pwc-archive/papers-with-abstracts huggingface dataset:
Learning Semantic Neural Tree for Human Parsing
Authors: Ruyi Ji, Dawei Du, Libo Zhang, Longyin Wen, Yanjun Wu, Chen Zhao, Feiyue Huang, Siwei Lyu
Date: 2019-12-20 00:00:00
Proceeding: ECCV 2020 8
Tasks: Human Parsing, Semantic Segmentation

The majority of existing human parsing methods formulate the task as semantic segmentation, which regard each semantic category equally and fail to exploit the intrinsic physiological structure of human body, resulting in inaccurate results. In this paper, we design a novel semantic neural tree for human parsing, which uses a tree architecture to encode physiological structure of human body, and designs a coarse to fine process in a cascade manner to generate accurate results. Specifically, the semantic neural tree is designed to segment human regions into multiple semantic subregions (e.g., face, arms, and legs) in a hierarchical way using a new designed attention routing module. Meanwhile, we introduce the semantic aggregation module to combine multiple hierarchical features to exploit more context information for better performance. Our semantic neural tree can be trained in an end-to-end fashion by standard stochastic gradient descent (SGD) with back-propagation. Several experiments conducted on four challenging datasets for both single and multiple human parsing, i.e., LIP, PASCAL-Person-Part, CIHP and MHP-v2, demonstrate the effectiveness of the proposed method. Code can be found at https://isrc.iscas.ac.cn/gitlab/research/sematree.
Understanding Deep Neural Network Predictions for Medical Imaging Applications
Authors: Barath Narayanan Narayanan, Manawaduge Supun De Silva, Russell C. Hardie, Nathan K. Kueterman, Redha Ali
Date: 2019-12-20 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

Computer-aided detection has been a research area attracting great interest in the past decade. Machine learning algorithms have been utilized extensively for this application as they provide a valuable second opinion to the doctors. Despite several machine learning models being available for medical imaging applications, not many have been implemented in the real-world due to the uninterpretable nature of the decisions made by the network. In this paper, we investigate the results provided by deep neural networks for the detection of malaria, diabetic retinopathy, brain tumor, and tuberculosis in different imaging modalities. We visualize the class activation mappings for all the applications in order to enhance the understanding of these networks. This type of visualization, along with the corresponding network performance metrics, would aid the data science experts in better understanding of their models as well as assisting doctors in their decision-making process.
Features or Shape? Tackling the False Dichotomy of Time Series Classification
Authors: Sara Alaee, Alireza Abdoli, Christian Shelton, Amy C. Murillo, Alec C. Gerry, Eamonn Keogh
Date: 2019-12-20 00:00:00
Tasks: Classification, General Classification, Time Series, Time Series Analysis, Time Series Classification

Time series classification is an important task in its own right, and it is often a precursor to further downstream analytics. To date, virtually all works in the literature have used either shape-based classification using a distance measure or feature-based classification after finding some suitable features for the domain. It seems to be underappreciated that in many datasets it is the case that some classes are best discriminated with features, while others are best discriminated with shape. Thus, making the shape vs. feature choice will condemn us to poor results, at least for some classes. In this work, we propose a new model for classifying time series that allows the use of both shape and feature-based measures, when warranted. Our algorithm automatically decides which approach is best for which class, and at query time chooses which classifier to trust the most. We evaluate our idea on real world datasets and demonstrate that our ideas produce statistically significant improvement in classification accuracy.
Divide and Conquer: an Accurate Machine Learning Algorithm to Process Split Videos on a Parallel Processing Infrastructure
Authors: Walter M. Mayor Toro, Juan C. Perafan Villota, Oscar H. Mondragon, Johan S. Obando Ceron
Date: 2019-12-20 00:00:00

