A Comparison of Algorithms for Learning Hidden Variables in Normal Graphs

A Bayesian factor graph reduced to normal form consists in the interconnection of diverter units (or equal constraint units) and Single-Input/Single-Output (SISO) blocks. In this framework localized adaptation rules are explicitly derived from a constrained maximum likelihood (ML) formulation and from a minimum KL-divergence criterion using KKT conditions. The learning algorithms are compared with two other updating equations based on a Viterbi-like and on a variational approximation respectively. The performance of the various algorithm is verified on synthetic data sets for various architectures. The objective of this paper is to provide the programmer with explicit algorithms for rapid deployment of Bayesian graphs in the applications.Comment: Submitted for journal publicatio

Towards Building Deep Networks with Bayesian Factor Graphs

We propose a Multi-Layer Network based on the Bayesian framework of the Factor Graphs in Reduced Normal Form (FGrn) applied to a two-dimensional lattice. The Latent Variable Model (LVM) is the basic building block of a quadtree hierarchy built on top of a bottom layer of random variables that represent pixels of an image, a feature map, or more generally a collection of spatially distributed discrete variables. The multi-layer architecture implements a hierarchical data representation that, via belief propagation, can be used for learning and inference. Typical uses are pattern completion, correction and classification. The FGrn paradigm provides great flexibility and modularity and appears as a promising candidate for building deep networks: the system can be easily extended by introducing new and different (in cardinality and in type) variables. Prior knowledge, or supervised information, can be introduced at different scales. The FGrn paradigm provides a handy way for building all kinds of architectures by interconnecting only three types of units: Single Input Single Output (SISO) blocks, Sources and Replicators. The network is designed like a circuit diagram and the belief messages flow bidirectionally in the whole system. The learning algorithms operate only locally within each block. The framework is demonstrated in this paper in a three-layer structure applied to images extracted from a standard data set.Comment: Submitted for journal publicatio

3-D Hand Pose Estimation from Kinect's Point Cloud Using Appearance Matching

We present a novel appearance-based approach for pose estimation of a human hand using the point clouds provided by the low-cost Microsoft Kinect sensor. Both the free-hand case, in which the hand is isolated from the surrounding environment, and the hand-object case, in which the different types of interactions are classified, have been considered. The hand-object case is clearly the most challenging task having to deal with multiple tracks. The approach proposed here belongs to the class of partial pose estimation where the estimated pose in a frame is used for the initialization of the next one. The pose estimation is obtained by applying a modified version of the Iterative Closest Point (ICP) algorithm to synthetic models to obtain the rigid transformation that aligns each model with respect to the input data. The proposed framework uses a "pure" point cloud as provided by the Kinect sensor without any other information such as RGB values or normal vector components. For this reason, the proposed method can also be applied to data obtained from other types of depth sensor, or RGB-D camera

Optimized Realization of Bayesian Networks in Reduced Normal Form using Latent Variable Model

Bayesian networks in their Factor Graph Reduced Normal Form (FGrn) are a powerful paradigm for implementing inference graphs. Unfortunately, the computational and memory costs of these networks may be considerable, even for relatively small networks, and this is one of the main reasons why these structures have often been underused in practice. In this work, through a detailed algorithmic and structural analysis, various solutions for cost reduction are proposed. An online version of the classic batch learning algorithm is also analyzed, showing very similar results (in an unsupervised context); which is essential even if multilevel structures are to be built. The solutions proposed, together with the possible online learning algorithm, are included in a C++ library that is quite efficient, especially if compared to the direct use of the well-known sum-product and Maximum Likelihood (ML) algorithms. The results are discussed with particular reference to a Latent Variable Model (LVM) structure.Comment: 20 pages, 8 figure

