October 17, 2019

2981 words 14 mins read

Paper Group ANR 851

A Generative Approach to Zero-Shot and Few-Shot Action Recognition. An Information-Theoretic Analysis for Thompson Sampling with Many Actions. ISIS at its apogee: the Arabic discourse on Twitter and what we can learn from that about ISIS support and Foreign Fighters. Joint 3D Reconstruction of a Static Scene and Moving Objects. Probabilistic Planni …

A Generative Approach to Zero-Shot and Few-Shot Action Recognition


Title	A Generative Approach to Zero-Shot and Few-Shot Action Recognition
Authors	Ashish Mishra, Vinay Kumar Verma, M Shiva Krishna Reddy, Arulkumar S, Piyush Rai, Anurag Mittal
Abstract	We present a generative framework for zero-shot action recognition where some of the possible action classes do not occur in the training data. Our approach is based on modeling each action class using a probability distribution whose parameters are functions of the attribute vector representing that action class. In particular, we assume that the distribution parameters for any action class in the visual space can be expressed as a linear combination of a set of basis vectors where the combination weights are given by the attributes of the action class. These basis vectors can be learned solely using labeled data from the known (i.e., previously seen) action classes, and can then be used to predict the parameters of the probability distributions of unseen action classes. We consider two settings: (1) Inductive setting, where we use only the labeled examples of the seen action classes to predict the unseen action class parameters; and (2) Transductive setting which further leverages unlabeled data from the unseen action classes. Our framework also naturally extends to few-shot action recognition where a few labeled examples from unseen classes are available. Our experiments on benchmark datasets (UCF101, HMDB51 and Olympic) show significant performance improvements as compared to various baselines, in both standard zero-shot (disjoint seen and unseen classes) and generalized zero-shot learning settings.
Tasks	Temporal Action Localization, Zero-Shot Learning
Published	2018-01-27
URL	http://arxiv.org/abs/1801.09086v1
PDF	http://arxiv.org/pdf/1801.09086v1.pdf
PWC	https://paperswithcode.com/paper/a-generative-approach-to-zero-shot-and-few
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An Information-Theoretic Analysis for Thompson Sampling with Many Actions


Title	An Information-Theoretic Analysis for Thompson Sampling with Many Actions
Authors	Shi Dong, Benjamin Van Roy
Abstract	Information-theoretic Bayesian regret bounds of Russo and Van Roy capture the dependence of regret on prior uncertainty. However, this dependence is through entropy, which can become arbitrarily large as the number of actions increases. We establish new bounds that depend instead on a notion of rate-distortion. Among other things, this allows us to recover through information-theoretic arguments a near-optimal bound for the linear bandit. We also offer a bound for the logistic bandit that dramatically improves on the best previously available, though this bound depends on an information-theoretic statistic that we have only been able to quantify via computation.
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Published	2018-05-30
URL	http://arxiv.org/abs/1805.11845v3
PDF	http://arxiv.org/pdf/1805.11845v3.pdf
PWC	https://paperswithcode.com/paper/an-information-theoretic-analysis-for
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ISIS at its apogee: the Arabic discourse on Twitter and what we can learn from that about ISIS support and Foreign Fighters


Title	ISIS at its apogee: the Arabic discourse on Twitter and what we can learn from that about ISIS support and Foreign Fighters
Authors	A. Ceron, L. Curini, S. M. Iacus
Abstract	We analyze 26.2 million comments published in Arabic language on Twitter, from July 2014 to January 2015, when ISIS’ strength reached its peak and the group was prominently expanding the territorial area under its control. By doing that, we are able to measure the share of support and aversion toward the Islamic State within the online Arab communities. We then investigate two specific topics. First, by exploiting the time-granularity of the tweets, we link the opinions with daily events to understand the main determinants of the changing trend in support toward ISIS. Second, by taking advantage of the geographical locations of tweets, we explore the relationship between online opinions across countries and the number of foreign fighters joining ISIS.
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Published	2018-03-14
URL	http://arxiv.org/abs/1804.04059v2
PDF	http://arxiv.org/pdf/1804.04059v2.pdf
PWC	https://paperswithcode.com/paper/isis-at-its-apogee-the-arabic-discourse-on
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Joint 3D Reconstruction of a Static Scene and Moving Objects


