pwc-archive/papers-with-abstracts huggingface dataset:
A User-based Visual Analytics Workflow for Exploratory Model Analysis
Authors: Dylan Cashman, Shah Rukh Humayoun, Florian Heimerl, Kendall Park, Subhajit Das, John Thompson, Bahador Saket, Abigail Mosca, John Stasko, Alex Endert, Michael Gleicher, Remco Chang
Date: 2018-09-27 00:00:00
Tasks: AutoML, Model Selection

Many visual analytics systems allow users to interact with machine learning models towards the goals of data exploration and insight generation on a given dataset. However, in some situations, insights may be less important than the production of an accurate predictive model for future use. In that case, users are more interested in generating of diverse and robust predictive models, verifying their performance on holdout data, and selecting the most suitable model for their usage scenario. In this paper, we consider the concept of Exploratory Model Analysis (EMA), which is defined as the process of discovering and selecting relevant models that can be used to make predictions on a data source. We delineate the differences between EMA and the well-known term exploratory data analysis in terms of the desired outcome of the analytic process: insights into the data or a set of deployable models. The contributions of this work are a visual analytics system workflow for EMA, a user study, and two use cases validating the effectiveness of the workflow. We found that our system workflow enabled users to generate complex models, to assess them for various qualities, and to select the most relevant model for their task.
Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning
Authors: Daniel C. Castro, Jeremy Tan, Bernhard Kainz, Ender Konukoglu, Ben Glocker
Date: 2018-09-27 00:00:00
Proceeding: ICLR 2019 5
Tasks: Diversity, Domain Adaptation, Outlier Detection, Representation Learning

Revealing latent structure in data is an active field of research, having introduced exciting technologies such as variational autoencoders and adversarial networks, and is essential to push machine learning towards unsupervised knowledge discovery. However, a major challenge is the lack of suitable benchmarks for an objective and quantitative evaluation of learned representations. To address this issue we introduce Morpho-MNIST, a framework that aims to answer: "to what extent has my model learned to represent specific factors of variation in the data?" We extend the popular MNIST dataset by adding a morphometric analysis enabling quantitative comparison of trained models, identification of the roles of latent variables, and characterisation of sample diversity. We further propose a set of quantifiable perturbations to assess the performance of unsupervised and supervised methods on challenging tasks such as outlier detection and domain adaptation. Data and code are available at https://github.com/dccastro/Morpho-MNIST.
Building a Lemmatizer and a Spell-checker for Sorani Kurdish
Authors: Shahin Salavati, Sina Ahmadi
Date: 2018-09-27 00:00:00
Tasks: Language Modeling, Language Modelling, Lemmatization

The present paper aims at presenting a lemmatization and a word-level error
correction system for Sorani Kurdish. We propose a hybrid approach based on the
morphological rules and a n-gram language model. We have called our
lemmatization and error correction systems Peyv and R\^en\^us respectively,
which are the first tools presented for Sorani Kurdish to the best of our
knowledge. The Peyv lemmatizer has shown 86.7% accuracy. As for R\^en\^us,
using a lexicon, we have obtained 96.4% accuracy while without a lexicon, the
correction system has 87% accuracy. As two fundamental text processing tools,
these tools can pave the way for further researches on more natural language
processing applications for Sorani Kurdish.
An Introduction to Probabilistic Programming
Authors: Jan-Willem van de Meent, Brooks Paige, Hongseok Yang, Frank Wood
Date: 2018-09-27 00:00:00
Tasks: Probabilistic Programming

