We examine an analytic variational inference scheme for the Gaussian Process State Space Model (GPSSM) - a probabilistic model for system identification and time-series modelling. Our approach performs variational inference over both the system states and the transition function. We exploit Markov structure in the true posterior, as well as an inducing point approximation to achieve linear time complexity in the length of the time series. Contrary to previous approaches, no Monte Carlo sampling is required: inference is cast as a deterministic optimisation problem. In a number of experiments, we demonstrate the ability to model non-linear dynamics in the presence of both process and observation noise as well as to impute missing information (e.g. velocities from raw positions through time), to de-noise, and to estimate the underlying dimensionality of the system. Finally, we also introduce a closed-form method for multi-step prediction, and a novel criterion for assessing the quality of our approximate posterior.

Traditional model-based RL relies on hand-specified or learned models of transition dynamics of the environment. These methods are sample efficient and facilitate learning in the real world but fail to generalize to subtle variations in the underlying dynamics, e.g., due to differences in mass, friction, or actuators across robotic agents or across time. We propose using variational inference to learn an explicit latent representation of unknown environment properties that accelerates learning and facilitates generalization on novel environments at test time. We use Online Bayesian Inference of these learned latents to rapidly adapt online to changes in environments without retaining large replay buffers of recent data. Combined with a neural network ensemble that models dynamics and captures uncertainty over dynamics, our approach demonstrates positive transfer during training and online adaptation on the continuous control task HalfCheetah.

Faces play a magnificent role in human robot interaction, as they do in our daily life. The inherent ability of the human mind facilitates us to recognize a person by exploiting various challenges such as bad illumination, occlusions, pose variation etc. which are involved in face recognition. But it is a very complex task in nature to identify a human face by humanoid robots. The recent literatures on face biometric recognition are extremely rich in its application on structured environment for solving human identification problem. But the application of face biometric on mobile robotics is limited for its inability to produce accurate identification in uneven circumstances. The existing face recognition problem has been tackled with our proposed component based fragmented face recognition framework. The proposed framework uses only a subset of the full face such as eyes, nose and mouth to recognize a person. It's less searching cost, encouraging accuracy and ability to handle various challenges of face recognition offers its applicability on humanoid robots. The second problem in face recognition is the face spoofing, in which a face recognition system is not able to distinguish between a person and an imposter (photo/video of the genuine user). The problem will become more detrimental when robots are used as an authenticator. A depth analysis method has been investigated in our research work to test the liveness of imposters to discriminate them from the legitimate users. The implication of the previous earned techniques has been used with respect to criminal identification with NAO robot. An eyewitness can interact with NAO through a user interface. NAO asks several questions about the suspect, such as age, height, her/his facial shape and size etc., and then making a guess about her/his face.

Deep neural networks(NNs) have achieved impressive performance, often exceed human performance on many computer vision tasks. However, one of the most challenging issues that still remains is that NNs are overconfident in their predictions, which can be very harmful when this arises in safety critical applications. In this paper, we show that predictive uncertainty can be efficiently estimated when we incorporate the concept of gradients uncertainty into posterior sampling. The proposed method is tested on two different datasets, MNIST for in-distribution confusing examples and notMNIST for out-of-distribution data. We show that our method is able to efficiently represent predictive uncertainty on both datasets.

Abstract-- In an automated search system, similarity is a key concept in solving a human task. Indeed, human process is usually a natural categorization that underlies many natural abilities such as image recovery, language comprehension, decision making, or pattern recognition. In the image search axis, there are several ways to measure the similarity between images in an image database, to a query image. Image search by content is based on the similarity of the visual characteristics of the images. The distance function used to evaluate the similarity between images depends on the criteria of the search but also on the representation of the characteristics of the image; this is the main idea of the near and fuzzy sets approaches. In this article, we introduce a new category of beta type-2 fuzzy sets for the description of image characteristics as well as the near sets approach for image recovery. Finally, we illustrate our work with examples of image recovery problems used in the real world. I. INTRODUCTION He number of daily-generated images by websites and personal archives are constantly growing. Indeed, the effective management of the rapid expansion of visual information has become a major problem and a necessity for strengthening visual search technique based on visual content [3]. This necessity is behind the emergence of new visual search techniques based on visual content. It has been widely identified that the most efficient and intuitive way to research visual information is based on the properties that are extracted from the images themselves. Researchers from different communities ("Computer Vision" [4], "Database Management", "Man-machine Interface", "Information Retrieval") were attracted by this field. Since then, the search for images by content has developed quite rapidly. The intuitive idea of "any system that analyzes or automatically organizes a set of data or knowledge must use, in one form or another, a similarity operator whose purpose is to establish similarities or the relationships that exist between the manipulated information".

