pwc-archive/papers-with-abstracts huggingface dataset:
Target-Specific Action Classification for Automated Assessment of Human Motor Behavior from Video
Authors: Behnaz Rezaei, Yiorgos Christakis, Bryan Ho, Kevin Thomas, Kelley Erb, Sarah Ostadabbas, Shyamal Patel
Date: 2019-09-20 00:00:00
Tasks: Action Classification, Action Recognition, Classification, General Classification, Management

Objective monitoring and assessment of human motor behavior can improve the diagnosis and management of several medical conditions. Over the past decade, significant advances have been made in the use of wearable technology for continuously monitoring human motor behavior in free-living conditions. However, wearable technology remains ill-suited for applications which require monitoring and interpretation of complex motor behaviors (e.g. involving interactions with the environment). Recent advances in computer vision and deep learning have opened up new possibilities for extracting information from video recordings. In this paper, we present a hierarchical vision-based behavior phenotyping method for classification of basic human actions in video recordings performed using a single RGB camera. Our method addresses challenges associated with tracking multiple human actors and classification of actions in videos recorded in changing environments with different fields of view. We implement a cascaded pose tracker that uses temporal relationships between detections for short-term tracking and appearance-based tracklet fusion for long-term tracking. Furthermore, for action classification, we use pose evolution maps derived from the cascaded pose tracker as low-dimensional and interpretable representations of the movement sequences for training a convolutional neural network. The cascaded pose tracker achieves an average accuracy of 88\% in tracking the target human actor in our video recordings, and overall system achieves average test accuracy of 84\% for target-specific action classification in untrimmed video recordings.
Automatic Table completion using Knowledge Base
Authors: Bortik Bandyopadhyay, Xiang Deng, Goonmeet Bajaj, Huan Sun, Srinivasan Parthasarathy
Date: 2019-09-20 00:00:00
Tasks: Decision Making

Table is a popular data format to organize and present relational information. Users often have to manually compose tables when gathering their desiderate information (e.g., entities and their attributes) for decision making. In this work, we propose to resolve a new type of heterogeneous query viz: tabular query, which contains a natural language query description, column names of the desired table, and an example row. We aim to acquire more entity tuples (rows) and automatically fill the table specified by the tabular query. We design a novel framework AutoTableComplete which aims to integrate schema specific structural information with the natural language contextual information provided by the user, to complete tables automatically, using a heterogeneous knowledge base (KB) as the main information source. Given a tabular query as input, our framework first constructs a set of candidate chains that connect the given example entities in KB. We learn to select the best matching chain from these candidates using the semantic context from tabular query. The selected chain is then converted into a SPARQL query, executed against KB to gather a set of candidate rows, that are then ranked in order of their relevance to the tabular query, to complete the desired table. We construct a new dataset based on tables in Wikipedia pages and Freebase, using which we perform a wide range of experiments to demonstrate the effectiveness of AutoTableComplete as well as present a detailed error analysis of our method.
Leveraging Implicit Expert Knowledge for Non-Circular Machine Learning in Sepsis Prediction
Authors: Shigehiko Schamoni, Holger A. Lindner, Verena Schneider-Lindner, Manfred Thiel, Stefan Riezler
Date: 2019-09-20 00:00:00
Tasks: BIG-bench Machine Learning

