pwc-archive/papers-with-abstracts huggingface dataset:
Whither Fair Clustering?
Authors: Deepak P
Date: 2020-07-08 00:00:00
Tasks: Clustering, Fairness, Position

Within the relatively busy area of fair machine learning that has been dominated by classification fairness research, fairness in clustering has started to see some recent attention. In this position paper, we assess the existing work in fair clustering and observe that there are several directions that are yet to be explored, and postulate that the state-of-the-art in fair clustering has been quite parochial in outlook. We posit that widening the normative principles to target for, characterizing shortfalls where the target cannot be achieved fully, and making use of knowledge of downstream processes can significantly widen the scope of research in fair clustering research. At a time when clustering and unsupervised learning are being increasingly used to make and influence decisions that matter significantly to human lives, we believe that widening the ambit of fair clustering is of immense significance.
NERD: Neural Network for Edict of Risky Data Streams
Authors: Sandro Passarelli, Cem Gündogan, Lars Stiemert, Matthias Schopp, Peter Hillmann
Date: 2020-07-08 00:00:00
Tasks: Decision Making, Intrusion Detection

Cyber incidents can have a wide range of cause from a simple connection loss to an insistent attack. Once a potential cyber security incidents and system failures have been identified, deciding how to proceed is often complex. Especially, if the real cause is not directly in detail determinable. Therefore, we developed the concept of a Cyber Incident Handling Support System. The developed system is enriched with information by multiple sources such as intrusion detection systems and monitoring tools. It uses over twenty key attributes like sync-package ratio to identify potential security incidents and to classify the data into different priority categories. Afterwards, the system uses artificial intelligence to support the further decision-making process and to generate corresponding reports to brief the Board of Directors. Originating from this information, appropriate and detailed suggestions are made regarding the causes and troubleshooting measures. Feedback from users regarding the problem solutions are included into future decision-making by using labelled flow data as input for the learning process. The prototype shows that the decision making can be sustainably improved and the Cyber Incident Handling process becomes much more effective.
Adversarial learning for product recommendation
Authors: Joel R. Bock, Akhilesh Maewal
Date: 2020-07-07 00:00:00
Tasks: Generative Adversarial Network, Product Recommendation, Two-sample testing

Product recommendation can be considered as a problem in data fusion-- estimation of the joint distribution between individuals, their behaviors, and goods or services of interest. This work proposes a conditional, coupled generative adversarial network (RecommenderGAN) that learns to produce samples from a joint distribution between (view, buy) behaviors found in extremely sparse implicit feedback training data. User interaction is represented by two matrices having binary-valued elements. In each matrix, nonzero values indicate whether a user viewed or bought a specific item in a given product category, respectively. By encoding actions in this manner, the model is able to represent entire, large scale product catalogs. Conversion rate statistics computed on trained GAN output samples ranged from 1.323 to 1.763 percent. These statistics are found to be significant in comparison to null hypothesis testing results. The results are shown comparable to published conversion rates aggregated across many industries and product types. Our results are preliminary, however they suggest that the recommendations produced by the model may provide utility for consumers and digital retailers.
Vision-Based Fall Event Detection in Complex Background Using Attention Guided Bi-directional LSTM
Authors: Yong Chen, Lu Wang, Jiajia Hu, Mingbin Ye
Date: 2020-06-24 00:00:00
Tasks: Event Detection

Fall event detection, as one of the greatest risks to the elderly, has been a hot research issue in the solitary scene in recent years. Nevertheless, there are few researches on the fall event detection in complex background. Different from most conventional background subtraction methods which depend on background modeling, Mask R-CNN method based on deep learning technique can clearly extract the moving object in noise background. We further propose an attention guided Bi-directional LSTM model for the final fall event detection. To demonstrate the efficiency, the proposed method is verified in the public dataset and self-build dataset. Evaluation of the algorithm performances in comparison with other state-of-the-art methods indicates that the proposed design is accurate and robust, which means it is suitable for the task of fall event detection in complex situation.
ANALYSIS OF CALIBRATED SEA CLUTTER AND BOAT REFLECTIVITY DATA AT C- AND X-BAND IN SOUTH AFRICAN COASTAL WATERS
Authors: Ron Rubinstein, Member, Tomer Peleg, Student Member, and Michael Elad, Fellow, IEEE
Date: 2007-10-01 00:00:00
Proceeding: IEEE 2007 10
Tasks: Dictionary Learning

