May 7, 2019

3019 words 15 mins read

Paper Group ANR 88

Practical optimal experiment design with probabilistic programs. Old Content and Modern Tools - Searching Named Entities in a Finnish OCRed Historical Newspaper Collection 1771-1910. Chinese/English mixed Character Segmentation as Semantic Segmentation. CNN-aware Binary Map for General Semantic Segmentation. Automatic verbal aggression detection fo …

Practical optimal experiment design with probabilistic programs


Title	Practical optimal experiment design with probabilistic programs
Authors	Long Ouyang, Michael Henry Tessler, Daniel Ly, Noah Goodman
Abstract	Scientists often run experiments to distinguish competing theories. This requires patience, rigor, and ingenuity - there is often a large space of possible experiments one could run. But we need not comb this space by hand - if we represent our theories as formal models and explicitly declare the space of experiments, we can automate the search for good experiments, looking for those with high expected information gain. Here, we present a general and principled approach to experiment design based on probabilistic programming languages (PPLs). PPLs offer a clean separation between declaring problems and solving them, which means that the scientist can automate experiment design by simply declaring her model and experiment spaces in the PPL without having to worry about the details of calculating information gain. We demonstrate our system in two case studies drawn from cognitive psychology, where we use it to design optimal experiments in the domains of sequence prediction and categorization. We find strong empirical validation that our automatically designed experiments were indeed optimal. We conclude by discussing a number of interesting questions for future research.
Tasks	Probabilistic Programming
Published	2016-08-17
URL	http://arxiv.org/abs/1608.05046v1
PDF	http://arxiv.org/pdf/1608.05046v1.pdf
PWC	https://paperswithcode.com/paper/practical-optimal-experiment-design-with
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Old Content and Modern Tools - Searching Named Entities in a Finnish OCRed Historical Newspaper Collection 1771-1910


Title	Old Content and Modern Tools - Searching Named Entities in a Finnish OCRed Historical Newspaper Collection 1771-1910
Authors	Kimmo Kettunen, Eetu Mäkelä, Teemu Ruokolainen, Juha Kuokkala, Laura Löfberg
Abstract	Named Entity Recognition (NER), search, classification and tagging of names and name like frequent informational elements in texts, has become a standard information extraction procedure for textual data. NER has been applied to many types of texts and different types of entities: newspapers, fiction, historical records, persons, locations, chemical compounds, protein families, animals etc. In general a NER system’s performance is genre and domain dependent and also used entity categories vary (Nadeau and Sekine, 2007). The most general set of named entities is usually some version of three partite categorization of locations, persons and organizations. In this paper we report first large scale trials and evaluation of NER with data out of a digitized Finnish historical newspaper collection Digi. Experiments, results and discussion of this research serve development of the Web collection of historical Finnish newspapers. Digi collection contains 1,960,921 pages of newspaper material from years 1771-1910 both in Finnish and Swedish. We use only material of Finnish documents in our evaluation. The OCRed newspaper collection has lots of OCR errors; its estimated word level correctness is about 70-75 % (Kettunen and P"a"akk"onen, 2016). Our principal NER tagger is a rule-based tagger of Finnish, FiNER, provided by the FIN-CLARIN consortium. We show also results of limited category semantic tagging with tools of the Semantic Computing Research Group (SeCo) of the Aalto University. Three other tools are also evaluated briefly. This research reports first published large scale results of NER in a historical Finnish OCRed newspaper collection. Results of the research supplement NER results of other languages with similar noisy data.
Tasks	Named Entity Recognition, Optical Character Recognition
Published	2016-11-09
URL	http://arxiv.org/abs/1611.02839v1
PDF	http://arxiv.org/pdf/1611.02839v1.pdf
PWC	https://paperswithcode.com/paper/old-content-and-modern-tools-searching-named
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Chinese/English mixed Character Segmentation as Semantic Segmentation