Sepsis is the leading cause of death in non-coronary intensive care units. Moreover, a delay of antibiotic treatment of patients with severe sepsis by only few hours is associated with increased mortality. This insight makes accurate models for early prediction of sepsis a key task in machine learning for healthcare. Previous approaches have achieved high AUROC by learning from electronic health records where sepsis labels were defined automatically following established clinical criteria. We argue that the practice of incorporating the clinical criteria that are used to automatically define ground truth sepsis labels as features of severity scoring models is inherently circular and compromises the validity of the proposed approaches. We propose to create an independent ground truth for sepsis research by exploiting implicit knowledge of clinical practitioners via an electronic questionnaire which records attending physicians' daily judgements of patients' sepsis status. We show that despite its small size, our dataset allows to achieve state-of-the-art AUROC scores. An inspection of learned weights for standardized features of the linear model lets us infer potentially surprising feature contributions and allows to interpret seemingly counterintuitive findings.
Defending Against Physically Realizable Attacks on Image Classification
Authors: Tong Wu, Liang Tong, Yevgeniy Vorobeychik
Date: 2019-09-20 00:00:00
Proceeding: ICLR 2020 1
Tasks: Classification, General Classification, image-classification, Image Classification

We study the problem of defending deep neural network approaches for image classification from physically realizable attacks. First, we demonstrate that the two most scalable and effective methods for learning robust models, adversarial training with PGD attacks and randomized smoothing, exhibit very limited effectiveness against three of the highest profile physical attacks. Next, we propose a new abstract adversarial model, rectangular occlusion attacks, in which an adversary places a small adversarially crafted rectangle in an image, and develop two approaches for efficiently computing the resulting adversarial examples. Finally, we demonstrate that adversarial training using our new attack yields image classification models that exhibit high robustness against the physically realizable attacks we study, offering the first effective generic defense against such attacks.
Natural Language Processing via LDA Topic Model in Recommendation Systems
Authors: Hamed Jelodar, Yongli Wang, Mahdi Rabbani, SeyedValyAllah Ayobi
Date: 2019-09-20 00:00:00
Tasks: Articles, Collaborative Filtering, Movie Recommendation, Recommendation Systems

Today, Internet is one of the widest available media worldwide. Recommendation systems are increasingly being used in various applications such as movie recommendation, mobile recommendation, article recommendation and etc. Collaborative Filtering (CF) and Content-Based (CB) are Well-known techniques for building recommendation systems. Topic modeling based on LDA, is a powerful technique for semantic mining and perform topic extraction. In the past few years, many articles have been published based on LDA technique for building recommendation systems. In this paper, we present taxonomy of recommendation systems and applications based on LDA. In addition, we utilize LDA and Gibbs sampling algorithms to evaluate ISWC and WWW conference publications in computer science. Our study suggest that the recommendation systems based on LDA could be effective in building smart recommendation system in online communities.
An Efficient Sampling-based Method for Online Informative Path Planning in Unknown Environments
Authors: Lukas Schmid, Michael Pantic, Raghav Khanna, Lionel Ott, Roland Siegwart, Juan Nieto
Date: 2019-09-20 00:00:00
Tasks: 3D Reconstruction

The ability to plan informative paths online is essential to robot autonomy. In particular, sampling-based approaches are often used as they are capable of using arbitrary information gain formulations. However, they are prone to local minima, resulting in sub-optimal trajectories, and sometimes do not reach global coverage. In this paper, we present a new RRT*-inspired online informative path planning algorithm. Our method continuously expands a single tree of candidate trajectories and rewires segments to maintain the tree and refine intermediate trajectories. This allows the algorithm to achieve global coverage and maximize the utility of a path in a global context, using a single objective function. We demonstrate the algorithm's capabilities in the applications of autonomous indoor exploration as well as accurate Truncated Signed Distance Field (TSDF)-based 3D reconstruction on-board a Micro Aerial vehicle (MAV). We study the impact of commonly used information gain and cost formulations in these scenarios and propose a novel TSDF-based 3D reconstruction gain and cost-utility formulation. Detailed evaluation in realistic simulation environments show that our approach outperforms state of the art methods in these tasks. Experiments on a real MAV demonstrate the ability of our method to robustly plan in real-time, exploring an indoor environment solely with on-board sensing and computation. We make our framework available for future research.
A Transfer Learning Approach for Automated Segmentation of Prostate Whole Gland and Transition Zone in Diffusion Weighted MRI
Authors: Saman Motamed, Isha Gujrathi, Dominik Deniffel, Anton Oentoro, Masoom A. Haider, Farzad Khalvati
Date: 2019-09-20 00:00:00
Tasks: Medical Image Analysis, Segmentation, Transfer Learning