Abstract—The synthesis-based sparse representation model for
signals has drawn considerable interest in the past decade. Such
a model assumes that the signal of interest can be decomposed
as a linear combination of a few atoms from a given dictionary.
In this paper we concentrate on an alternative, analysis-based
model, where an analysis operator—hereafter referred to as the
analysis dictionary—multiplies the signal, leading to a sparse
outcome. Our goal is to learn the analysis dictionary from a set of
examples. The approach taken is parallel and similar to the one
adopted by the K-SVD algorithm that serves the corresponding
problem in the synthesismodel.We present the development of the
algorithm steps: This includes tailored pursuit algorithms—the
Backward Greedy and the Optimized Backward Greedy algorithms,
and a penalty function that defines the objective for the dictionary
update stage. We demonstrate the effectiveness of the proposed
dictionary learning in several experiments, treating synthetic
data and real images, and showing a successful and meaningful
recovery of the analysis dictionary.
A Survey of Paraphrasing and Textual Entailment Methods
Authors: Ion Androutsopoulos, Prodromos Malakasiotis
Date: 2009-12-18 00:00:00
Tasks: Articles, Machine Translation, Natural Language Inference, Question Answering, Survey, Text Generation, Translation

Paraphrasing methods recognize, generate, or extract phrases, sentences, or longer natural language expressions that convey almost the same information. Textual entailment methods, on the other hand, recognize, generate, or extract pairs of natural language expressions, such that a human who reads (and trusts) the first element of a pair would most likely infer that the other element is also true. Paraphrasing can be seen as bidirectional textual entailment and methods from the two areas are often similar. Both kinds of methods are useful, at least in principle, in a wide range of natural language processing applications, including question answering, summarization, text generation, and machine translation. We summarize key ideas from the two areas by considering in turn recognition, generation, and extraction methods, also pointing to prominent articles and resources.
Distributional Measures of Semantic Distance: A Survey
Authors: Saif M. Mohammad, Graeme Hirst
Date: 2012-03-08 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity, Survey

The ability to mimic human notions of semantic distance has widespread applications. Some measures rely only on raw text (distributional measures) and some rely on knowledge sources such as WordNet. Although extensive studies have been performed to compare WordNet-based measures with human judgment, the use of distributional measures as proxies to estimate semantic distance has received little attention. Even though they have traditionally performed poorly when compared to WordNet-based measures, they lay claim to certain uniquely attractive features, such as their applicability in resource-poor languages and their ability to mimic both semantic similarity and semantic relatedness. Therefore, this paper presents a detailed study of distributional measures. Particular attention is paid to flesh out the strengths and limitations of both WordNet-based and distributional measures, and how distributional measures of distance can be brought more in line with human notions of semantic distance. We conclude with a brief discussion of recent work on hybrid measures.
Sentiment Analysis on YouTube: A Brief Survey
Authors: Muhammad Zubair Asghar, Shakeel Ahmad, Afsana Marwat, Fazal Masud Kundi
Date: 2015-11-30 00:00:00
Tasks: Opinion Mining, Sentiment Analysis, Survey

Sentiment analysis or opinion mining is the field of study related to analyze opinions, sentiments, evaluations, attitudes, and emotions of users which they express on social media and other online resources. The revolution of social media sites has also attracted the users towards video sharing sites, such as YouTube. The online users express their opinions or sentiments on the videos that they watch on such sites. This paper presents a brief survey of techniques to analyze opinions posted by users about a particular video.
A Survey of Naïve Bayes Machine Learning approach in Text Document Classification
Authors: Vidhya. K. A, G. Aghila
Date: 2010-03-09 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Document Classification, feature selection, General Classification

