pwc-archive/papers-with-abstracts huggingface dataset:
Countdown Regression: Sharp and Calibrated Survival Predictions
Authors: Anand Avati, Tony Duan, Sharon Zhou, Kenneth Jung, Nigam H. Shah, Andrew Ng
Date: 2018-06-21 00:00:00
Tasks: Decision Making, Mortality Prediction, regression, Survival Prediction

Probabilistic survival predictions from models trained with Maximum Likelihood Estimation (MLE) can have high, and sometimes unacceptably high variance. The field of meteorology, where the paradigm of maximizing sharpness subject to calibration is popular, has addressed this problem by using scoring rules beyond MLE, such as the Continuous Ranked Probability Score (CRPS). In this paper we present the \emph{Survival-CRPS}, a generalization of the CRPS to the survival prediction setting, with right-censored and interval-censored variants. We evaluate our ideas on the mortality prediction task using two different Electronic Health Record (EHR) data sets (STARR and MIMIC-III) covering millions of patients, with suitable deep neural network architectures: a Recurrent Neural Network (RNN) for STARR and a Fully Connected Network (FCN) for MIMIC-III. We compare results between the two scoring rules while keeping the network architecture and data fixed, and show that models trained with Survival-CRPS result in sharper predictive distributions compared to those trained by MLE, while still maintaining calibration.
Fashion-Gen: The Generative Fashion Dataset and Challenge
Authors: Negar Rostamzadeh, Seyedarian Hosseini, Thomas Boquet, Wojciech Stokowiec, Ying Zhang, Christian Jauvin, Chris Pal
Date: 2018-06-21 00:00:00
Tasks: Image Generation

We introduce a new dataset of 293,008 high definition (1360 x 1360 pixels)
fashion images paired with item descriptions provided by professional stylists.
Each item is photographed from a variety of angles. We provide baseline results
on 1) high-resolution image generation, and 2) image generation conditioned on
the given text descriptions. We invite the community to improve upon these
baselines. In this paper, we also outline the details of a challenge that we
are launching based upon this dataset.
Par4Sim -- Adaptive Paraphrasing for Text Simplification
Authors: Seid Muhie Yimam, Chris Biemann
Date: 2018-06-21 00:00:00
Proceeding: COLING 2018 8
Tasks: Learning-To-Rank, Text Simplification

Learning from a real-world data stream and continuously updating the model
without explicit supervision is a new challenge for NLP applications with
machine learning components. In this work, we have developed an adaptive
learning system for text simplification, which improves the underlying
learning-to-rank model from usage data, i.e. how users have employed the system
for the task of simplification. Our experimental result shows that, over a
period of time, the performance of the embedded paraphrase ranking model
increases steadily improving from a score of 62.88% up to 75.70% based on the
NDCG@10 evaluation metrics. To our knowledge, this is the first study where an
NLP component is adaptively improved through usage.
The Online Saddle Point Problem and Online Convex Optimization with Knapsacks
Authors: Adrian Rivera, He Wang, Huan Xu
Date: 2018-06-21 00:00:00

We study the online saddle point problem, an online learning problem where at each iteration a pair of actions need to be chosen without knowledge of the current and future (convex-concave) payoff functions. The objective is to minimize the gap between the cumulative payoffs and the saddle point value of the aggregate payoff function, which we measure using a metric called "SP-Regret". The problem generalizes the online convex optimization framework but here we must ensure both players incur cumulative payoffs close to that of the Nash equilibrium of the sum of the games. We propose an algorithm that achieves SP-Regret proportional to $\sqrt{\ln(T)T}$ in the general case, and $\log(T)$ SP-Regret for the strongly convex-concave case. We also consider the special case where the payoff functions are bilinear and the decision sets are the probability simplex. In this setting we are able to design algorithms that reduce the bounds on SP-Regret from a linear dependence in the dimension of the problem to a \textit{logarithmic} one. We also study the problem under bandit feedback and provide an algorithm that achieves sublinear SP-Regret. We then consider an online convex optimization with knapsacks problem motivated by a wide variety of applications such as: dynamic pricing, auctions, and crowdsourcing. We relate this problem to the online saddle point problem and establish $O(\sqrt{T})$ regret using a primal-dual algorithm.
Learning Graph Weighted Models on Pictures
Authors: Philip Amortila, Guillaume Rabusseau
Date: 2018-06-21 00:00:00

Graph Weighted Models (GWMs) have recently been proposed as a natural
generalization of weighted automata over strings and trees to arbitrary
families of labeled graphs (and hypergraphs). A GWM generically associates a
labeled graph with a tensor network and computes a value by successive
contractions directed by its edges. In this paper, we consider the problem of
learning GWMs defined over the graph family of pictures (or 2-dimensional
words). As a proof of concept, we consider regression and classification tasks
over the simple Bars & Stripes and Shifting Bits picture languages and provide
an experimental study investigating whether these languages can be learned in
the form of a GWM from positive and negative examples using gradient-based
methods. Our results suggest that this is indeed possible and that
investigating the use of gradient-based methods to learn picture series and
functions computed by GWMs over other families of graphs could be a fruitful
direction.
Are good local minima wide in sparse recovery?
Authors: Michael Moeller, Otmar Loffeld, Juergen Gall, Felix Krahmer
Date: 2018-06-21 00:00:00
Tasks: compressed sensing