The segmentation of prostate whole gland and transition zone in Diffusion Weighted MRI (DWI) are the first step in designing computer-aided detection algorithms for prostate cancer. However, variations in MRI acquisition parameters and scanner manufacturing result in different appearances of prostate tissue in the images. Convolutional neural networks (CNNs) which have shown to be successful in various medical image analysis tasks including segmentation are typically sensitive to the variations in imaging parameters. This sensitivity leads to poor segmentation performance of CNNs trained on a source cohort and tested on a target cohort from a different scanner and hence, it limits the applicability of CNNs for cross-cohort training and testing. Contouring prostate whole gland and transition zone in DWI images are time-consuming and expensive. Thus, it is important to enable CNNs pretrained on images of source domain, to segment images of target domain with minimum requirement for manual segmentation of images from the target domain. In this work, we propose a transfer learning method based on a modified U-net architecture and loss function, for segmentation of prostate whole gland and transition zone in DWIs using a CNN pretrained on a source dataset and tested on the target dataset. We explore the effect of the size of subset of target dataset used for fine-tuning the pre-trained CNN on the overall segmentation accuracy. Our results show that with a fine-tuning data as few as 30 patients from the target domain, the proposed transfer learning-based algorithm can reach dice score coefficient of 0.80 for both prostate whole gland and transition zone segmentation. Using a fine-tuning data of 115 patients from the target domain, dice score coefficient of 0.85 and 0.84 are achieved for segmentation of whole gland and transition zone, respectively, in the target domain.
Reconnaissance and Planning algorithm for constrained MDP
Authors: Shin-ichi Maeda, Hayato Watahiki, Shintarou Okada, Masanori Koyama
Date: 2019-09-20 00:00:00
Tasks: Reinforcement Learning

Practical reinforcement learning problems are often formulated as constrained Markov decision process (CMDP) problems, in which the agent has to maximize the expected return while satisfying a set of prescribed safety constraints. In this study, we propose a novel simulator-based method to approximately solve a CMDP problem without making any compromise on the safety constraints. We achieve this by decomposing the CMDP into a pair of MDPs; reconnaissance MDP and planning MDP. The purpose of reconnaissance MDP is to evaluate the set of actions that are safe, and the purpose of planning MDP is to maximize the return while using the actions authorized by reconnaissance MDP. RMDP can define a set of safe policies for any given set of safety constraint, and this set of safe policies can be used to solve another CMDP problem with different reward. Our method is not only computationally less demanding than the previous simulator-based approaches to CMDP, but also capable of finding a competitive reward-seeking policy in a high dimensional environment, including those involving multiple moving obstacles.
Creative GANs for generating poems, lyrics, and metaphors
Authors: Asir Saeed, Suzana Ilić, Eva Zangerle
Date: 2019-09-20 00:00:00
Tasks: Generative Adversarial Network, Language Modeling, Language Modelling, Machine Translation, Text Generation, Translation

Generative models for text have substantially contributed to tasks like machine translation and language modeling, using maximum likelihood optimization (MLE). However, for creative text generation, where multiple outputs are possible and originality and uniqueness are encouraged, MLE falls short. Methods optimized for MLE lead to outputs that can be generic, repetitive and incoherent. In this work, we use a Generative Adversarial Network framework to alleviate this problem. We evaluate our framework on poetry, lyrics and metaphor datasets, each with widely different characteristics, and report better performance of our objective function over other generative models.
Designing dialogue systems: A mean, grumpy, sarcastic chatbot in the browser
Authors: Suzana Ilić, Reiichiro Nakano, Ivo Hajnal
Date: 2019-09-20 00:00:00
Tasks: Chatbot, Decoder, Question Answering