This book is a graduate-level introduction to probabilistic programming. It not only provides a thorough background for anyone wishing to use a probabilistic programming system, but also introduces the techniques needed to design and build these systems. It is aimed at people who have an undergraduate-level understanding of either or, ideally, both probabilistic machine learning and programming languages. We start with a discussion of model-based reasoning and explain why conditioning is a foundational computation central to the fields of probabilistic machine learning and artificial intelligence. We then introduce a first-order probabilistic programming language (PPL) whose programs correspond to graphical models with a known, finite, set of random variables. In the context of this PPL we introduce fundamental inference algorithms and describe how they can be implemented. We then turn to higher-order probabilistic programming languages. Programs in such languages can define models with dynamic computation graphs, which may not instantiate the same set of random variables in each execution. Inference requires methods that generate samples by repeatedly evaluating the program. Foundational algorithms for this kind of language are discussed in the context of an interface between program executions and an inference controller. Finally we consider the intersection of probabilistic and differentiable programming. We begin with a discussion of automatic differentiation, and how it can be used to implement efficient inference methods based on Hamiltonian Monte Carlo. We then discuss gradient-based maximum likelihood estimation in programs that are parameterized using neural networks, how to amortize inference using by learning neural approximations to the program posterior, and how language features impact the design of deep probabilistic programming systems.
On the loss landscape of a class of deep neural networks with no bad local valleys
Authors: Quynh Nguyen, Mahesh Chandra Mukkamala, Matthias Hein
Date: 2018-09-27 00:00:00
Proceeding: ICLR 2019 5

We identify a class of over-parameterized deep neural networks with standard
activation functions and cross-entropy loss which provably have no bad local
valley, in the sense that from any point in parameter space there exists a
continuous path on which the cross-entropy loss is non-increasing and gets
arbitrarily close to zero. This implies that these networks have no sub-optimal
strict local minima.
Controllable Neural Story Plot Generation via Reward Shaping
Authors: Pradyumna Tambwekar, Murtaza Dhuliawala, Lara J. Martin, Animesh Mehta, Brent Harrison, Mark O. Riedl
Date: 2018-09-27 00:00:00
Tasks: Language Modeling, Language Modelling, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sentence, Story Completion, Story Generation

Language-modeling--based approaches to story plot generation attempt to construct a plot by sampling from a language model (LM) to predict the next character, word, or sentence to add to the story. LM techniques lack the ability to receive guidance from the user to achieve a specific goal, resulting in stories that don't have a clear sense of progression and lack coherence. We present a reward-shaping technique that analyzes a story corpus and produces intermediate rewards that are backpropagated into a pre-trained LM in order to guide the model towards a given goal. Automated evaluations show our technique can create a model that generates story plots which consistently achieve a specified goal. Human-subject studies show that the generated stories have more plausible event ordering than baseline plot generation techniques.
A Qualitative Comparison of CoQA, SQuAD 2.0 and QuAC
Authors: Mark Yatskar
Date: 2018-09-27 00:00:00
Proceeding: NAACL 2019 6
Tasks: Question Answering

We compare three new datasets for question answering: SQuAD 2.0, QuAC, and CoQA, along several of their new features: (1) unanswerable questions, (2) multi-turn interactions, and (3) abstractive answers. We show that the datasets provide complementary coverage of the first two aspects, but weak coverage of the third. Because of the datasets' structural similarity, a single extractive model can be easily adapted to any of the datasets and we show improved baseline results on both SQuAD 2.0 and CoQA. Despite the similarity, models trained on one dataset are ineffective on another dataset, but we find moderate performance improvement through pretraining. To encourage cross-evaluation, we release code for conversion between datasets at https://github.com/my89/co-squac .
Dataset: Rare Event Classification in Multivariate Time Series
Authors: Chitta Ranjan, Mahendranath Reddy, Markku Mustonen, Kamran Paynabar, Karim Pourak
Date: 2018-09-27 00:00:00
Tasks: Classification, General Classification, Time Series, Time Series Analysis

A real-world dataset is provided from a pulp-and-paper manufacturing industry. The dataset comes from a multivariate time series process. The data contains a rare event of paper break that commonly occurs in the industry. The data contains sensor readings at regular time-intervals (x's) and the event label (y). The primary purpose of the data is thought to be building a classification model for early prediction of the rare event. However, it can also be used for multivariate time series data exploration and building other supervised and unsupervised models.
Semantic Topic Analysis of Traffic Camera Images
Authors: Jeffrey Liu, Andrew Weinert, Saurabh Amin
Date: 2018-09-27 00:00:00
Tasks: Data Compression, Object Recognition