Every day the number of traffic cameras in cities rapidly increase and huge amount of video data are generated. Parallel processing infrastruture, such as Hadoop, and programming models, such as MapReduce, are being used to promptly process that amount of data. The common approach for video processing by using Hadoop MapReduce is to process an entire video on only one node, however, in order to avoid parallelization problems, such as load imbalance, we propose to process videos by splitting it into equal parts and processing each resulting chunk on a different node. We used some machine learning techniques to detect and track the vehicles. However, video division may produce inaccurate results. To solve this problem we proposed a heuristic algorithm to avoid process a vehicle in more than one chunk.
Group-Connected Multilayer Perceptron Networks
Authors: Mohammad Kachuee, Sajad Darabi, Shayan Fazeli, Majid Sarrafzadeh
Date: 2019-12-20 00:00:00
Tasks: Representation Learning

Despite the success of deep learning in domains such as image, voice, and graphs, there has been little progress in deep representation learning for domains without a known structure between features. For instance, a tabular dataset of different demographic and clinical factors where the feature interactions are not given as a prior. In this paper, we propose Group-Connected Multilayer Perceptron (GMLP) networks to enable deep representation learning in these domains. GMLP is based on the idea of learning expressive feature combinations (groups) and exploiting them to reduce the network complexity by defining local group-wise operations. During the training phase, GMLP learns a sparse feature grouping matrix using temperature annealing softmax with an added entropy loss term to encourage the sparsity. Furthermore, an architecture is suggested which resembles binary trees, where group-wise operations are followed by pooling operations to combine information; reducing the number of groups as the network grows in depth. To evaluate the proposed method, we conducted experiments on different real-world datasets covering various application areas. Additionally, we provide visualizations on MNIST and synthesized data. According to the results, GMLP is able to successfully learn and exploit expressive feature combinations and achieve state-of-the-art classification performance on different datasets.
Non-congruent non-degenerate curves with identical signatures
Authors: Eric Geiger, Irina A. Kogan
Date: 2019-12-20 00:00:00
Tasks: Math

While the equality of differential signatures (Calabi et al, Int. J. Comput. Vis. 26: 107-135, 1998) is known to be a necessary condition for congruence, it is not sufficient (Musso and Nicolodi, J. Math Imaging Vis. 35: 68-85, 2009). Hickman (J. Math Imaging Vis. 43: 206-213, 2012, Theorem 2) claimed that for non-degenerate planar curves, equality of Euclidean signatures implies congruence. We prove that while Hickman's claim holds for simple, closed curves with simple signatures, it fails for curves with non-simple signatures. In the later case, we associate a directed graph with the signature and show how various paths along the graph give rise to a family of non-congruent, non-degenerate curves with identical signatures. Using this additional structure, we formulate congruence criteria for non-degenerate, closed, simple curves and show how the paths reflect the global and local symmetries of the corresponding curve.
Exploiting the potential of deep reinforcement learning for classification tasks in high-dimensional and unstructured data
Authors: Johan S. Obando-Ceron, Victor Romero Cano, Walter Mayor Toro
Date: 2019-12-20 00:00:00
Tasks: Deep Reinforcement Learning, feature selection, General Classification, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper presents a framework for efficiently learning feature selection policies which use less features to reach a high classification precision on large unstructured data. It uses a Deep Convolutional Autoencoder (DCAE) for learning compact feature spaces, in combination with recently-proposed Reinforcement Learning (RL) algorithms as Double DQN and Retrace.
Gaussian Process Latent Variable Model Factorization for Context-aware Recommender Systems
Authors: Wei Huang, Richard Yi Da Xu
Date: 2019-12-19 00:00:00
Tasks: Dimensionality Reduction, Recommendation Systems