The idea of compressed sensing is to exploit representations in suitable
(overcomplete) dictionaries that allow to recover signals far beyond the
Nyquist rate provided that they admit a sparse representation in the respective
dictionary. The latter gives rise to the sparse recovery problem of finding the
best sparse linear approximation of given data in a given generating system. In
this paper we analyze the iterative hard thresholding (IHT) algorithm as one of
the most popular greedy methods for solving the sparse recovery problem, and
demonstrate that systematically perturbing the IHT algorithm by adding noise to
intermediate iterates yields improved results. Further improvements can be
obtained by entirely rephrasing the problem as a parametric deep-learning-type
of optimization problem. By introducing perturbations via dropout, we
demonstrate to significantly outperform the classical IHT algorithm, obtaining
$3$ to $6$ times lower average objective errors.
How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning Experiments
Authors: Cédric Colas, Olivier Sigaud, Pierre-Yves Oudeyer
Date: 2018-06-21 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Consistently checking the statistical significance of experimental results is
one of the mandatory methodological steps to address the so-called
"reproducibility crisis" in deep reinforcement learning. In this tutorial
paper, we explain how the number of random seeds relates to the probabilities
of statistical errors. For both the t-test and the bootstrap confidence
interval test, we recall theoretical guidelines to determine the number of
random seeds one should use to provide a statistically significant comparison
of the performance of two algorithms. Finally, we discuss the influence of
deviations from the assumptions usually made by statistical tests. We show that
they can lead to inaccurate evaluations of statistical errors and provide
guidelines to counter these negative effects. We make our code available to
perform the tests.
Layouts from Panoramic Images with Geometry and Deep Learning
Authors: Clara Fernandez-Labrador, Alejandro Perez-Yus, Gonzalo Lopez-Nicolas, Jose J. Guerrero
Date: 2018-06-21 00:00:00
Tasks: Deep Learning

In this paper, we propose a novel procedure for 3D layout recovery of indoor
scenes from single 360 degrees panoramic images. With such images, all scene is
seen at once, allowing to recover closed geometries. Our method combines
strategically the accuracy provided by geometric reasoning (lines and vanishing
points) with the higher level of data abstraction and pattern recognition
achieved by deep learning techniques (edge and normal maps). Thus, we extract
structural corners from which we generate layout hypotheses of the room
assuming Manhattan world. The best layout model is selected, achieving good
performance on both simple rooms (box-type) and complex shaped rooms (with more
than four walls). Experiments of the proposed approach are conducted within two
public datasets, SUN360 and Stanford (2D-3D-S) demonstrating the advantages of
estimating layouts by combining geometry and deep learning and the
effectiveness of our proposal with respect to the state of the art.
Don't only Feel Read: Using Scene text to understand advertisements
Authors: Arka Ujjal Dey, Suman K. Ghosh, Ernest Valveny
Date: 2018-06-21 00:00:00
Tasks: General Classification

We propose a framework for automated classification of Advertisement Images, using not just Visual features but also Textual cues extracted from embedded text. Our approach takes inspiration from the assumption that Ad images contain meaningful textual content, that can provide discriminative semantic interpretetion, and can thus aid in classifcation tasks. To this end, we develop a framework using off-the-shelf components, and demonstrate the effectiveness of Textual cues in semantic Classfication tasks.
Complex Gated Recurrent Neural Networks
Authors: Moritz Wolter, Angela Yao
Date: 2018-06-21 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Human motion prediction, motion prediction, Music Transcription, Time Series, Time Series Analysis

Complex numbers have long been favoured for digital signal processing, yet
complex representations rarely appear in deep learning architectures. RNNs,
widely used to process time series and sequence information, could greatly
benefit from complex representations. We present a novel complex gated
recurrent cell, which is a hybrid cell combining complex-valued and
norm-preserving state transitions with a gating mechanism. The resulting RNN
exhibits excellent stability and convergence properties and performs
competitively on the synthetic memory and adding task, as well as on the
real-world tasks of human motion prediction.
Learning Multimodal Representations for Unseen Activities
Authors: AJ Piergiovanni, Michael S. Ryoo
Date: 2018-06-21 00:00:00
Tasks: General Classification, Temporal Action Localization

We present a method to learn a joint multimodal representation space that enables recognition of unseen activities in videos. We first compare the effect of placing various constraints on the embedding space using paired text and video data. We also propose a method to improve the joint embedding space using an adversarial formulation, allowing it to benefit from unpaired text and video data. By using unpaired text data, we show the ability to learn a representation that better captures unseen activities. In addition to testing on publicly available datasets, we introduce a new, large-scale text/video dataset. We experimentally confirm that using paired and unpaired data to learn a shared embedding space benefits three difficult tasks (i) zero-shot activity classification, (ii) unsupervised activity discovery, and (iii) unseen activity captioning, outperforming the state-of-the-arts.
Log Skeletons: A Classification Approach to Process Discovery
Authors: H. M. W. Verbeek, R. Medeiros de Carvalho
Date: 2018-06-21 00:00:00
Tasks: Classification, General Classification