Traffic cameras are commonly deployed monitoring components in road
infrastructure networks, providing operators visual information about
conditions at critical points in the network. However, human observers are
often limited in their ability to process simultaneous information sources.
Recent advancements in computer vision, driven by deep learning methods, have
enabled general object recognition, unlocking opportunities for camera-based
sensing beyond the existing human observer paradigm. In this paper, we present
a Natural Language Processing (NLP)-inspired approach, entitled
Bag-of-Label-Words (BoLW), for analyzing image data sets using exclusively
textual labels. The BoLW model represents the data in a conventional matrix
form, enabling data compression and decomposition techniques, while preserving
semantic interpretability. We apply the Latent Dirichlet Allocation (LDA) topic
model to decompose the label data into a small number of semantic topics. To
illustrate our approach, we use freeway camera images collected from the Boston
area between December 2017-January 2018. We analyze the cameras' sensitivity to
weather events; identify temporal traffic patterns; and analyze the impact of
infrequent events, such as the winter holidays and the "bomb cyclone" winter
storm. This study demonstrates the flexibility of our approach, which allows us
to analyze weather events and freeway traffic using only traffic camera image
labels.
Learning Pose Estimation for High-Precision Robotic Assembly Using Simulated Depth Images
Authors: Yuval Litvak, Armin Biess, Aharon Bar-Hillel
Date: 2018-09-27 00:00:00
Tasks: Pose Estimation

Most of industrial robotic assembly tasks today require fixed initial
conditions for successful assembly. These constraints induce high production
costs and low adaptability to new tasks. In this work we aim towards flexible
and adaptable robotic assembly by using 3D CAD models for all parts to be
assembled. We focus on a generic assembly task - the Siemens Innovation
Challenge - in which a robot needs to assemble a gear-like mechanism with high
precision into an operating system. To obtain the millimeter-accuracy required
for this task and industrial settings alike, we use a depth camera mounted near
the robot end-effector. We present a high-accuracy two-stage pose estimation
procedure based on deep convolutional neural networks, which includes
detection, pose estimation, refinement, and handling of near- and full
symmetries of parts. The networks are trained on simulated depth images with
means to ensure successful transfer to the real robot. We obtain an average
pose estimation error of 2.16 millimeters and 0.64 degree leading to 91%
success rate for robotic assembly of randomly distributed parts. To the best of
our knowledge, this is the first time that the Siemens Innovation Challenge is
fully addressed, with all the parts assembled with high success rates.
Weakly-Supervised Localization and Classification of Proximal Femur Fractures
Authors: Amelia Jiménez-Sánchez, Anees Kazi, Shadi Albarqouni, Sonja Kirchhoff, Alexandra Sträter, Peter Biberthaler, Diana Mateus, Nassir Navab
Date: 2018-09-27 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Transfer Learning

In this paper, we target the problem of fracture classification from clinical
X-Ray images towards an automated Computer Aided Diagnosis (CAD) system.
Although primarily dealing with an image classification problem, we argue that
localizing the fracture in the image is crucial to make good class predictions.
Therefore, we propose and thoroughly analyze several schemes for simultaneous
fracture localization and classification. We show that using an auxiliary
localization task, in general, improves the classification performance.
Moreover, it is possible to avoid the need for additional localization
annotations thanks to recent advancements in weakly-supervised deep learning
approaches. Among such approaches, we investigate and adapt Spatial
Transformers (ST), Self-Transfer Learning (STL), and localization from global
pooling layers. We provide a detailed quantitative and qualitative validation
on a dataset of 1347 femur fractures images and report high accuracy with
regard to inter-expert correlation values reported in the literature. Our
investigations show that i) lesion localization improves the classification
outcome, ii) weakly-supervised methods improve baseline classification without
any additional cost, iii) STL guides feature activations and boost performance.
We plan to make both the dataset and code available.
Supervised Nonnegative Matrix Factorization to Predict ICU Mortality Risk
Authors: Guoqing Chao, Chengsheng Mao, Fei Wang, Yuan Zhao, Yuan Luo
Date: 2018-09-27 00:00:00
Tasks: ICU Mortality, Prediction, Time Series, Time Series Analysis

