Synthetic Aperture Radar (SAR) image understanding is crucial in remote sensing applications, but it is hindered by its intrinsic noise contamination, called speckle. Sophisticated statistical models, such as the $\mathcal{G}^0$ family of distributions, have been employed to SAR data and many of the current advancements in processing this imagery have been accomplished through extracting information from these models. In this paper, we propose improvements to parameter estimation in $\mathcal{G}^0$ distributions using the Method of Log-Cumulants. First, using Bayesian modeling, we construct that regularly produce reliable roughness estimates under both $\mathcal{G}^0_A$ and $\mathcal{G}^0_I$ models. Second, we make use of an approximation of the Trigamma function to compute the estimated roughness in constant time, making it considerably faster than the existing method for this task. Finally, we show how we can use this method to achieve fast and reliable SAR image understanding based on roughness information.

Spurio Mancini et al. (2022) (SM22 hereafter), the exploration of high-dimensional parameter spaces in particular, developed CosmoPower, a suite of - O(100) parameters and higher - necessary to accurately neural network emulators of cosmological power spectra model the physical signals and their several systematic that replaces the computation of these quantities traditionally contaminants. Sampling the posterior distribution performed with Einstein-Boltzmann solvers such in these high-dimensional spaces represents a significant as the Code for Anisotropies in the Microwave Background computational challenge for Markov Chain Monte Carlo (CAMB, Lewis & Challinor 2011) or the Cosmic (MCMC) algorithms (Roberts et al. 1997; Katafygiotis Linear Anisotropy Solving System (CLASS, Blas et al. & Zuev 2008; Liu 2009), which are traditionally used 2011). In SM22 the authors show how Bayesian inference in cosmological analyses (Lewis & Bridle 2002; Audren of cosmological parameters can be accelerated by several et al. 2013; Brinckmann & Lesgourgues 2019; Torrado & orders of magnitude using CosmoPower; the speed-up becomes Lewis 2021). Gradient-based inference methods, such as particularly relevant when the emulators are employed Hamiltonian Monte Carlo (HMC, Duane et al. 1987; Neal within an inference pipeline that can be run on 1996) and variational inference (VI, Hoffman et al. 2013; graphics processing units (GPUs). Blei et al. 2017), manage to concentrate the sampling An additional advantage in using machine learning in regions of high posterior mass, even in large parameter emulators is that they efficiently provide accurate derivatives spaces, provided one has efficient access to accurate with respect to their input parameters. This is derivatives of the likelihood function with respect to made possible by the automatic differentiation features the model parameters (Brooks et al. 2011; Neal 2011; implemented in the libraries routinely used to build these Zhang et al. 2017; Betancourt 2017).

Predicting the presence of major depressive disorder (MDD) using behavioural and cognitive signals is a highly non-trivial task. The heterogeneous clinical profile of MDD means that any given speech, facial expression and/or observed cognitive pattern may be associated with a unique combination of depressive symptoms. Conventional discriminative machine learning models potentially lack the complexity to robustly model this heterogeneity. Bayesian networks, however, may instead be well-suited to such a scenario. These networks are probabilistic graphical models that efficiently describe the joint probability distribution over a set of random variables by explicitly capturing their conditional dependencies. This framework provides further advantages over standard discriminative modelling by offering the possibility to incorporate expert opinion in the graphical structure of the models, generating explainable model predictions, informing about the uncertainty of predictions, and naturally handling missing data. In this study, we apply a Bayesian framework to capture the relationships between depression, depression symptoms, and features derived from speech, facial expression and cognitive game data collected at thymia.

In this article, I take inspiration from stochastic thermodynamics to derive a problem formulation for online learning in uncertain MDPs while grounded in system dynamics. The system balances the diffusion process with drif dynamics as a way to formulate the explorationexploitation trade-off. To this effect, I make an explicit link between the information entropy and the stochastic dynamics of a system coupled to an environment. I analyze various sources of entropy production: due to the decision-maker's uncertainty about the system-environment interaction characteristics; due to the stochastic nature of system dynamics; and the interaction of the decision maker's knowledge with system dynamics. This analysis provides a framework that can be formulated either as a maximum entropy program to derive efficient policies that balance the exploration and exploitation trade-off, or as a modified cost optimization program that includes informational costs and benefits.

An obstacle to artificial general intelligence is set by continual learning of multiple tasks of different nature. Recently, various heuristic tricks, both from machine learning and from neuroscience angles, were proposed, but they lack a unified theory ground. Here, we focus on continual learning in single-layered and multi-layered neural networks of binary weights. A variational Bayesian learning setting is thus proposed, where the neural networks are trained in a field-space, rather than gradient-ill-defined discrete-weight space, and furthermore, weight uncertainty is naturally incorporated, and modulates synaptic resources among tasks. From a physics perspective, we translate the variational continual learning into Franz-Parisi thermodynamic potential framework, where previous task knowledge acts as a prior and a reference as well. We thus interpret the continual learning of the binary perceptron in a teacher-student setting as a Franz-Parisi potential computation. The learning performance can then be analytically studied with mean-field order parameters, whose predictions coincide with numerical experiments using stochastic gradient descent methods. Based on the variational principle and Gaussian field approximation of internal preactivations in hidden layers, we also derive the learning algorithm considering weight uncertainty, which solves the continual learning with binary weights using multi-layered neural networks, and performs better than the currently available metaplasticity algorithm where binary synapses bear hidden continuous states and the synaptic plasticity is modulated by a heuristic regularization function. Our proposed principled frameworks also connect to elastic weight consolidation, weight-uncertainty modulated learning, and neuroscience inspired metaplasticity, providing a theory-grounded method for the real-world multi-task learning with deep networks.