Title	Chinese/English mixed Character Segmentation as Semantic Segmentation
Authors	Huabin Zheng, Jingyu Wang, Zhengjie Huang, Yang Yang, Rong Pan
Abstract	OCR character segmentation for multilingual printed documents is difficult due to the diversity of different linguistic characters. Previous approaches mainly focus on monolingual texts and are not suitable for multilingual-lingual cases. In this work, we particularly tackle the Chinese/English mixed case by reframing it as a semantic segmentation problem. We take advantage of the successful architecture called fully convolutional networks (FCN) in the field of semantic segmentation. Given a wide enough receptive field, FCN can utilize the necessary context around a horizontal position to determinate whether this is a splitting point or not. As a deep neural architecture, FCN can automatically learn useful features from raw text line images. Although trained on synthesized samples with simulated random disturbance, our FCN model generalizes well to real-world samples. The experimental results show that our model significantly outperforms the previous methods.
Tasks	Optical Character Recognition, Semantic Segmentation
Published	2016-11-07
URL	http://arxiv.org/abs/1611.01982v2
PDF	http://arxiv.org/pdf/1611.01982v2.pdf
PWC	https://paperswithcode.com/paper/chineseenglish-mixed-character-segmentation
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CNN-aware Binary Map for General Semantic Segmentation


Title	CNN-aware Binary Map for General Semantic Segmentation
Authors	Mahdyar Ravanbakhsh, Hossein Mousavi, Moin Nabi, Mohammad Rastegari, Carlo Regazzoni
Abstract	In this paper we introduce a novel method for general semantic segmentation that can benefit from general semantics of Convolutional Neural Network (CNN). Our segmentation proposes visually and semantically coherent image segments. We use binary encoding of CNN features to overcome the difficulty of the clustering on the high-dimensional CNN feature space. These binary codes are very robust against noise and non-semantic changes in the image. These binary encoding can be embedded into the CNN as an extra layer at the end of the network. This results in real-time segmentation. To the best of our knowledge our method is the first attempt on general semantic image segmentation using CNN. All the previous papers were limited to few number of category of the images (e.g. PASCAL VOC). Experiments show that our segmentation algorithm outperform the state-of-the-art non-semantic segmentation methods by large margin.
Tasks	Semantic Segmentation
Published	2016-09-29
URL	http://arxiv.org/abs/1609.09220v1
PDF	http://arxiv.org/pdf/1609.09220v1.pdf
PWC	https://paperswithcode.com/paper/cnn-aware-binary-map-for-general-semantic
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Automatic verbal aggression detection for Russian and American imageboards


Title	Automatic verbal aggression detection for Russian and American imageboards
Authors	Denis Gordeev
Abstract	The problem of aggression for Internet communities is rampant. Anonymous forums usually called imageboards are notorious for their aggressive and deviant behaviour even in comparison with other Internet communities. This study is aimed at studying ways of automatic detection of verbal expression of aggression for the most popular American (4chan.org) and Russian (2ch.hk) imageboards. A set of 1,802,789 messages was used for this study. The machine learning algorithm word2vec was applied to detect the state of aggression. A decent result is obtained for English (88%), the results for Russian are yet to be improved.
Tasks
Published	2016-04-22
URL	http://arxiv.org/abs/1604.06648v1
PDF	http://arxiv.org/pdf/1604.06648v1.pdf
PWC	https://paperswithcode.com/paper/automatic-verbal-aggression-detection-for
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Reinforcement learning based local search for grouping problems: A case study on graph coloring


Title	Reinforcement learning based local search for grouping problems: A case study on graph coloring
Authors	Yangming Zhou, Jin-Kao Hao, Béatrice Duval
Abstract	Grouping problems aim to partition a set of items into multiple mutually disjoint subsets according to some specific criterion and constraints. Grouping problems cover a large class of important combinatorial optimization problems that are generally computationally difficult. In this paper, we propose a general solution approach for grouping problems, i.e., reinforcement learning based local search (RLS), which combines reinforcement learning techniques with descent-based local search. The viability of the proposed approach is verified on a well-known representative grouping problem (graph coloring) where a very simple descent-based coloring algorithm is applied. Experimental studies on popular DIMACS and COLOR02 benchmark graphs indicate that RLS achieves competitive performances compared to a number of well-known coloring algorithms.
Tasks	Combinatorial Optimization
Published	2016-04-01
URL	http://arxiv.org/abs/1604.00377v1
PDF	http://arxiv.org/pdf/1604.00377v1.pdf
PWC	https://paperswithcode.com/paper/reinforcement-learning-based-local-search-for
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Inertial Regularization and Selection (IRS): Sequential Regression in High-Dimension and Sparsity