Text Document classification aims in associating one or more predefined categories based on the likelihood suggested by the training set of labeled documents. Many machine learning algorithms play a vital role in training the system with predefined categories among which Na\"ive Bayes has some intriguing facts that it is simple, easy to implement and draws better accuracy in large datasets in spite of the na\"ive dependence. The importance of Na\"ive Bayes Machine learning approach has felt hence the study has been taken up for text document classification and the statistical event models available. This survey the various feature selection methods has been discussed and compared along with the metrics related to text document classification.
A survey of non-exchangeable priors for Bayesian nonparametric models
Authors: Nicholas J. Foti, Sinead Williamson
Date: 2012-11-20 00:00:00
Tasks: Survey

Dependent nonparametric processes extend distributions over measures, such as the Dirichlet process and the beta process, to give distributions over collections of measures, typically indexed by values in some covariate space. Such models are appropriate priors when exchangeability assumptions do not hold, and instead we want our model to vary fluidly with some set of covariates. Since the concept of dependent nonparametric processes was formalized by MacEachern [1], there have been a number of models proposed and used in the statistics and machine learning literatures. Many of these models exhibit underlying similarities, an understanding of which, we hope, will help in selecting an appropriate prior, developing new models, and leveraging inference techniques.
Hierarchical Mixtures-of-Experts for Exponential Family Regression Models with Generalized Linear Mean Functions: A Survey of Approximation and Consistency Results
Authors: Wenxin Jiang, Martin A. Tanner
Date: 2013-01-30 00:00:00
Tasks: regression

We investigate a class of hierarchical mixtures-of-experts (HME) models where exponential family regression models with generalized linear mean functions of the form psi(ga+fx^Tfgb) are mixed. Here psi(...) is the inverse link function. Suppose the true response y follows an exponential family regression model with mean function belonging to a class of smooth functions of the form psi(h(fx)) where h(...)in W_2^infty (a Sobolev class over [0,1]^{s}). It is shown that the HME probability density functions can approximate the true density, at a rate of O(m^{-2/s}) in L_p norm, and at a rate of O(m^{-4/s}) in Kullback-Leibler divergence. These rates can be achieved within the family of HME structures with no more than s-layers, where s is the dimension of the predictor fx. It is also shown that likelihood-based inference based on HME is consistent in recovering the truth, in the sense that as the sample size n and the number of experts m both increase, the mean square error of the predicted mean response goes to zero. Conditions for such results to hold are stated and discussed.
Mean-Field Learning: a Survey
Authors: Hamidou Tembine, Raul Tempone, Pedro Vilanova
Date: 2012-10-17 00:00:00
Tasks: global-optimization, Survey

In this paper we study iterative procedures for stationary equilibria in games with large number of players. Most of learning algorithms for games with continuous action spaces are limited to strict contraction best reply maps in which the Banach-Picard iteration converges with geometrical convergence rate. When the best reply map is not a contraction, Ishikawa-based learning is proposed. The algorithm is shown to behave well for Lipschitz continuous and pseudo-contractive maps. However, the convergence rate is still unsatisfactory. Several acceleration techniques are presented. We explain how cognitive users can improve the convergence rate based only on few number of measurements. The methodology provides nice properties in mean field games where the payoff function depends only on own-action and the mean of the mean-field (first moment mean-field games). A learning framework that exploits the structure of such games, called, mean-field learning, is proposed. The proposed mean-field learning framework is suitable not only for games but also for non-convex global optimization problems. Then, we introduce mean-field learning without feedback and examine the convergence to equilibria in beauty contest games, which have interesting applications in financial markets. Finally, we provide a fully distributed mean-field learning and its speedup versions for satisfactory solution in wireless networks. We illustrate the convergence rate improvement with numerical examples.
Survey & Experiment: Towards the Learning Accuracy
Authors: Zeyuan Allen Zhu
Date: 2010-12-18 00:00:00
Tasks: Binary Classification, Generalization Bounds, Learning Theory, Survey

