton (TA) for every unique threshold found when Booleanizing continuous input, we employ two Stochastic Searching on the Line (SSL) automata to learn discriminative lower and upper bounds. The two resulting Boolean features are adapted to the rest of the clause by equipping each clause with its own team of SSLs, which update the bounds during the learning process. Two standard TAs finally decide whether to include the resulting features as part of the clause. In this way, only four automata altogether represent one continuous feature (instead of potentially h…
Design of one-year mortality forecast at hospital admission based: a machine learning approach
2019
Background: Palliative care is referred to a set of programs for patients that suffer life-limiting illnesses. These programs aim to guarantee a minimum level of quality of life (QoL) for the last stage of life. They are currently based on clinical evaluation of risk of one-year mortality. Objectives: The main objective of this work is to develop and validate machine-learning based models to predict the exitus of a patient within the next year using data gathered at hospital admission. Methods: Five machine learning techniques were applied in our study to develop machine-learning predictive models: Support Vector Machines, K-neighbors Classifier, Gradient Boosting Classifier, Random Forest …
Secure Sum Outperforms Homomorphic Encryption in (Current) Collaborative Deep Learning
2020
Deep learning (DL) approaches are achieving extraordinary results in a wide range of domains, but often require a massive collection of private data. Hence, methods for training neural networks on the joint data of different data owners, that keep each party's input confidential, are called for. We address a specific setting in federated learning, namely that of deep learning from horizontally distributed data with a limited number of parties, where their vulnerable intermediate results have to be processed in a privacy-preserving manner. This setting can be found in medical and healthcare as well as industrial applications. The predominant scheme for this is based on homomorphic encryption…
Machine learning: A modern approach to pediatric asthma
2021
Among modern methods of statistical and computational analysis, the application of machine learning (ML) to healthcare data has been gaining recognition in helping us understand the heterogeneity of asthma and predicting its progression. In pediatric research, ML approaches may provide rapid advances in uncovering asthma phenotypes with potential translational impact in clinical practice. Also, several accurate models to predict asthma and its progression have been developed using ML. Here, we provide a brief overview of ML approaches recently proposed to characterize pediatric asthma.
Nonlinear Distribution Regression for Remote Sensing Applications
2020
In many remote sensing applications, one wants to estimate variables or parameters of interest from observations. When the target variable is available at a resolution that matches the remote sensing observations, standard algorithms, such as neural networks, random forests, or the Gaussian processes, are readily available to relate the two. However, we often encounter situations where the target variable is only available at the group level, i.e., collectively associated with a number of remotely sensed observations. This problem setting is known in statistics and machine learning as multiple instance learning (MIL) or distribution regression (DR). This article introduces a nonlinear (kern…
Causal Inference in Geoscience and Remote Sensing From Observational Data
2020
Establishing causal relations between random variables from observational data is perhaps the most important challenge in today’s science. In remote sensing and geosciences, this is of special relevance to better understand the earth’s system and the complex interactions between the governing processes. In this paper, we focus on an observational causal inference, and thus, we try to estimate the correct direction of causation using a finite set of empirical data. In addition, we focus on the more complex bivariate scenario that requires strong assumptions and no conditional independence tests can be used. In particular, we explore the framework of (nondeterministic) additive noise models, …