Title	Joint 3D Reconstruction of a Static Scene and Moving Objects
Authors	Sergio Caccamo, Esra Ataer-Cansizoglu, Yuichi Taguchi
Abstract	We present a technique for simultaneous 3D reconstruction of static regions and rigidly moving objects in a scene. An RGB-D frame is represented as a collection of features, which are points and planes. We classify the features into static and dynamic regions and grow separate maps, static and object maps, for each of them. To robustly classify the features in each frame, we fuse multiple RANSAC-based registration results obtained by registering different groups of the features to different maps, including (1) all the features to the static map, (2) all the features to each object map, and (3) subsets of the features, each forming a segment, to each object map. This multi-group registration approach is designed to overcome the following challenges: scenes can be dominated by static regions, making object tracking more difficult; and moving object might have larger pose variation between frames compared to the static regions. We show qualitative results from indoor scenes with objects in various shapes. The technique enables on-the-fly object model generation to be used for robotic manipulation.
Tasks	3D Reconstruction, Object Tracking
Published	2018-02-13
URL	http://arxiv.org/abs/1802.04738v1
PDF	http://arxiv.org/pdf/1802.04738v1.pdf
PWC	https://paperswithcode.com/paper/joint-3d-reconstruction-of-a-static-scene-and
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Probabilistic Planning by Probabilistic Programming


Title	Probabilistic Planning by Probabilistic Programming
Authors	Vaishak Belle
Abstract	Automated planning is a major topic of research in artificial intelligence, and enjoys a long and distinguished history. The classical paradigm assumes a distinguished initial state, comprised of a set of facts, and is defined over a set of actions which change that state in one way or another. Planning in many real-world settings, however, is much more involved: an agent’s knowledge is almost never simply a set of facts that are true, and actions that the agent intends to execute never operate the way they are supposed to. Thus, probabilistic planning attempts to incorporate stochastic models directly into the planning process. In this article, we briefly report on probabilistic planning through the lens of probabilistic programming: a programming paradigm that aims to ease the specification of structured probability distributions. In particular, we provide an overview of the features of two systems, HYPE and ALLEGRO, which emphasise different strengths of probabilistic programming that are particularly useful for complex modelling issues raised in probabilistic planning. Among other things, with these systems, one can instantiate planning problems with growing and shrinking state spaces, discrete and continuous probability distributions, and non-unique prior distributions in a first-order setting.
Tasks	Probabilistic Programming
Published	2018-01-25
URL	http://arxiv.org/abs/1801.08365v1
PDF	http://arxiv.org/pdf/1801.08365v1.pdf
PWC	https://paperswithcode.com/paper/probabilistic-planning-by-probabilistic
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CRH: A Simple Benchmark Approach to Continuous Hashing


Title	CRH: A Simple Benchmark Approach to Continuous Hashing
Authors	Miao Cheng, Ah Chung Tsoi
Abstract	In recent years, the distinctive advancement of handling huge data promotes the evolution of ubiquitous computing and analysis technologies. With the constantly upward system burden and computational complexity, adaptive coding has been a fascinating topic for pattern analysis, with outstanding performance. In this work, a continuous hashing method, termed continuous random hashing (CRH), is proposed to encode sequential data stream, while ignorance of previously hashing knowledge is possible. Instead, a random selection idea is adopted to adaptively approximate the differential encoding patterns of data stream, e.g., streaming media, and iteration is avoided for stepwise learning. Experimental results demonstrate our method is able to provide outstanding performance, as a benchmark approach to continuous hashing.
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Published	2018-10-10
URL	http://arxiv.org/abs/1810.05730v1
PDF	http://arxiv.org/pdf/1810.05730v1.pdf
PWC	https://paperswithcode.com/paper/crh-a-simple-benchmark-approach-to-continuous
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How agents see things: On visual representations in an emergent language game