To attain the best learning accuracy, people move on with difficulties and frustrations. Though one can optimize the empirical objective using a given set of samples, its generalization ability to the entire sample distribution remains questionable. Even if a fair generalization guarantee is offered, one still wants to know what is to happen if the regularizer is removed, and/or how well the artificial loss (like the hinge loss) relates to the accuracy. For such reason, this report surveys four different trials towards the learning accuracy, embracing the major advances in supervised learning theory in the past four years. Starting from the generic setting of learning, the first two trials introduce the best optimization and generalization bounds for convex learning, and the third trial gets rid of the regularizer. As an innovative attempt, the fourth trial studies the optimization when the objective is exactly the accuracy, in the special case of binary classification. This report also analyzes the last trial through experiments.
Fine-tuning Siamese Networks to Assess Sport Gestures Quality
Authors: Mégane Millan, Catherine Achard
Date: 2020-02-27 00:00:00
Proceeding: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 2020 2
Tasks: Action Quality Assessment, Temporal Sequences

This paper presents an Action Quality Assessment (AQA) approach that learns to automatically score action
realization from temporal sequences like videos. To manage the small size of most of databases capturing
actions or gestures, we propose to use Siamese Networks. In the literature, Siamese Networks are widely used
to rank action scores. Indeed, their purpose is not to regress scores but to predict a value that respects true
scores order so that it can be used to rank actions according to their quality. For AQA, we need to predict
real scores, as well as the difference between these scores and their range. Thus, we first introduce a new loss
function to train Siamese Networks in order to regress score gaps. Once the Siamese network is trained, a
branch of this network is extracted and fine-tuned for score prediction. We tested our approach on a public
database, the AQA-7 dataset, composed of videos from 7 sports. Our results outperform state of the art on
AQA task. Moreover, we show that the proposed method is also more efficient for action ranking.
RetrieveGAN: Image Synthesis via Differentiable Patch Retrieval
Authors: Hung-Yu Tseng, Hsin-Ying Lee, Lu Jiang, Ming-Hsuan Yang, Weilong Yang
Date: 2020-07-16 00:00:00
Proceeding: ECCV 2020 8
Tasks: Image Generation, Retrieval

Image generation from scene description is a cornerstone technique for the controlled generation, which is beneficial to applications such as content creation and image editing. In this work, we aim to synthesize images from scene description with retrieved patches as reference. We propose a differentiable retrieval module. With the differentiable retrieval module, we can (1) make the entire pipeline end-to-end trainable, enabling the learning of better feature embedding for retrieval; (2) encourage the selection of mutually compatible patches with additional objective functions. We conduct extensive quantitative and qualitative experiments to demonstrate that the proposed method can generate realistic and diverse images, where the retrieved patches are reasonable and mutually compatible.
World-Consistent Video-to-Video Synthesis
Authors: Arun Mallya, Ting-Chun Wang, Karan Sapra, Ming-Yu Liu
Date: 2020-07-16 00:00:00
Proceeding: ECCV 2020 8
Tasks: Video-to-Video Synthesis

Video-to-video synthesis (vid2vid) aims for converting high-level semantic inputs to photorealistic videos. While existing vid2vid methods can achieve short-term temporal consistency, they fail to ensure the long-term one. This is because they lack knowledge of the 3D world being rendered and generate each frame only based on the past few frames. To address the limitation, we introduce a novel vid2vid framework that efficiently and effectively utilizes all past generated frames during rendering. This is achieved by condensing the 3D world rendered so far into a physically-grounded estimate of the current frame, which we call the guidance image. We further propose a novel neural network architecture to take advantage of the information stored in the guidance images. Extensive experimental results on several challenging datasets verify the effectiveness of our approach in achieving world consistency - the output video is consistent within the entire rendered 3D world. https://nvlabs.github.io/wc-vid2vid/
RepPoints V2: Verification Meets Regression for Object Detection
Authors: Yihong Chen, Zheng Zhang, Yue Cao, Li-Wei Wang, Stephen Lin, Han Hu
Date: 2020-07-16 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Instance Segmentation, Object, object-detection, Object Detection, Object Localization, Philosophy, regression, Semantic Segmentation