Title	Inertial Regularization and Selection (IRS): Sequential Regression in High-Dimension and Sparsity
Authors	Chitta Ranjan, Samaneh Ebrahimi, Kamran Paynabar
Abstract	In this paper, we develop a new sequential regression modeling approach for data streams. Data streams are commonly found around us, e.g in a retail enterprise sales data is continuously collected every day. A demand forecasting model is an important outcome from the data that needs to be continuously updated with the new incoming data. The main challenge in such modeling arises when there is a) high dimensional and sparsity, b) need for an adaptive use of prior knowledge, and/or c) structural changes in the system. The proposed approach addresses these challenges by incorporating an adaptive L1-penalty and inertia terms in the loss function, and thus called Inertial Regularization and Selection (IRS). The former term performs model selection to handle the first challenge while the latter is shown to address the last two challenges. A recursive estimation algorithm is developed, and shown to outperform the commonly used state-space models, such as Kalman Filters, in experimental studies and real data.
Tasks	Model Selection
Published	2016-10-23
URL	http://arxiv.org/abs/1610.07216v2
PDF	http://arxiv.org/pdf/1610.07216v2.pdf
PWC	https://paperswithcode.com/paper/inertial-regularization-and-selection-irs
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An Evolutionary Strategy based on Partial Imitation for Solving Optimization Problems


Title	An Evolutionary Strategy based on Partial Imitation for Solving Optimization Problems
Authors	Marco Alberto Javarone
Abstract	In this work we introduce an evolutionary strategy to solve combinatorial optimization tasks, i.e. problems characterized by a discrete search space. In particular, we focus on the Traveling Salesman Problem (TSP), i.e. a famous problem whose search space grows exponentially, increasing the number of cities, up to becoming NP-hard. The solutions of the TSP can be codified by arrays of cities, and can be evaluated by fitness, computed according to a cost function (e.g. the length of a path). Our method is based on the evolution of an agent population by means of an imitative mechanism, we define `partial imitation'. In particular, agents receive a random solution and then, interacting among themselves, may imitate the solutions of agents with a higher fitness. Since the imitation mechanism is only partial, agents copy only one entry (randomly chosen) of another array (i.e. solution). In doing so, the population converges towards a shared solution, behaving like a spin system undergoing a cooling process, i.e. driven towards an ordered phase. We highlight that the adopted` partial imitation’ mechanism allows the population to generate solutions over time, before reaching the final equilibrium. Results of numerical simulations show that our method is able to find, in a finite time, both optimal and suboptimal solutions, depending on the size of the considered search space.
Tasks	Combinatorial Optimization
Published	2016-02-12
URL	http://arxiv.org/abs/1602.04186v2
PDF	http://arxiv.org/pdf/1602.04186v2.pdf
PWC	https://paperswithcode.com/paper/an-evolutionary-strategy-based-on-partial
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Unconstrained Two-parallel-plane Model for Focused Plenoptic Cameras Calibration