A fuzzy opinion is a Gaussian fuzzy set with the center representing the opinion and the standard deviation representing the uncertainty about the opinion, and a fuzzy opinion network is a connection of a number of fuzzy opinions in a structured way. In this paper, we propose: (a) a top-down hierarchical fuzzy opinion network to model how the opinion of a top leader is penetrated into the members in social organizations, and (b) a bottom-up fuzzy opinion network to model how the opinions of a large number of agents are agglomerated layer-by-layer into a consensus or a few opinions in the social processes such as an election. For the top-down hierarchical fuzzy opinion network, we prove that the opinions of all the agents converge to the leaders opinion, but the uncertainties of the agents in different groups are generally converging to different values. We demonstrate that the speed of convergence is greatly improved by organizing the agents in a hierarchical structure of small groups. For the bottom-up hierarchical fuzzy opinion network, we simulate how a wide spectrum of opinions are negotiating and summarizing with each other in a layer-by-layer fashion in some typical situations.

As machine learning models continue to increase in complexity, collecting large hand-labeled training sets has become one of the biggest roadblocks in practice. Instead, weaker forms of supervision that provide noisier but cheaper labels are often used. However, these weak supervision sources have diverse and unknown accuracies, may output correlated labels, and may label different tasks or apply at different levels of granularity. We propose a framework for integrating and modeling such weak supervision sources by viewing them as labeling different related sub-tasks of a problem, which we refer to as the multi-task weak supervision setting. We show that by solving a matrix completion-style problem, we can recover the accuracies of these multi-task sources given their dependency structure, but without any labeled data, leading to higher-quality supervision for training an end model. Theoretically, we show that the generalization error of models trained with this approach improves with the number of unlabeled data points, and characterize the scaling with respect to the task and dependency structures. On three fine-grained classification problems, we show that our approach leads to average gains of 20.2 points in accuracy over a traditional supervised approach, 6.8 points over a majority vote baseline, and 4.1 points over a previously proposed weak supervision method that models tasks separately.

In this paper we proposed an ultimate theory to solve the multi-target control problem through its introduction to the machine learning framework in automatic driving, which explored the implementation of excellent drivers' knowledge acquisition. Nowadays there exist some core problems that have not been fully realized by the researchers in automatic driving, such as the optimal way to control the multi-target objective functions of energy saving, safe driving, headway distance control and comfort driving, as well as the resolvability of the networks that automatic driving relied on and the high-performance chips like GPU on the complex driving environments. According to these problems, we developed a new theory to map multitarget objective functions in different spaces into the same one and thus introduced a machine learning framework of SDL(Super Deep Learning) for optimal multi-targetcontrol based on knowledge acquisition. We will present in this paper the optimal multi-target control by combining the fuzzy relationship of each multi-target objective function and the implementation of excellent drivers' knowledge acquired by machine learning. Theoretically, the impact of this method will exceed that of the fuzzy control method used in automatic train.

Mathematical Theory of Evidence (MTE), a foundation for reasoning under partial ignorance, is blamed to leave frequencies outside (or aside of) its framework. The seriousness of this accusation is obvious: no experiment may be run to compare the performance of MTE-based models of real world processes against real world data. In this paper we consider this problem from the point of view of conditioning in the MTE. We describe the class of belief functions for which marginal consistency with observed frequencies may be achieved and conditional belief functions are proper belief functions,%\ and deal with implications for (marginal) approximation of general belief functions by this class of belief functions and for inference models in MTE.

Probabilistic models are a critical part of the modern deep learning toolbox - ranging fromgenerative models (VAEs, GANs), sequence to sequence models used in machine translation and speech processing to models over functional spaces (conditional neuralprocesses, neural processes). Given the size and complexity of these models, safely deploying them in applications requires the development of tools to analyze their behavior rigorously and provide some guarantees that these models are consistent with a list of desirable properties or specifications. For example, a machine translation model should produce semantically equivalent outputs for innocuous changes in the input to the model. A functional regression model that is learning a distribution over monotonic functions should predict a larger value at a larger input. Verification of these properties requires a new framework that goes beyond notions of verification studied in deterministic feedforward networks, since requiring worst-case guarantees in probabilistic models is likely to produce conservative orvacuous results. We propose a novel formulation of verification for deep probabilistic models that take in conditioning inputs and sample latent variables in the course of producing an output: We require that the output of the model satisfies a linear constraint with high probability over the sampling of latent variables and for every choice of conditioning input to the model. We show that rigorous lower bounds on the probability that the constraint is satisfied can be obtained efficiently. Experiments with neural processes show that several properties of interest while modeling functional spaces can be modeled within this framework (monotonicity, convexity) and verified efficiently using our algorithms.