Context-aware recommender systems (CARS) have gained increasing attention due to their ability to utilize contextual information. Compared to traditional recommender systems, CARS are, in general, able to generate more accurate recommendations. Latent factors approach accounts for a large proportion of CARS. Recently, a non-linear Gaussian Process (GP) based factorization method was proven to outperform the state-of-the-art methods in CARS. Despite its effectiveness, GP model-based methods can suffer from over-fitting and may not be able to determine the impact of each context automatically. In order to address such shortcomings, we propose a Gaussian Process Latent Variable Model Factorization (GPLVMF) method, where we apply an appropriate prior to the original GP model. Our work is primarily inspired by the Gaussian Process Latent Variable Model (GPLVM), which was a non-linear dimensionality reduction method. As a result, we improve the performance on the real datasets significantly as well as capturing the importance of each context. In addition to the general advantages, our method provides two main contributions regarding recommender system settings: (1) addressing the influence of bias by setting a non-zero mean function, and (2) utilizing real-valued contexts by fixing the latent space with real values.
Graph Convolutional Networks: analysis, improvements and results
Authors: Ihsan Ullah, Mario Manzo, Mitul Shah, Michael Madden
Date: 2019-12-19 00:00:00
Tasks: Clustering

In the current era of neural networks and big data, higher dimensional data is processed for automation of different application areas. Graphs represent a complex data organization in which dependencies between more than one object or activity occur. Due to the high dimensionality, this data creates challenges for machine learning algorithms. Graph convolutional networks were introduced to utilize the convolutional models concepts that shows good results. In this context, we enhanced two of the existing Graph convolutional network models by proposing four enhancements. These changes includes: hyper parameters optimization, convex combination of activation functions, topological information enrichment through clustering coefficients measure, and structural redesign of the network through addition of dense layers. We present extensive results on four state-of-art benchmark datasets. The performance is notable not only in terms of lesser computational cost compared to competitors, but also achieved competitive results for three of the datasets and state-of-the-art for the fourth dataset.
Smart Home Appliances: Chat with Your Fridge
Authors: Denis Gudovskiy, Gyuri Han, Takuya Yamaguchi, Sotaro Tsukizawa
Date: 2019-12-19 00:00:00
Tasks: Dataset Generation, Visual Reasoning

Current home appliances are capable to execute a limited number of voice commands such as turning devices on or off, adjusting music volume or light conditions. Recent progress in machine reasoning gives an opportunity to develop new types of conversational user interfaces for home appliances. In this paper, we apply state-of-the-art visual reasoning model and demonstrate that it is feasible to ask a smart fridge about its contents and various properties of the food with close-to-natural conversation experience. Our visual reasoning model answers user questions about existence, count, category and freshness of each product by analyzing photos made by the image sensor inside the smart fridge. Users may chat with their fridge using off-the-shelf phone messenger while being away from home, for example, when shopping in the supermarket. We generate a visually realistic synthetic dataset to train machine learning reasoning model that achieves 95% answer accuracy on test data. We present the results of initial user tests and discuss how we modify distribution of generated questions for model training based on human-in-the-loop guidance. We open source code for the whole system including dataset generation, reasoning model and demonstration scripts.
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax
Authors: Andres Potapczynski, Gabriel Loaiza-Ganem, John P. Cunningham
Date: 2019-12-19 00:00:00
Proceeding: NeurIPS 2020 12

The Gumbel-Softmax is a continuous distribution over the simplex that is often used as a relaxation of discrete distributions. Because it can be readily interpreted and easily reparameterized, it enjoys widespread use. We propose a modular and more flexible family of reparameterizable distributions where Gaussian noise is transformed into a one-hot approximation through an invertible function. This invertible function is composed of a modified softmax and can incorporate diverse transformations that serve different specific purposes. For example, the stick-breaking procedure allows us to extend the reparameterization trick to distributions with countably infinite support, thus enabling the use of our distribution along nonparametric models, or normalizing flows let us increase the flexibility of the distribution. Our construction enjoys theoretical advantages over the Gumbel-Softmax, such as closed form KL, and significantly outperforms it in a variety of experiments. Our code is available at https://github.com/cunningham-lab/igr.
BERTje: A Dutch BERT Model
Authors: Wietse de Vries, Andreas van Cranenburgh, Arianna Bisazza, Tommaso Caselli, Gertjan van Noord, Malvina Nissim
Date: 2019-12-19 00:00:00
Tasks: Language Modeling, Language Modelling, model, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Part-Of-Speech Tagging, Semantic Role Labeling, Sentiment Analysis