Title	How agents see things: On visual representations in an emergent language game
Authors	Diane Bouchacourt, Marco Baroni
Abstract	There is growing interest in the language developed by agents interacting in emergent-communication settings. Earlier studies have focused on the agents’ symbol usage, rather than on their representation of visual input. In this paper, we consider the referential games of Lazaridou et al. (2017) and investigate the representations the agents develop during their evolving interaction. We find that the agents establish successful communication by inducing visual representations that almost perfectly align with each other, but, surprisingly, do not capture the conceptual properties of the objects depicted in the input images. We conclude that, if we are interested in developing language-like communication systems, we must pay more attention to the visual semantics agents associate to the symbols they use.
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Published	2018-08-31
URL	http://arxiv.org/abs/1808.10696v2
PDF	http://arxiv.org/pdf/1808.10696v2.pdf
PWC	https://paperswithcode.com/paper/how-agents-see-things-on-visual
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Assessing robustness of radiomic features by image perturbation


Title	Assessing robustness of radiomic features by image perturbation
Authors	Alex Zwanenburg, Stefan Leger, Linda Agolli, Karoline Pilz, Esther G. C. Troost, Christian Richter, Steffen Löck
Abstract	Image features need to be robust against differences in positioning, acquisition and segmentation to ensure reproducibility. Radiomic models that only include robust features can be used to analyse new images, whereas models with non-robust features may fail to predict the outcome of interest accurately. Test-retest imaging is recommended to assess robustness, but may not be available for the phenotype of interest. We therefore investigated 18 methods to determine feature robustness based on image perturbations. Test-retest and perturbation robustness were compared for 4032 features that were computed from the gross tumour volume in two cohorts with computed tomography imaging: I) 31 non-small-cell lung cancer (NSCLC) patients; II): 19 head-and-neck squamous cell carcinoma (HNSCC) patients. Robustness was measured using the intraclass correlation coefficient (1,1) (ICC). Features with ICC$\geq0.90$ were considered robust. The NSCLC cohort contained more robust features for test-retest imaging than the HNSCC cohort ($73.5%$ vs. $34.0%$). A perturbation chain consisting of noise addition, affine translation, volume growth/shrinkage and supervoxel-based contour randomisation identified the fewest false positive robust features (NSCLC: $3.3%$; HNSCC: $10.0%$). Thus, this perturbation chain may be used to assess feature robustness.
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Published	2018-06-18
URL	http://arxiv.org/abs/1806.06719v1
PDF	http://arxiv.org/pdf/1806.06719v1.pdf
PWC	https://paperswithcode.com/paper/assessing-robustness-of-radiomic-features-by
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Optimization over Continuous and Multi-dimensional Decisions with Observational Data


Title	Optimization over Continuous and Multi-dimensional Decisions with Observational Data
Authors	Dimitris Bertsimas, Christopher McCord
Abstract	We consider the optimization of an uncertain objective over continuous and multi-dimensional decision spaces in problems in which we are only provided with observational data. We propose a novel algorithmic framework that is tractable, asymptotically consistent, and superior to comparable methods on example problems. Our approach leverages predictive machine learning methods and incorporates information on the uncertainty of the predicted outcomes for the purpose of prescribing decisions. We demonstrate the efficacy of our method on examples involving both synthetic and real data sets.
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Published	2018-07-11
URL	http://arxiv.org/abs/1807.04183v2
PDF	http://arxiv.org/pdf/1807.04183v2.pdf
PWC	https://paperswithcode.com/paper/optimization-over-continuous-and-multi
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DeepWriting: Making Digital Ink Editable via Deep Generative Modeling