Title	Unconstrained Two-parallel-plane Model for Focused Plenoptic Cameras Calibration
Authors	Chunping Zhang, Zhe Ji, Qing Wang
Abstract	The plenoptic camera can capture both angular and spatial information of the rays, enabling 3D reconstruction by single exposure. The geometry of the recovered scene structure is affected by the calibration of the plenoptic camera significantly. In this paper, we propose a novel unconstrained two-parallel-plane (TPP) model with 7 parameters to describe a 4D light field. By reconstructing scene points from ray-ray association, a 3D projective transformation is deduced to establish the relationship between the scene structure and the TPP parameters. Based on the transformation, we simplify the focused plenoptic camera as a TPP model and calibrate its intrinsic parameters. Our calibration method includes a close-form solution and a nonlinear optimization by minimizing re-projection error. Experiments on both simulated data and real scene data verify the performance of the calibration on the focused plenoptic camera.
Tasks	3D Reconstruction, Calibration
Published	2016-08-16
URL	http://arxiv.org/abs/1608.04509v1
PDF	http://arxiv.org/pdf/1608.04509v1.pdf
PWC	https://paperswithcode.com/paper/unconstrained-two-parallel-plane-model-for
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On the Geometric Ergodicity of Hamiltonian Monte Carlo


Title	On the Geometric Ergodicity of Hamiltonian Monte Carlo
Authors	Samuel Livingstone, Michael Betancourt, Simon Byrne, Mark Girolami
Abstract	We establish general conditions under which Markov chains produced by the Hamiltonian Monte Carlo method will and will not be geometrically ergodic. We consider implementations with both position-independent and position-dependent integration times. In the former case we find that the conditions for geometric ergodicity are essentially a gradient of the log-density which asymptotically points towards the centre of the space and grows no faster than linearly. In an idealised scenario in which the integration time is allowed to change in different regions of the space, we show that geometric ergodicity can be recovered for a much broader class of tail behaviours, leading to some guidelines for the choice of this free parameter in practice.
Tasks
Published	2016-01-29
URL	http://arxiv.org/abs/1601.08057v4
PDF	http://arxiv.org/pdf/1601.08057v4.pdf
PWC	https://paperswithcode.com/paper/on-the-geometric-ergodicity-of-hamiltonian
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Randomised Algorithm for Feature Selection and Classification


Title	Randomised Algorithm for Feature Selection and Classification
Authors	Aida Brankovic, Alessandro Falsone, Maria Prandini, Luigi Piroddi
Abstract	We here introduce a novel classification approach adopted from the nonlinear model identification framework, which jointly addresses the feature selection and classifier design tasks. The classifier is constructed as a polynomial expansion of the original attributes and a model structure selection process is applied to find the relevant terms of the model. The selection method progressively refines a probability distribution defined on the model structure space, by extracting sample models from the current distribution and using the aggregate information obtained from the evaluation of the population of models to reinforce the probability of extracting the most important terms. To reduce the initial search space, distance correlation filtering can be applied as a preprocessing technique. The proposed method is evaluated and compared to other well-known feature selection and classification methods on standard benchmark classification problems. The results show the effectiveness of the proposed method with respect to competitor methods both in terms of classification accuracy and model complexity. The obtained models have a simple structure, easily amenable to interpretation and analysis.
Tasks	Feature Selection
Published	2016-07-28
URL	http://arxiv.org/abs/1607.08400v1
PDF	http://arxiv.org/pdf/1607.08400v1.pdf
PWC	https://paperswithcode.com/paper/randomised-algorithm-for-feature-selection
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On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models


Title	On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models
Authors	Yining Wang, Adams Wei Yu, Aarti Singh
Abstract	We derive computationally tractable methods to select a small subset of experiment settings from a large pool of given design points. The primary focus is on linear regression models, while the technique extends to generalized linear models and Delta’s method (estimating functions of linear regression models) as well. The algorithms are based on a continuous relaxation of an otherwise intractable combinatorial optimization problem, with sampling or greedy procedures as post-processing steps. Formal approximation guarantees are established for both algorithms, and numerical results on both synthetic and real-world data confirm the effectiveness of the proposed methods.
Tasks	Combinatorial Optimization
Published	2016-01-09
URL	http://arxiv.org/abs/1601.02068v6
PDF	http://arxiv.org/pdf/1601.02068v6.pdf
PWC	https://paperswithcode.com/paper/on-computationally-tractable-selection-of
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Contradiction Detection for Rumorous Claims


