Extending Stan for Deep Probabilistic Programming

Stan is a popular declarative probabilistic programming language with a high-level syntax for expressing graphical models and beyond. Stan differs by nature from generative probabilistic programming languages like Church, Anglican, or Pyro. This paper presents a comprehensive compilation scheme to compile any Stan model to a generative language and proves its correctness. This sheds a clearer light on the relative expressiveness of different kinds of probabilistic languages and opens the door to combining their mutual strengths. Specifically, we use our compilation scheme to build a compiler from Stan to Pyro and extend Stan with support for explicit variational inference guides and deep probabilistic models. That way, users familiar with Stan get access to new features without having to learn a fundamentally new language. Overall, our paper clarifies the relationship between declarative and generative probabilistic programming languages and is a step towards making deep probabilistic programming easier

Architectural authorship in generative design

The emergence of evolutionary digital design methods, relying on the creative generation of novel forms, has transformed the design process altogether and consequently the role of the architect. These methods are more than the means to aid and enhance the design process or to perfect the representation of finite architectural projects. The architectural design philosophy is gradually transcending to a hybrid of art, engineering, computer programming and biology. Within this framework, the emergence of designs relies on the architect- machine interaction and the authorship that each of the two shares. This work aims to explore the changes within the design process and to define the authorial control of a new breed of architects- programmers and architects-users on architecture and its design representation. For the investigation of these problems, this thesis is to be based on an experiment conducted by the author in order to test the interaction of architects with different digital design methods and their authorial control over the final product. Eventually, the results will be compared and evaluated in relation to the theoretic views. Ultimately, the architect will establish his authorial role

C++ Standard Template Library by template specialized containers

The C++ Standard Template Library is the flagship example for libraries based on the generic programming paradigm. The usage of this library is intended to minimize the number of classical C/C++ errors, but does not warrant bug-free programs. Furthermore, many new kinds of errors may arise from the inaccurate use of the generic programming paradigm, like dereferencing invalid iterators or misunderstanding remove-like algorithms. In this paper we present some typical scenarios that may cause runtime or portability problems. We emit warnings and errors while these risky constructs are used. We also present a general approach to emit "customized" warnings. We support the so-called "believe-me marks" to disable warnings. We present another typical usage of our technique, when classes become deprecated during the software lifecycle

Incremental learning of skills in a task-parameterized Gaussian Mixture Model

The final publication is available at link.springer.comProgramming by demonstration techniques facilitate the programming of robots. Some of them allow the generalization of tasks through parameters, although they require new training when trajectories different from the ones used to estimate the model need to be added. One of the ways to re-train a robot is by incremental learning, which supplies additional information of the task and does not require teaching the whole task again. The present study proposes three techniques to add trajectories to a previously estimated task-parameterized Gaussian mixture model. The first technique estimates a new model by accumulating the new trajectory and the set of trajectories generated using the previous model. The second technique permits adding to the parameters of the existent model those obtained for the new trajectories. The third one updates the model parameters by running a modified version of the Expectation-Maximization algorithm, with the information of the new trajectories. The techniques were evaluated in a simulated task and a real one, and they showed better performance than that of the existent model.Peer ReviewedPostprint (author's final draft

Semantic Robot Programming for Goal-Directed Manipulation in Cluttered Scenes

We present the Semantic Robot Programming (SRP) paradigm as a convergence of robot programming by demonstration and semantic mapping. In SRP, a user can directly program a robot manipulator by demonstrating a snapshot of their intended goal scene in workspace. The robot then parses this goal as a scene graph comprised of object poses and inter-object relations, assuming known object geometries. Task and motion planning is then used to realize the user's goal from an arbitrary initial scene configuration. Even when faced with different initial scene configurations, SRP enables the robot to seamlessly adapt to reach the user's demonstrated goal. For scene perception, we propose the Discriminatively-Informed Generative Estimation of Scenes and Transforms (DIGEST) method to infer the initial and goal states of the world from RGBD images. The efficacy of SRP with DIGEST perception is demonstrated for the task of tray-setting with a Michigan Progress Fetch robot. Scene perception and task execution are evaluated with a public household occlusion dataset and our cluttered scene dataset.Comment: published in ICRA 201

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

Symbolic regression of generative network models

Networks are a powerful abstraction with applicability to a variety of scientific fields. Models explaining their morphology and growth processes permit a wide range of phenomena to be more systematically analysed and understood. At the same time, creating such models is often challenging and requires insights that may be counter-intuitive. Yet there currently exists no general method to arrive at better models. We have developed an approach to automatically detect realistic decentralised network growth models from empirical data, employing a machine learning technique inspired by natural selection and defining a unified formalism to describe such models as computer programs. As the proposed method is completely general and does not assume any pre-existing models, it can be applied "out of the box" to any given network. To validate our approach empirically, we systematically rediscover pre-defined growth laws underlying several canonical network generation models and credible laws for diverse real-world networks. We were able to find programs that are simple enough to lead to an actual understanding of the mechanisms proposed, namely for a simple brain and a social network

A Compilation Target for Probabilistic Programming Languages

Forward inference techniques such as sequential Monte Carlo and particle Markov chain Monte Carlo for probabilistic programming can be implemented in any programming language by creative use of standardized operating system functionality including processes, forking, mutexes, and shared memory. Exploiting this we have defined, developed, and tested a probabilistic programming language intermediate representation language we call probabilistic C, which itself can be compiled to machine code by standard compilers and linked to operating system libraries yielding an efficient, scalable, portable probabilistic programming compilation target. This opens up a new hardware and systems research path for optimizing probabilistic programming systems.Comment: In Proceedings of the 31st International Conference on Machine Learning (ICML), 201