Model-based reinforcement learning (MBRL) is a sample efficient technique to obtain control policies, yet unavoidable modeling errors often lead performance deterioration. The model in MBRL is often solely fitted to reconstruct dynamics, state observations in particular, while the impact of model error on the policy is not captured by the training objective. This leads to a mismatch between the intended goal of MBRL, enabling good policy and value learning, and the target of the loss function employed in practice, future state prediction. Naive intuition would suggest that value-aware model learning would fix this problem and, indeed, several solutions to this objective mismatch problem have been proposed based on theoretical analysis. However, they tend to be inferior in practice to commonly used maximum likelihood (MLE) based approaches. In this paper we propose the Value-gradient weighted Model Learning (VaGraM), a novel method for value-aware model learning which improves the performance of MBRL in challenging settings, such as small model capacity and the presence of distracting state dimensions. We analyze both MLE and value-aware approaches and demonstrate how they fail to account for exploration and the behavior of function approximation when learning value-aware models and highlight the additional goals that must be met to stabilize optimization in the deep learning setting. We verify our analysis by showing that our loss function is able to achieve high returns on the Mujoco benchmark suite while being more robust than maximum likelihood based approaches.

Neural networks trained on model simulations enable amortized inference: A pre-trained network can be stored and re-used for Bayesian inference on millions of data sets (von This work proposes "jointly amortized neural Krause et al., 2022). Crucially, most previous neural approaches approximation" (JANA) of intractable likelihood have tackled either SM or SBI in isolation, but little functions and posterior densities arising in attention has been paid to learning both tasks simultaneously. Bayesian surrogate modeling and simulation-based To address this gap, we propose JANA ("Jointly Amortized inference. We train three complementary networks Neural Approximation"), a Bayesian neural framework for in an end-to-end fashion: 1) a summary network simultaneously amortized SM and SBI, and show how it enables to compress individual data points, sets, or time novel solutions to challenging downstream tasks like series into informative embedding vectors; 2) a posterior the estimation of marginal and posterior predictive distributions network to learn an amortized approximate (see Figure 1). JANA also presents a major qualitative posterior; and 3) a likelihood network to learn an upgrade to the BayesFlow framework (Radev et al., 2020), amortized approximate likelihood. Their interaction which was originally designed for amortized SBI alone.

Optimal control of general nonlinear systems is a central challenge in automation. Enabled by powerful function approximators, data-driven approaches to control have recently successfully tackled challenging applications. However, such methods often obscure the structure of dynamics and control behind black-box over-parameterized representations, thus limiting our ability to understand closed-loop behavior. This paper adopts a hybrid-system view of nonlinear modeling and control that lends an explicit hierarchical structure to the problem and breaks down complex dynamics into simpler localized units. We consider a sequence modeling paradigm that captures the temporal structure of the data and derive an expectation-maximization (EM) algorithm that automatically decomposes nonlinear dynamics into stochastic piecewise affine models with nonlinear transition boundaries. Furthermore, we show that these time-series models naturally admit a closed-loop extension that we use to extract local polynomial feedback controllers from nonlinear experts via behavioral cloning. Finally, we introduce a novel hybrid relative entropy policy search (Hb-REPS) technique that incorporates the hierarchical nature of hybrid models and optimizes a set of time-invariant piecewise feedback controllers derived from a piecewise polynomial approximation of a global state-value function.

The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment. FMER is a subset of image processing and it is a multidisciplinary topic to analysis. So, it requires familiarity with other topics of Artifactual Intelligence (AI) such as machine learning, digital image processing, psychology and more. So, it is a great opportunity to write a book which covers all of these topics for beginner to professional readers in the field of AI and even without having background of AI. Our goal is to provide a standalone introduction in the field of MFER analysis in the form of theorical descriptions for readers with no background in image processing with reproducible Matlab practical examples. Also, we describe any basic definitions for FMER analysis and MATLAB library which is used in the text, that helps final reader to apply the experiments in the real-world applications. We believe that this book is suitable for students, researchers, and professionals alike, who need to develop practical skills, along with a basic understanding of the field. We expect that, after reading this book, the reader feels comfortable with different key stages such as color and depth image processing, color and depth image representation, classification, machine learning, facial micro-expressions recognition, feature extraction and dimensionality reduction. The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment.

Discount regularization, using a shorter planning horizon when calculating the optimal policy, is a popular choice to restrict planning to a less complex set of policies when estimating an MDP from sparse or noisy data (Jiang et al., 2015). It is commonly understood that discount regularization functions by de-emphasizing or ignoring delayed effects. In this paper, we reveal an alternate view of discount regularization that exposes unintended consequences. We demonstrate that planning under a lower discount factor produces an identical optimal policy to planning using any prior on the transition matrix that has the same distribution for all states and actions. In fact, it functions like a prior with stronger regularization on state-action pairs with more transition data. This leads to poor performance when the transition matrix is estimated from data sets with uneven amounts of data across state-action pairs. Our equivalence theorem leads to an explicit formula to set regularization parameters locally for individual state-action pairs rather than globally. We demonstrate the failures of discount regularization and how we remedy them using our state-action-specific method across simple empirical examples as well as a medical cancer simulator.