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"Topic": models, code, and papers

Towards Interactive, Incremental Programming of ROS Nodes

Dec 15, 2014
Sorin Adam, Ulrik Pagh Schultz

Writing software for controlling robots is a complex task, usually demanding command of many programming languages and requiring significant experimentation. We believe that a bottom-up development process that complements traditional component- and MDSD-based approaches can facilitate experimentation. We propose the use of an internal DSL providing both a tool to interactively create ROS nodes and a behaviour-replacement mechanism to interactively reshape existing ROS nodes by wrapping the external interfaces (the publish/subscribe topics), dynamically controlled using the Python command line interface.

* Presented at DSLRob 2014 (arXiv:cs/1411.7148) 

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Plan Recognition in Stories and in Life

Mar 27, 2013
Eugene Charniak, Robert P. Goldman

Plan recognition does not work the same way in stories and in "real life" (people tend to jump to conclusions more in stories). We present a theory of this, for the particular case of how objects in stories (or in life) influence plan recognition decisions. We provide a Bayesian network formalization of a simple first-order theory of plans, and show how a particular network parameter seems to govern the difference between "life-like" and "story-like" response. We then show why this parameter would be influenced (in the desired way) by a model of speaker (or author) topic selection which assumes that facts in stories are typically "relevant".

* Appears in Proceedings of the Fifth Conference on Uncertainty in Artificial Intelligence (UAI1989) 

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The Natural Gradient by Analogy to Signal Whitening, and Recipes and Tricks for its Use

May 08, 2012
Jascha Sohl-Dickstein

The natural gradient allows for more efficient gradient descent by removing dependencies and biases inherent in a function's parameterization. Several papers present the topic thoroughly and precisely. It remains a very difficult idea to get your head around however. The intent of this note is to provide simple intuition for the natural gradient and its use. We review how an ill conditioned parameter space can undermine learning, introduce the natural gradient by analogy to the more widely understood concept of signal whitening, and present tricks and specific prescriptions for applying the natural gradient to learning problems.


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Applying Personal Knowledge Graphs to Health

Apr 15, 2021
Sola Shirai, Oshani Seneviratne, Deborah L. McGuinness

Knowledge graphs that encapsulate personal health information, or personal health knowledge graphs (PHKG), can help enable personalized health care in knowledge-driven systems. In this paper we provide a short survey of existing work surrounding the emerging paradigm of PHKGs and highlight the major challenges that remain. We find that while some preliminary exploration exists on the topic of personal knowledge graphs, development of PHKGs remains under-explored. A range of challenges surrounding the collection, linkage, and maintenance of personal health knowledge remains to be addressed to fully realize PHKGs.

* Extended abstract for the PHKG2020 workshop 

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Equilibrium Propagation for Complete Directed Neural Networks

Jun 17, 2020
Matilde Tristany Farinha, Sérgio Pequito, Pedro A. Santos, Mário A. T. Figueiredo

Artificial neural networks, one of the most successful approaches to supervised learning, were originally inspired by their biological counterparts. However, the most successful learning algorithm for artificial neural networks, backpropagation, is considered biologically implausible. We contribute to the topic of biologically plausible neuronal learning by building upon and extending the equilibrium propagation learning framework. Specifically, we introduce: a new neuronal dynamics and learning rule for arbitrary network architectures; a sparsity-inducing method able to prune irrelevant connections; a dynamical-systems characterization of the models, using Lyapunov theory.

* 6 pages, 6 images, accepted for ESANN 2020 

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Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS -- a collection of Technical Notes Part 2

Feb 28, 2020
Robin Bloomfield, Gareth Fletcher, Heidy Khlaaf, Philippa Ryan, Shuji Kinoshita, Yoshiki Kinoshit, Makoto Takeyama, Yutaka Matsubara, Peter Popov, Kazuki Imai, Yoshinori Tsutake

This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. This report is Part 2 and discusses: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines.

* Authors of the individual notes are indicated in the text 

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The Historical Significance of Textual Distances

Jun 30, 2018
Ted Underwood

Measuring similarity is a basic task in information retrieval, and now often a building-block for more complex arguments about cultural change. But do measures of textual similarity and distance really correspond to evidence about cultural proximity and differentiation? To explore that question empirically, this paper compares textual and social measures of the similarities between genres of English-language fiction. Existing measures of textual similarity (cosine similarity on tf-idf vectors or topic vectors) are also compared to new strategies that use supervised learning to anchor textual measurement in a social context.

* Preprint of a paper for the 2nd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2018). Code is available at https://github.com/tedunderwood/genredistance or, archivally, at https://zenodo.org/record/1300934 

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EffNet: An Efficient Structure for Convolutional Neural Networks

Jun 05, 2018
Ido Freeman, Lutz Roese-Koerner, Anton Kummert

With the ever increasing application of Convolutional Neural Networks to customer products the need emerges for models to efficiently run on embedded, mobile hardware. Slimmer models have therefore become a hot research topic with various approaches which vary from binary networks to revised convolution layers. We offer our contribution to the latter and propose a novel convolution block which significantly reduces the computational burden while surpassing the current state-of-the-art. Our model, dubbed EffNet, is optimised for models which are slim to begin with and is created to tackle issues in existing models such as MobileNet and ShuffleNet.


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Creating Scalable and Interactive Web Applications Using High Performance Latent Variable Models

Oct 21, 2015
Aaron Q Li, Yuntian Deng, Kublai Jing, Joseph W Robinson

In this project we outline a modularized, scalable system for comparing Amazon products in an interactive and informative way using efficient latent variable models and dynamic visualization. We demonstrate how our system can build on the structure and rich review information of Amazon products in order to provide a fast, multifaceted, and intuitive comparison. By providing a condensed per-topic comparison visualization to the user, we are able to display aggregate information from the entire set of reviews while providing an interface that is at least as compact as the "most helpful reviews" currently displayed by Amazon, yet far more informative.


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Detecting Early Onset of Depression from Social Media Text using Learned Confidence Scores

Nov 03, 2020
Ana-Maria Bucur, Liviu P. Dinu

Computational research on mental health disorders from written texts covers an interdisciplinary area between natural language processing and psychology. A crucial aspect of this problem is prevention and early diagnosis, as suicide resulted from depression being the second leading cause of death for young adults. In this work, we focus on methods for detecting the early onset of depression from social media texts, in particular from Reddit. To that end, we explore the eRisk 2018 dataset and achieve good results with regard to the state of the art by leveraging topic analysis and learned confidence scores to guide the decision process.

* Accepted at Seventh Italian Conference on Computational Linguistics CLiC-it 2020 

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