In this work we explore a deep learning-based dialogue system that generates sarcastic and humorous responses from a conversation design perspective. We trained a seq2seq model on a carefully curated dataset of 3000 question-answering pairs, the core of our mean, grumpy, sarcastic chatbot. We show that end-to-end systems learn patterns very quickly from small datasets and thus, are able to transfer simple linguistic structures representing abstract concepts to unseen settings. We also deploy our LSTM-based encoder-decoder model in the browser, where users can directly interact with the chatbot. Human raters evaluated linguistic quality, creativity and human-like traits, revealing the system's strengths, limitations and potential for future research.
Multi-user Augmented Reality Application for Video Communication in Virtual Space
Authors: Kumar Mridul, M. Ramanathan, Kunal Ahirwar, Mansi Sharma
Date: 2019-09-20 00:00:00

Communication is the most useful tool to impart knowledge, understand ideas, clarify thoughts and expressions, organize plan and manage every single day-to-day activity. Although there are different modes of communication, physical barrier always affects the clarity of the message due to the absence of body language and facial expressions. These barriers are overcome by video calling, which is technically the most advance mode of communication at present. The proposed work concentrates around the concept of video calling in a more natural and seamless way using Augmented Reality (AR). AR can be helpful in giving the users an experience of physical presence in each other's environment. Our work provides an entirely new platform for video calling, wherein the users can enjoy the privilege of their own virtual space to interact with the individual's environment. Moreover, there is no limitation of sharing the same screen space. Any number of participants can be accommodated over a single conference without having to compromise the screen size.
Nonparametric learning for impulse control problems
Authors: Sören Christensen, Claudia Strauch
Date: 2019-09-20 00:00:00

One of the fundamental assumptions in stochastic control of continuous time processes is that the dynamics of the underlying (diffusion) process is known. This is, however, usually obviously not fulfilled in practice. On the other hand, over the last decades, a rich theory for nonparametric estimation of the drift (and volatility) for continuous time processes has been developed. The aim of this paper is bringing together techniques from stochastic control with methods from statistics for stochastic processes to find a way to both learn the dynamics of the underlying process and control in a reasonable way at the same time. More precisely, we study a long-term average impulse control problem, a stochastic version of the classical Faustmann timber harvesting problem. One of the problems that immediately arises is an exploration-exploitation dilemma as is well known for problems in machine learning. We propose a way to deal with this issue by combining exploration and exploitation periods in a suitable way. Our main finding is that this construction can be based on the rates of convergence of estimators for the invariant density. Using this, we obtain that the average cumulated regret is of uniform order $O({T^{-1/3}})$.
Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages
Authors: Yunsu Kim, Petre Petrov, Pavel Petrushkov, Shahram Khadivi, Hermann Ney
Date: 2019-09-20 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Decoder, Machine Translation, NMT, Transfer Learning, Translation, valid

We present effective pre-training strategies for neural machine translation (NMT) using parallel corpora involving a pivot language, i.e., source-pivot and pivot-target, leading to a significant improvement in source-target translation. We propose three methods to increase the relation among source, pivot, and target languages in the pre-training: 1) step-wise training of a single model for different language pairs, 2) additional adapter component to smoothly connect pre-trained encoder and decoder, and 3) cross-lingual encoder training via autoencoding of the pivot language. Our methods greatly outperform multilingual models up to +2.6% BLEU in WMT 2019 French-German and German-Czech tasks. We show that our improvements are valid also in zero-shot/zero-resource scenarios.
Redirection Controller Using Reinforcement Learning
Authors: Yuchen Chang, Keigo Matsumoto, Takuji Narumi, Tomohiro Tanikawa, Michitaka Hirose
Date: 2019-09-20 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