The transformer-based pre-trained language model BERT has helped to improve state-of-the-art performance on many natural language processing (NLP) tasks. Using the same architecture and parameters, we developed and evaluated a monolingual Dutch BERT model called BERTje. Compared to the multilingual BERT model, which includes Dutch but is only based on Wikipedia text, BERTje is based on a large and diverse dataset of 2.4 billion tokens. BERTje consistently outperforms the equally-sized multilingual BERT model on downstream NLP tasks (part-of-speech tagging, named-entity recognition, semantic role labeling, and sentiment analysis). Our pre-trained Dutch BERT model is made available at https://github.com/wietsedv/bertje.
Line Drawings of Natural Scenes Guide Visual Attention
Authors: Kai-Fu Yang, Wen-Wen Jiang, Teng-Fei Zhan, Yong-Jie Li
Date: 2019-12-19 00:00:00

Visual search is an important strategy of the human visual system for fast scene perception. The guided search theory suggests that the global layout or other top-down sources of scenes play a crucial role in guiding object searching. In order to verify the specific roles of scene layout and regional cues in guiding visual attention, we executed a psychophysical experiment to record the human fixations on line drawings of natural scenes with an eye-tracking system in this work. We collected the human fixations of ten subjects from 498 natural images and of another ten subjects from the corresponding 996 human-marked line drawings of boundaries (two boundary maps per image) under free-viewing condition. The experimental results show that with the absence of some basic features like color and luminance, the distribution of the fixations on the line drawings has a high correlation with that on the natural images. Moreover, compared to the basic cues of regions, subjects pay more attention to the closed regions of line drawings which are usually related to the dominant objects of the scenes. Finally, we built a computational model to demonstrate that the fixation information on the line drawings can be used to significantly improve the performances of classical bottom-up models for fixation prediction in natural scenes. These results support that Gestalt features and scene layout are important cues for guiding fast visual object searching.
High Resolution Millimeter Wave Imaging For Self-Driving Cars
Authors: Junfeng Guan, Sohrab Madani, Suraj Jog, Haitham Hassanieh
Date: 2019-12-19 00:00:00
Tasks: Self-Driving Cars, Vocal Bursts Intensity Prediction

Recent years have witnessed much interest in expanding the use of networking signals beyond communication to sensing, localization, robotics, and autonomous systems. This paper explores how we can leverage recent advances in 5G millimeter wave (mmWave) technology for imaging in self-driving cars. Specifically, the use of mmWave in 5G has led to the creation of compact phased arrays with hundreds of antenna elements that can be electronically steered. Such phased arrays can expand the use of mmWave beyond vehicular communications and simple ranging sensors to a full-fledged imaging system that enables self-driving cars to see through fog, smog, snow, etc. Unfortunately, using mmWave signals for imaging in self-driving cars is challenging due to the very low resolution, the presence of fake artifacts resulting from multipath reflections and the absence of portions of the car due to specularity. This paper presents HawkEye, a system that can enable high resolution mmWave imaging in self driving cars. HawkEye addresses the above challenges by leveraging recent advances in deep learning known as Generative Adversarial Networks (GANs). HawkEye introduces a GAN architecture that is customized to mmWave imaging and builds a system that can significantly enhance the quality of mmWave images for self-driving cars.
RIMAX: Ranking Semantic Rhymes by calculating Definition Similarity
Authors: Alfonso Medina-Urrea, Juan-Manuel Torres-Moreno
Date: 2019-12-19 00:00:00

This paper presents RIMAX, a new system for detecting semantic rhymes, using a Comprehensive Mexican Spanish Dictionary (DEM) and its Rhyming Dictionary (REM). We use the Vector Space Model to calculate the similarity of the definition of a query with the definitions corresponding to the assonant and consonant rhymes of the query. The preliminary results using a manual evaluation are very encouraging.
Deep Exemplar Networks for VQA and VQG
Authors: Badri N. Patro, Vinay P. Namboodiri
Date: 2019-12-19 00:00:00
Tasks: Decoder, Question Answering, Question Generation, Question-Generation, Visual Question Answering, Visual Question Answering (VQA)