ICU mortality risk prediction is a tough yet important task. On one hand, due
to the complex temporal data collected, it is difficult to identify the
effective features and interpret them easily; on the other hand, good
prediction can help clinicians take timely actions to prevent the mortality.
These correspond to the interpretability and accuracy problems. Most existing
methods lack of the interpretability, but recently Subgraph Augmented
Nonnegative Matrix Factorization (SANMF) has been successfully applied to time
series data to provide a path to interpret the features well. Therefore, we
adopted this approach as the backbone to analyze the patient data. One
limitation of the raw SANMF method is its poor prediction ability due to its
unsupervised nature. To deal with this problem, we proposed a supervised SANMF
algorithm by integrating the logistic regression loss function into the NMF
framework and solved it with an alternating optimization procedure. We used the
simulation data to verify the effectiveness of this method, and then we applied
it to ICU mortality risk prediction and demonstrated its superiority over other
conventional supervised NMF methods.
Definition and evaluation of model-free coordination of electrical vehicle charging with reinforcement learning
Authors: Nasrin Sadeghianpourhamami, Johannes Deleu, Chris Develder
Date: 2018-09-27 00:00:00
Tasks: Model Predictive Control, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Initial DR studies mainly adopt model predictive control and thus require
accurate models of the control problem (e.g., a customer behavior model), which
are to a large extent uncertain for the EV scenario. Hence, model-free
approaches, especially based on reinforcement learning (RL) are an attractive
alternative. In this paper, we propose a new Markov decision process (MDP)
formulation in the RL framework, to jointly coordinate a set of EV charging
stations. State-of-the-art algorithms either focus on a single EV, or perform
the control of an aggregate of EVs in multiple steps (e.g., aggregate load
decisions in one step, then a step translating the aggregate decision to
individual connected EVs). On the contrary, we propose an RL approach to
jointly control the whole set of EVs at once. We contribute a new MDP
formulation, with a scalable state representation that is independent of the
number of EV charging stations. Further, we use a batch reinforcement learning
algorithm, i.e., an instance of fitted Q-iteration, to learn the optimal
charging policy. We analyze its performance using simulation experiments based
on a real-world EV charging data. More specifically, we (i) explore the various
settings in training the RL policy (e.g., duration of the period with training
data), (ii) compare its performance to an oracle all-knowing benchmark (which
provides an upper bound for performance, relying on information that is not
available or at least imperfect in practice), (iii) analyze performance over
time, over the course of a full year to evaluate possible performance
fluctuations (e.g, across different seasons), and (iv) demonstrate the
generalization capacity of a learned control policy to larger sets of charging
stations.
Introducing Noise in Decentralized Training of Neural Networks
Authors: Linara Adilova, Nathalie Paul, Peter Schlicht
Date: 2018-09-27 00:00:00

It has been shown that injecting noise into the neural network weights during
the training process leads to a better generalization of the resulting model.
Noise injection in the distributed setup is a straightforward technique and it
represents a promising approach to improve the locally trained models. We
investigate the effects of noise injection into the neural networks during a
decentralized training process. We show both theoretically and empirically that
noise injection has no positive effect in expectation on linear models, though.
However for non-linear neural networks we empirically show that noise injection
substantially improves model quality helping to reach a generalization ability
of a local model close to the serial baseline.
A Note on Spectral Clustering and SVD of Graph Data
Authors: Ziwei Zhang
Date: 2018-09-27 00:00:00
Tasks: Clustering

Spectral clustering and Singular Value Decomposition (SVD) are both widely
used technique for analyzing graph data. In this note, I will present their
connections using simple linear algebra, aiming to provide some in-depth
understanding for future research.
A Coarse-To-Fine Framework For Video Object Segmentation
Authors: Chi Zhang, Alexander Loui
Date: 2018-09-26 00:00:00
Tasks: Object, Segmentation, Semantic Segmentation, Video Object Segmentation, Video Semantic Segmentation