There is a growing demand for redirected walking (RDW) techniques and their application. To apply appropriate RDW methods and manipulation, the RDW controllers are predominantly used. There are three types of RDW controllers: direct scripted controller, generalized controller, and predictive controller. The scripted controller type pre-scripts the mapping between the real and virtual environments. The generalized controller type employs the RDW method and manipulation quantities according to a certain procedure depending on the user's position in relation to the real space. This approach has the potential to be reused in any environment; however, it is not fully optimized. The predictive controller type predicts the user's future path using the user's behavior and manages RDW techniques. This approach is highly anticipated to be very effective and versatile; however, it has not been sufficiently developed. This paper proposes a novel RDW controller using reinforcement learning (RL) with advanced plannability/versatility. Our simulation experiments indicate that the proposed method can reduce the number of reset manipulations, which is one of the indicators of the effectiveness of the RDW controller, compared to the generalized controller under real environments with many obstacles. Meanwhile, the experimental results also showed that the gain output by the proposed method oscillates. The results of a user study conducted showed that the proposed RDW controller can reduce the number of resets compared to the conventional generalized controller. Furthermore, no adverse effects such as cybersickness associated with the oscillation of the output gain were evinced. The simulation and user studies demonstrate that the proposed RDW controller with RL outperforms the existing generalized controllers and can be applied to users.
An Empirical Exploration of Deep Recurrent Connections and Memory Cells Using Neuro-Evolution
Authors: Travis J. Desell, AbdElRahman A. ElSaid, Alexander G. Ororbia
Date: 2019-09-20 00:00:00
Tasks: Neural Architecture Search, Time Series, Time Series Analysis, Time Series Prediction

Neuro-evolution and neural architecture search algorithms have gained increasing interest due to the challenges involved in designing optimal artificial neural networks (ANNs). While these algorithms have been shown to possess the potential to outperform the best human crafted architectures, a less common use of them is as a tool for analysis of ANN structural components and connectivity structures. In this work, we focus on this particular use-case to develop a rigorous examination and comparison framework for analyzing recurrent neural networks (RNNs) applied to time series prediction using the novel neuro-evolutionary process known as Evolutionary eXploration of Augmenting Memory Models (EXAMM). Specifically, we use our EXAMM-based analysis to investigate the capabilities of recurrent memory cells and the generalization ability afforded by various complex recurrent connectivity patterns that span one or more steps in time, i.e., deep recurrent connections. EXAMM, in this study, was used to train over 10.56 million RNNs in 5,280 repeated experiments with varying components. While many modern, often hand-crafted RNNs rely on complex memory cells (which have internal recurrent connections that only span a single time step) operating under the assumption that these sufficiently latch information and handle long term dependencies, our results show that networks evolved with deep recurrent connections perform significantly better than those without. More importantly, in some cases, the best performing RNNs consisted of only simple neurons and deep time skip connections, without any memory cells. These results strongly suggest that utilizing deep time skip connections in RNNs for time series data prediction not only deserves further, dedicated study, but also demonstrate the potential of neuro-evolution as a means to better study, understand, and train effective RNNs.
Trivializations for Gradient-Based Optimization on Manifolds
Authors: Mario Lezcano-Casado
Date: 2019-09-20 00:00:00

We introduce a framework to study the transformation of problems with manifold constraints into unconstrained problems through parametrizations in terms of a Euclidean space. We call these parametrizations "trivializations". We prove conditions under which a trivialization is sound in the context of gradient-based optimization and we show how two large families of trivializations have overall favorable properties, but also suffer from a performance issue. We then introduce "dynamic trivializations", which solve this problem, and we show how these form a family of optimization methods that lie between trivializations and Riemannian gradient descent, and combine the benefits of both of them. We then show how to implement these two families of trivializations in practice for different matrix manifolds. To this end, we prove a formula for the gradient of the exponential of matrices, which can be of practical interest on its own. Finally, we show how dynamic trivializations improve the performance of existing methods on standard tasks designed to test long-term memory within neural networks.