Verification and regression are two general methodologies for prediction in neural networks. Each has its own strengths: verification can be easier to infer accurately, and regression is more efficient and applicable to continuous target variables. Hence, it is often beneficial to carefully combine them to take advantage of their benefits. In this paper, we take this philosophy to improve state-of-the-art object detection, specifically by RepPoints. Though RepPoints provides high performance, we find that its heavy reliance on regression for object localization leaves room for improvement. We introduce verification tasks into the localization prediction of RepPoints, producing RepPoints v2, which provides consistent improvements of about 2.0 mAP over the original RepPoints on the COCO object detection benchmark using different backbones and training methods. RepPoints v2 also achieves 52.1 mAP on COCO \texttt{test-dev} by a single model. Moreover, we show that the proposed approach can more generally elevate other object detection frameworks as well as applications such as instance segmentation. The code is available at https://github.com/Scalsol/RepPointsV2.
SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Design
Authors: Ari Seff, Yaniv Ovadia, Wenda Zhou, Ryan P. Adams
Date: 2020-07-16 00:00:00
Tasks: Program Synthesis

Parametric computer-aided design (CAD) is the dominant paradigm in mechanical engineering for physical design. Distinguished by relational geometry, parametric CAD models begin as two-dimensional sketches consisting of geometric primitives (e.g., line segments, arcs) and explicit constraints between them (e.g., coincidence, perpendicularity) that form the basis for three-dimensional construction operations. Training machine learning models to reason about and synthesize parametric CAD designs has the potential to reduce design time and enable new design workflows. Additionally, parametric CAD designs can be viewed as instances of constraint programming and they offer a well-scoped test bed for exploring ideas in program synthesis and induction. To facilitate this research, we introduce SketchGraphs, a collection of 15 million sketches extracted from real-world CAD models coupled with an open-source data processing pipeline. Each sketch is represented as a geometric constraint graph where edges denote designer-imposed geometric relationships between primitives, the nodes of the graph. We demonstrate and establish benchmarks for two use cases of the dataset: generative modeling of sketches and conditional generation of likely constraints given unconstrained geometry.
FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning
Authors: Chia-Wen Kuo, Chih-Yao Ma, Jia-Bin Huang, Zsolt Kira
Date: 2020-07-16 00:00:00
Proceeding: ECCV 2020 8
Tasks: Clustering, Data Augmentation, Semi-Supervised Image Classification

Recent state-of-the-art semi-supervised learning (SSL) methods use a combination of image-based transformations and consistency regularization as core components. Such methods, however, are limited to simple transformations such as traditional data augmentation or convex combinations of two images. In this paper, we propose a novel learned feature-based refinement and augmentation method that produces a varied set of complex transformations. Importantly, these transformations also use information from both within-class and across-class prototypical representations that we extract through clustering. We use features already computed across iterations by storing them in a memory bank, obviating the need for significant extra computation. These transformations, combined with traditional image-based augmentation, are then used as part of the consistency-based regularization loss. We demonstrate that our method is comparable to current state of art for smaller datasets (CIFAR-10 and SVHN) while being able to scale up to larger datasets such as CIFAR-100 and mini-Imagenet where we achieve significant gains over the state of art (\textit{e.g.,} absolute 17.44\% gain on mini-ImageNet). We further test our method on DomainNet, demonstrating better robustness to out-of-domain unlabeled data, and perform rigorous ablations and analysis to validate the method.