Title	DeepWriting: Making Digital Ink Editable via Deep Generative Modeling
Authors	Emre Aksan, Fabrizio Pece, Otmar Hilliges
Abstract	Digital ink promises to combine the flexibility and aesthetics of handwriting and the ability to process, search and edit digital text. Character recognition converts handwritten text into a digital representation, albeit at the cost of losing personalized appearance due to the technical difficulties of separating the interwoven components of content and style. In this paper, we propose a novel generative neural network architecture that is capable of disentangling style from content and thus making digital ink editable. Our model can synthesize arbitrary text, while giving users control over the visual appearance (style). For example, allowing for style transfer without changing the content, editing of digital ink at the word level and other application scenarios such as spell-checking and correction of handwritten text. We furthermore contribute a new dataset of handwritten text with fine-grained annotations at the character level and report results from an initial user evaluation.
Tasks	Style Transfer
Published	2018-01-25
URL	http://arxiv.org/abs/1801.08379v1
PDF	http://arxiv.org/pdf/1801.08379v1.pdf
PWC	https://paperswithcode.com/paper/deepwriting-making-digital-ink-editable-via
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Speeding Up Budgeted Stochastic Gradient Descent SVM Training with Precomputed Golden Section Search


Title	Speeding Up Budgeted Stochastic Gradient Descent SVM Training with Precomputed Golden Section Search
Authors	Tobias Glasmachers, Sahar Qaadan
Abstract	Limiting the model size of a kernel support vector machine to a pre-defined budget is a well-established technique that allows to scale SVM learning and prediction to large-scale data. Its core addition to simple stochastic gradient training is budget maintenance through merging of support vectors. This requires solving an inner optimization problem with an iterative method many times per gradient step. In this paper we replace the iterative procedure with a fast lookup. We manage to reduce the merging time by up to 65% and the total training time by 44% without any loss of accuracy.
Tasks
Published	2018-06-26
URL	http://arxiv.org/abs/1806.10180v1
PDF	http://arxiv.org/pdf/1806.10180v1.pdf
PWC	https://paperswithcode.com/paper/speeding-up-budgeted-stochastic-gradient
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Evolutionary n-level Hypergraph Partitioning with Adaptive Coarsening


Title	Evolutionary n-level Hypergraph Partitioning with Adaptive Coarsening
Authors	Richard J. Preen, Jim Smith
Abstract	Hypergraph partitioning is an NP-hard problem that occurs in many computer science applications where it is necessary to reduce large problems into a number of smaller, computationally tractable sub-problems. Current techniques use a multilevel approach wherein an initial partitioning is performed after compressing the hypergraph to a predetermined level. This level is typically chosen to produce very coarse hypergraphs in which heuristic algorithms are fast and effective. This article presents a novel memetic algorithm which remains effective on larger initial hypergraphs. This enables the exploitation of information that can be lost during coarsening and results in improved final solution quality. We use this algorithm to present an empirical analysis of the space of possible initial hypergraphs in terms of its searchability at different levels of coarsening. We find that the best results arise at coarsening levels unique to each hypergraph. Based on this, we introduce an adaptive scheme that stops coarsening when the rate of information loss in a hypergraph becomes non-linear and show that this produces further improvements. The results show that we have identified a valuable role for evolutionary algorithms within the current state-of-the-art hypergraph partitioning framework.
Tasks	hypergraph partitioning
Published	2018-03-25
URL	http://arxiv.org/abs/1803.09258v3
PDF	http://arxiv.org/pdf/1803.09258v3.pdf
PWC	https://paperswithcode.com/paper/evolutionary-n-level-hypergraph-partitioning
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Progressive Ensemble Networks for Zero-Shot Recognition