Title	Contradiction Detection for Rumorous Claims
Authors	Piroska Lendvai, Uwe D. Reichel
Abstract	The utilization of social media material in journalistic workflows is increasing, demanding automated methods for the identification of mis- and disinformation. Since textual contradiction across social media posts can be a signal of rumorousness, we seek to model how claims in Twitter posts are being textually contradicted. We identify two different contexts in which contradiction emerges: its broader form can be observed across independently posted tweets and its more specific form in threaded conversations. We define how the two scenarios differ in terms of central elements of argumentation: claims and conversation structure. We design and evaluate models for the two scenarios uniformly as 3-way Recognizing Textual Entailment tasks in order to represent claims and conversation structure implicitly in a generic inference model, while previous studies used explicit or no representation of these properties. To address noisy text, our classifiers use simple similarity features derived from the string and part-of-speech level. Corpus statistics reveal distribution differences for these features in contradictory as opposed to non-contradictory tweet relations, and the classifiers yield state of the art performance.
Tasks	Natural Language Inference
Published	2016-11-08
URL	http://arxiv.org/abs/1611.02588v2
PDF	http://arxiv.org/pdf/1611.02588v2.pdf
PWC	https://paperswithcode.com/paper/contradiction-detection-for-rumorous-claims
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Adaptive Online Sequential ELM for Concept Drift Tackling


Title	Adaptive Online Sequential ELM for Concept Drift Tackling
Authors	Arif Budiman, Mohamad Ivan Fanany, Chan Basaruddin
Abstract	A machine learning method needs to adapt to over time changes in the environment. Such changes are known as concept drift. In this paper, we propose concept drift tackling method as an enhancement of Online Sequential Extreme Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by adding adaptive capability for classification and regression problem. The scheme is named as adaptive OS-ELM (AOS-ELM). It is a single classifier scheme that works well to handle real drift, virtual drift, and hybrid drift. The AOS-ELM also works well for sudden drift and recurrent context change type. The scheme is a simple unified method implemented in simple lines of code. We evaluated AOS-ELM on regression and classification problem by using concept drift public data set (SEA and STAGGER) and other public data sets such as MNIST, USPS, and IDS. Experiments show that our method gives higher kappa value compared to the multiclassifier ELM ensemble. Even though AOS-ELM in practice does not need hidden nodes increase, we address some issues related to the increasing of the hidden nodes such as error condition and rank values. We propose taking the rank of the pseudoinverse matrix as an indicator parameter to detect underfitting condition.
Tasks
Published	2016-10-06
URL	http://arxiv.org/abs/1610.01922v1
PDF	http://arxiv.org/pdf/1610.01922v1.pdf
PWC	https://paperswithcode.com/paper/adaptive-online-sequential-elm-for-concept
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Evolutionary Synthesis of Deep Neural Networks via Synaptic Cluster-driven Genetic Encoding


Title	Evolutionary Synthesis of Deep Neural Networks via Synaptic Cluster-driven Genetic Encoding
Authors	Mohammad Javad Shafiee, Alexander Wong
Abstract	There has been significant recent interest towards achieving highly efficient deep neural network architectures. A promising paradigm for achieving this is the concept of evolutionary deep intelligence, which attempts to mimic biological evolution processes to synthesize highly-efficient deep neural networks over successive generations. An important aspect of evolutionary deep intelligence is the genetic encoding scheme used to mimic heredity, which can have a significant impact on the quality of offspring deep neural networks. Motivated by the neurobiological phenomenon of synaptic clustering, we introduce a new genetic encoding scheme where synaptic probability is driven towards the formation of a highly sparse set of synaptic clusters. Experimental results for the task of image classification demonstrated that the synthesized offspring networks using this synaptic cluster-driven genetic encoding scheme can achieve state-of-the-art performance while having network architectures that are not only significantly more efficient (with a ~125-fold decrease in synapses for MNIST) compared to the original ancestor network, but also tailored for GPU-accelerated machine learning applications.
Tasks	Image Classification
Published	2016-09-06
URL	http://arxiv.org/abs/1609.01360v2
PDF	http://arxiv.org/pdf/1609.01360v2.pdf
PWC	https://paperswithcode.com/paper/evolutionary-synthesis-of-deep-neural
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