A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms

The benefits of automating design cycles for Bayesian inference-based algorithms are becoming increasingly recognized by the machine learning community. As a result, interest in probabilistic programming frameworks has much increased over the past few years. This paper explores a specific probabilistic programming paradigm, namely message passing in Forney-style factor graphs (FFGs), in the context of automated design of efficient Bayesian signal processing algorithms. To this end, we developed "ForneyLab" (https://github.com/biaslab/ForneyLab.jl) as a Julia toolbox for message passing-based inference in FFGs. We show by example how ForneyLab enables automatic derivation of Bayesian signal processing algorithms, including algorithms for parameter estimation and model comparison. Crucially, due to the modular makeup of the FFG framework, both the model specification and inference methods are readily extensible in ForneyLab. In order to test this framework, we compared variational message passing as implemented by ForneyLab with automatic differentiation variational inference (ADVI) and Monte Carlo methods as implemented by state-of-the-art tools "Edward" and "Stan". In terms of performance, extensibility and stability issues, ForneyLab appears to enjoy an edge relative to its competitors for automated inference in state-space models.Comment: Accepted for publication in the International Journal of Approximate Reasonin

Inductive learning in Shared Neural Multi-Spaces

The learning of rules from examples is of continuing interest to machine learning since it allows generalization from fewer training ex- amples. Inductive Logic Programming (ILP) generates hypothetical rules (clauses) from a knowledge base augmented with (positive and negative) examples. A successful hypothesis entails all positive examples and does not entail any negative example. The Shared Neural Multi-Space (Shared NeMuS) structure encodes first order expressions in a graph suitable for ILP-style learning. This paper explores the NeMuS structure and its re- lationship with the Herbrand Base of a knowledge-base to generate hy- potheses inductively. It is demonstrated that inductive learning driven by the knowledge-base structure can be implementated successfully in the Amao cognitive agent framework, including the learning of recursive hypotheses

PyMVPA: A Unifying Approach to the Analysis of Neuroscientific Data

The Python programming language is steadily increasing in popularity as the language of choice for scientific computing. The ability of this scripting environment to access a huge code base in various languages, combined with its syntactical simplicity, make it the ideal tool for implementing and sharing ideas among scientists from numerous fields and with heterogeneous methodological backgrounds. The recent rise of reciprocal interest between the machine learning (ML) and neuroscience communities is an example of the desire for an inter-disciplinary transfer of computational methods that can benefit from a Python-based framework. For many years, a large fraction of both research communities have addressed, almost independently, very high-dimensional problems with almost completely non-overlapping methods. However, a number of recently published studies that applied ML methods to neuroscience research questions attracted a lot of attention from researchers from both fields, as well as the general public, and showed that this approach can provide novel and fruitful insights into the functioning of the brain. In this article we show how PyMVPA, a specialized Python framework for machine learning based data analysis, can help to facilitate this inter-disciplinary technology transfer by providing a single interface to a wide array of machine learning libraries and neural data-processing methods. We demonstrate the general applicability and power of PyMVPA via analyses of a number of neural data modalities, including fMRI, EEG, MEG, and extracellular recordings

Inductive programming meets the real world

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Neural Network Guided Transfer Learning for Genetic Programming

Programming-by-Example, and code synthesis in general, is a field with many different sub-fields, involving many forms of machine learning and computational logic. With advantages and disadvantages to each, attempts to build effective hybrid solutions would seem to be a promising direction. Transfer Learning (TL) provides a good framework for this, as it allows one of the classic code synthesis techniques, Genetic Programming, to be augmented by past success, to target a particular code synthesis system to the problem domain it is facing. TL allows one type of machine learning algorithm, in this thesis a neural network, to support the core GP process, and combine the strengths of both. This thesis explores the concept of hybrid code synthesis approaches, and then brings the identified strongest elements of each approach together into a single neural network driven Transfer Learning system for Genetic Programming. The TL system operates autonomously, without any human intervention required after the problem set (in example only format) is presented to the system. The thesis first studies how to structure a training corpus for a neural network, across two different experiments, exploring how the constraints placed on a corpus can result in superior training. After this, it studies how GP processes can be guided, to ensure that a hypothetical NN guide would be useful if it could be created and how it can best assist the GP. Finally, it combines the previous studies together into the full end-to-end TL system and tests its performance across two separate problem domain