Title	Progressive Ensemble Networks for Zero-Shot Recognition
Authors	Meng Ye, Yuhong Guo
Abstract	Despite the advancement of supervised image recognition algorithms, their dependence on the availability of labeled data and the rapid expansion of image categories raise the significant challenge of zero-shot learning. Zero-shot learning (ZSL) aims to transfer knowledge from labeled classes into unlabeled classes to reduce human labeling effort. In this paper, we propose a novel progressive ensemble network model with multiple projected label embeddings to address zero-shot image recognition. The ensemble network is built by learning multiple image classification functions with a shared feature extraction network but different label embedding representations, which enhance the diversity of the classifiers and facilitate information transfer to unlabeled classes. A progressive training framework is then deployed to gradually label the most confident images in each unlabeled class with predicted pseudo-labels and update the ensemble network with the training data augmented by the pseudo-labels. The proposed model performs training on both labeled and unlabeled data. It can naturally bridge the domain shift problem in visual appearances and be extended to the generalized zero-shot learning scenario. We conduct experiments on multiple ZSL datasets and the empirical results demonstrate the efficacy of the proposed model.
Tasks	Image Classification, Zero-Shot Learning
Published	2018-05-18
URL	http://arxiv.org/abs/1805.07473v2
PDF	http://arxiv.org/pdf/1805.07473v2.pdf
PWC	https://paperswithcode.com/paper/self-training-ensemble-networks-for-zero-shot
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Fourier analysis perspective for sufficient dimension reduction problem


Title	Fourier analysis perspective for sufficient dimension reduction problem
Authors	Rustem Takhanov
Abstract	A theory of sufficient dimension reduction (SDR) is developed from an optimizational perspective. In our formulation of the problem, instead of dealing with raw data, we assume that our ground truth includes a mapping ${\mathbf f}: {\mathbb R}^n\rightarrow {\mathbb R}^m$ and a probability distribution function $p$ over ${\mathbb R}^n$, both given analytically. We formulate SDR as a problem of finding a function ${\mathbf g}: {\mathbb R}^k\rightarrow {\mathbb R}^m$ and a matrix $P\in {\mathbb R}^{k\times n}$ such that ${\mathbb E}_{{\mathbf x}\sim p({\mathbf x})} \left{\mathbf f}({\mathbf x}) - {\mathbf g}(P{\mathbf x})\right^2$ is minimal. It turns out that the latter problem allows a reformulation in the dual space, i.e. instead of searching for ${\mathbf g}(P{\mathbf x})$ we suggest searching for its Fourier transform. First, we characterize all tempered distributions that can serve as the Fourier transform of such functions. The reformulation in the dual space can be interpreted as a problem of finding a $k$-dimensional linear subspace $S$ and a tempered distribution ${\mathbf t}$ supported in $S$ such that ${\mathbf t}$ is “close” in a certain sense to the Fourier transform of ${\mathbf f}$. Instead of optimizing over generalized functions with a $k$-dimensional support, we suggest minimizing over ordinary functions but with an additional term $R$ that penalizes a strong distortion of the support from any $k$-dimensional linear subspace. For a specific case of $R$, we develop an algorithm that can be formulated for functions given in the initial form as well as for their Fourier transforms. Eventually, we report results of numerical experiments with a discretized version of the latter algorithm.
Tasks	Dimensionality Reduction
Published	2018-08-19
URL	http://arxiv.org/abs/1808.06191v1
PDF	http://arxiv.org/pdf/1808.06191v1.pdf
PWC	https://paperswithcode.com/paper/fourier-analysis-perspective-for-sufficient
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Gradient Augmented Information Retrieval with Autoencoders and Semantic Hashing


Title	Gradient Augmented Information Retrieval with Autoencoders and Semantic Hashing
Authors	Sean Billings
Abstract	This paper will explore the use of autoencoders for semantic hashing in the context of Information Retrieval. This paper will summarize how to efficiently train an autoencoder in order to create meaningful and low-dimensional encodings of data. This paper will demonstrate how computing and storing the closest encodings to an input query can help speed up search time and improve the quality of our search results. The novel contributions of this paper involve using the representation of the data learned by an auto-encoder in order to augment our search query in various ways. I present and evaluate the new gradient search augmentation (GSA) approach, as well as the more well-known pseudo-relevance-feedback (PRF) adjustment. I find that GSA helps to improve the performance of the TF-IDF based information retrieval system, and PRF combined with GSA works best overall for the systems compared in this paper.
Tasks	Information Retrieval
Published	2018-03-12
URL	http://arxiv.org/abs/1803.04494v1
PDF	http://arxiv.org/pdf/1803.04494v1.pdf
PWC	https://paperswithcode.com/paper/gradient-augmented-information-retrieval-with
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