In this study, we develop an unsupervised coarse-to-fine video analysis
framework and prototype system to extract a salient object in a video sequence.
This framework starts from tracking grid-sampled points along temporal frames,
typically using KLT tracking method. The tracking points could be divided into
several groups due to their inconsistent movements. At the same time, the SLIC
algorithm is extended into 3D space to generate supervoxels. Coarse
segmentation is achieved by combining the categorized tracking points and
supervoxels of the corresponding frame in the video sequence. Finally, a
graph-based fine segmentation algorithm is used to extract the moving object in
the scene. Experimental results reveal that this method outperforms the
previous approaches in terms of accuracy and robustness.
Hybrid Noise Removal in Hyperspectral Imagery With a Spatial-Spectral Gradient Network
Authors: Qiang Zhang, Qiangqiang Yuan, Jie Li, Xin-Xin Liu, Huanfeng Shen, Liangpei Zhang
Date: 2018-10-01 00:00:00
Tasks: Denoising

The existence of hybrid noise in hyperspectral images (HSIs) severely
degrades the data quality, reduces the interpretation accuracy of HSIs, and
restricts the subsequent HSIs applications. In this paper, the spatial-spectral
gradient network (SSGN) is presented for mixed noise removal in HSIs. The
proposed method employs a spatial-spectral gradient learning strategy, in
consideration of the unique spatial structure directionality of sparse noise
and spectral differences with additional complementary information for better
extracting intrinsic and deep features of HSIs. Based on a fully cascaded
multi-scale convolutional network, SSGN can simultaneously deal with the
different types of noise in different HSIs or spectra by the use of the same
model. The simulated and real-data experiments undertaken in this study
confirmed that the proposed SSGN performs better at mixed noise removal than
the other state-of-the-art HSI denoising algorithms, in evaluation indices,
visual assessments, and time consumption.
Efficiently testing local optimality and escaping saddles for ReLU networks
Authors: Chulhee Yun, Suvrit Sra, Ali Jadbabaie
Date: 2018-09-28 00:00:00
Proceeding: ICLR 2019 5

We provide a theoretical algorithm for checking local optimality and escaping saddles at nondifferentiable points of empirical risks of two-layer ReLU networks. Our algorithm receives any parameter value and returns: local minimum, second-order stationary point, or a strict descent direction. The presence of $M$ data points on the nondifferentiability of the ReLU divides the parameter space into at most $2^M$ regions, which makes analysis difficult. By exploiting polyhedral geometry, we reduce the total computation down to one convex quadratic program (QP) for each hidden node, $O(M)$ (in)equality tests, and one (or a few) nonconvex QP. For the last QP, we show that our specific problem can be solved efficiently, in spite of nonconvexity. In the benign case, we solve one equality constrained QP, and we prove that projected gradient descent solves it exponentially fast. In the bad case, we have to solve a few more inequality constrained QPs, but we prove that the time complexity is exponential only in the number of inequality constraints. Our experiments show that either benign case or bad case with very few inequality constraints occurs, implying that our algorithm is efficient in most cases.
SurfelMeshing: Online Surfel-Based Mesh Reconstruction
Authors: Thomas Schöps, Torsten Sattler, Marc Pollefeys
Date: 2018-10-01 00:00:00

We address the problem of mesh reconstruction from live RGB-D video, assuming a calibrated camera and poses provided externally (e.g., by a SLAM system). In contrast to most existing approaches, we do not fuse depth measurements in a volume but in a dense surfel cloud. We asynchronously (re)triangulate the smoothed surfels to reconstruct a surface mesh. This novel approach enables to maintain a dense surface representation of the scene during SLAM which can quickly adapt to loop closures. This is possible by deforming the surfel cloud and asynchronously remeshing the surface where necessary. The surfel-based representation also naturally supports strongly varying scan resolution. In particular, it reconstructs colors at the input camera's resolution. Moreover, in contrast to many volumetric approaches, ours can reconstruct thin objects since objects do not need to enclose a volume. We demonstrate our approach in a number of experiments, showing that it produces reconstructions that are competitive with the state-of-the-art, and we discuss its advantages and limitations. The algorithm (excluding loop closure functionality) is available as open source at https://github.com/puzzlepaint/surfelmeshing .