Intent Classification in Question-Answering Using LSTM Architectures

Question-answering (QA) is certainly the best known and probably also one of the most complex problem within Natural Language Processing (NLP) and artificial intelligence (AI). Since the complete solution to the problem of finding a generic answer still seems far away, the wisest thing to do is to break down the problem by solving single simpler parts. Assuming a modular approach to the problem, we confine our research to intent classification for an answer, given a question. Through the use of an LSTM network, we show how this type of classification can be approached effectively and efficiently, and how it can be properly used within a basic prototype responder.Comment: Presented at the 2019 Italian Workshop on Neural Networks (WIRN'19) - June 201

An Analysis of Word2Vec for the Italian Language

Word representation is fundamental in NLP tasks, because it is precisely from the coding of semantic closeness between words that it is possible to think of teaching a machine to understand text. Despite the spread of word embedding concepts, still few are the achievements in linguistic contexts other than English. In this work, analysing the semantic capacity of the Word2Vec algorithm, an embedding for the Italian language is produced. Parameter setting such as the number of epochs, the size of the context window and the number of negatively backpropagated samples is explored.Comment: Presented at the 2019 Italian Workshop on Neural Networks (WIRN'19) - June 201

Point-based Path Prediction from Polar Histograms

We address the problem of modeling complex target behavior using a stochastic model that integrates object dynamics, statistics gathered from the environment and semantic knowledge about the scene. The method exploits prior knowledge to build point-wise polar histograms that provide the ability to forecast target motion to the most likely paths. Physical constraints are included in the model through a ray-launching procedure, while semantic scene segmentation is used to provide a coarser representation of the most likely crossable areas. The model is enhanced with statistics extracted from previously observed trajectories and with nearly-constant velocity dynamics. Information regarding the target's destination may also be included steering the prediction to a predetermined area. Our experimental results, validated in comparison to actual targets' trajectories, demonstrate that our approach can be effective in forecasting objects' behavior in structured scenes

A Unifying View of Estimation and Control Using Belief Propagation With Application to Path Planning

The use of estimation techniques on stochastic models to solve control problems is an emerging paradigm that falls under the rubric of Active Inference (AI) and Control as Inference (CAI). In this work, we use probability propagation on factor graphs to show that various algorithms proposed in the literature can be seen as specific composition rules in a factor graph. We show how this unified approach, presented both in probability space and in log of the probability space, provides a very general framework that includes the Sum-product, the Max-product, Dynamic programming and mixed Reward/Entropy criteria-based algorithms. The framework also expands algorithmic design options that lead to new smoother or sharper policy distributions. We propose original recursions such as: a generalized Sum/Max-product algorithm, a Smooth Dynamic programming algorithm and a modified versions of the Reward/Entropy algorithm. The discussion is carried over with reference to a path planning problem where the recursions that arise from various cost functions, although they may appear similar in scope, bear noticeable differences. We provide a comprehensive table of composition rules and a comparison through simulations, first on a synthetic small grid with a single goal with obstacles, and then on a grid extrapolated from a real-world scene with multiple goals and a semantic map

Identification of a founder BRCA2 mutation in Sardinia

Sardinian population can be instrumental in defining the molecular basis of cancer, using the identity-by-descent method. We selected seven Sardinian breast cancer families originating from the northern-central part of the island with multiple affected members in different generations. We genotyped 106 members of the seven families and 20 control nuclear families with markers flanking BRCA2 locus at 13q12–q13. The detection of a common haplotype shared by four out of seven families (60%) suggests the presence of a founder BRCA2 mutation. Direct sequencing of BRCA2 coding exons of patients carrying the shared haplotype, allowed the identification of a ‘frame-shift’ mutation at codon 2867 (8765delAG), causing a premature termination-codon. This mutation was found in breast cancer patients as well as one prostate and one bladder cancer patient with shared haplotype. We then investigated the frequency of 8765delAG in the Sardinian breast cancer population by analysing 270 paraffin-embedded normal tissue samples from breast cancer patients. Five patients (1.7%) were found to be positive for the 8765delAG mutation. Discovery of a founder mutation in Sardinia through the identity-by-descent method demonstrates that this approach can be applied successfully to find mutations either for breast cancer or for other types of tumours. © 2000 Cancer Research Campaig