In this paper, we consider the problem of solving semantic tasks such as `Visual Question Answering' (VQA), where one aims to answers related to an image and `Visual Question Generation' (VQG), where one aims to generate a natural question pertaining to an image. Solutions for VQA and VQG tasks have been proposed using variants of encoder-decoder deep learning based frameworks that have shown impressive performance. Humans however often show generalization by relying on exemplar based approaches. For instance, the work by Tversky and Kahneman suggests that humans use exemplars when making categorizations and decisions. In this work, we propose the incorporation of exemplar based approaches towards solving these problems. Specifically, we incorporate exemplar based approaches and show that an exemplar based module can be incorporated in almost any of the deep learning architectures proposed in the literature and the addition of such a block results in improved performance for solving these tasks. Thus, just as the incorporation of attention is now considered de facto useful for solving these tasks, similarly, incorporating exemplars also can be considered to improve any proposed architecture for solving this task. We provide extensive empirical analysis for the same through various architectures, ablations, and state of the art comparisons.
Interactive Open-Ended Learning for 3D Object Recognition
Authors: S. Hamidreza Kasaei
Date: 2019-12-19 00:00:00
Tasks: 3D Object Recognition, Object, Object Recognition

The thesis contributes in several important ways to the research area of 3D object category learning and recognition. To cope with the mentioned limitations, we look at human cognition, in particular at the fact that human beings learn to recognize object categories ceaselessly over time. This ability to refine knowledge from the set of accumulated experiences facilitates the adaptation to new environments. Inspired by this capability, we seek to create a cognitive object perception and perceptual learning architecture that can learn 3D object categories in an open-ended fashion. In this context, ``open-ended'' implies that the set of categories to be learned is not known in advance, and the training instances are extracted from actual experiences of a robot, and thus become gradually available, rather than being available since the beginning of the learning process. In particular, this architecture provides perception capabilities that will allow robots to incrementally learn object categories from the set of accumulated experiences and reason about how to perform complex tasks. This framework integrates detection, tracking, teaching, learning, and recognition of objects. An extensive set of systematic experiments, in multiple experimental settings, was carried out to thoroughly evaluate the described learning approaches. Experimental results show that the proposed system is able to interact with human users, learn new object categories over time, as well as perform complex tasks. The contributions presented in this thesis have been fully implemented and evaluated on different standard object and scene datasets and empirically evaluated on different robotic platforms.
Data Science through the looking glass and what we found there
Authors: Fotis Psallidas, Yiwen Zhu, Bojan Karlas, Matteo Interlandi, Avrilia Floratou, Konstantinos Karanasos, Wentao Wu, Ce Zhang, Subru Krishnan, Carlo Curino, Markus Weimer
Date: 2019-12-19 00:00:00

The recent success of machine learning (ML) has led to an explosive growth both in terms of new systems and algorithms built in industry and academia, and new applications built by an ever-growing community of data science (DS) practitioners. This quickly shifting panorama of technologies and applications is challenging for builders and practitioners alike to follow. In this paper, we set out to capture this panorama through a wide-angle lens, by performing the largest analysis of DS projects to date, focusing on questions that can help determine investments on either side. Specifically, we download and analyze: (a) over 6M Python notebooks publicly available on GITHUB, (b) over 2M enterprise DS pipelines developed within COMPANYX, and (c) the source code and metadata of over 900 releases from 12 important DS libraries. The analysis we perform ranges from coarse-grained statistical characterizations to analysis of library imports, pipelines, and comparative studies across datasets and time. We report a large number of measurements for our readers to interpret, and dare to draw a few (actionable, yet subjective) conclusions on (a) what systems builders should focus on to better serve practitioners, and (b) what technologies should practitioners bet on given current trends. We plan to automate this analysis and release associated tools and results periodically.