To test the effectiveness of process discovery algorithms, a Process
Discovery Contest (PDC) has been set up. This PDC uses a classification
approach to measure this effectiveness: The better the discovered model can
classify whether or not a new trace conforms to the event log, the better the
discovery algorithm is supposed to be. Unfortunately, even the state-of-the-art
fully-automated discovery algorithms score poorly on this classification. Even
the best of these algorithms, the Inductive Miner, scored only 147 correct
classified traces out of 200 traces on the PDC of 2017. This paper introduces
the rule-based log skeleton model, which is closely related to the Declare
constraint model, together with a way to classify traces using this model. This
classification using log skeletons is shown to score better on the PDC of 2017
than state-of-the-art discovery algorithms: 194 out of 200. As a result, one
can argue that the fully-automated algorithm to construct (or: discover) a log
skeleton from an event log outperforms existing state-of-the-art
fully-automated discovery algorithms.
Finding Person Relations in Image Data of the Internet Archive
Authors: Eric Müller-Budack, Kader Pustu-Iren, Sebastian Diering, Ralph Ewerth
Date: 2018-06-21 00:00:00
Tasks: Face Recognition, Person Recognition

The multimedia content in the World Wide Web is rapidly growing and contains valuable information for many applications in different domains. For this reason, the Internet Archive initiative has been gathering billions of time-versioned web pages since the mid-nineties. However, the huge amount of data is rarely labeled with appropriate metadata and automatic approaches are required to enable semantic search. Normally, the textual content of the Internet Archive is used to extract entities and their possible relations across domains such as politics and entertainment, whereas image and video content is usually neglected. In this paper, we introduce a system for person recognition in image content of web news stored in the Internet Archive. Thus, the system complements entity recognition in text and allows researchers and analysts to track media coverage and relations of persons more precisely. Based on a deep learning face recognition approach, we suggest a system that automatically detects persons of interest and gathers sample material, which is subsequently used to identify them in the image data of the Internet Archive. We evaluate the performance of the face recognition system on an appropriate standard benchmark dataset and demonstrate the feasibility of the approach with two use cases.
Reductive Clustering: An Efficient Linear-time Graph-based Divisive Cluster Analysis Approach
Authors: Ching Tarn, Yinan Zhang, Ye Feng
Date: 2018-06-21 00:00:00
Tasks: Clustering

We propose an efficient linear-time graph-based divisive cluster analysis approach called Reductive Clustering. The approach tries to reveal the hierarchical structural information through reducing the graph into a more concise one repeatedly. With the reductions, the original graph can be divided into subgraphs recursively, and a lite informative dendrogram is constructed based on the divisions. The reduction consists of three steps: selection, connection, and partition. First a subset of vertices of the graph are selected as representatives to build a concise graph. The representatives are re-connected to maintain a consistent structure with the previous graph. If possible, the concise graph is divided into subgraphs, and each subgraph is further reduced recursively until the termination condition is met. We discuss the approach, along with several selection and connection methods, in detail both theoretically and experimentally in this paper. Our implementations run in linear time and achieve outstanding performance on various types of datasets. Experimental results show that they outperform state-of-the-art clustering algorithms with significantly less computing resource requirements.
Learning Transposition-Invariant Interval Features from Symbolic Music and Audio
Authors: Stefan Lattner, Maarten Grachten, Gerhard Widmer
Date: 2018-06-21 00:00:00

Many music theoretical constructs (such as scale types, modes, cadences, and
chord types) are defined in terms of pitch intervals---relative distances
between pitches. Therefore, when computer models are employed in music tasks,
it can be useful to operate on interval representations rather than on the raw
musical surface. Moreover, interval representations are
transposition-invariant, valuable for tasks like audio alignment, cover song
detection and music structure analysis. We employ a gated autoencoder to learn
fixed-length, invertible and transposition-invariant interval representations
from polyphonic music in the symbolic domain and in audio. An unsupervised
training method is proposed yielding an organization of intervals in the
representation space which is musically plausible. Based on the
representations, a transposition-invariant self-similarity matrix is
constructed and used to determine repeated sections in symbolic music and in
audio, yielding competitive results in the MIREX task "Discovery of Repeated
Themes and Sections".
Synaptic partner prediction from point annotations in insect brains
Authors: Julia Buhmann, Renate Krause, Rodrigo Ceballos Lentini, Nils Eckstein, Matthew Cook, Srinivas Turaga, Jan Funke
Date: 2018-06-21 00:00:00

High-throughput electron microscopy allows recording of lar- ge stacks of
neural tissue with sufficient resolution to extract the wiring diagram of the
underlying neural network. Current efforts to automate this process focus
mainly on the segmentation of neurons. However, in order to recover a wiring
diagram, synaptic partners need to be identi- fied as well. This is especially
challenging in insect brains like Drosophila melanogaster, where one
presynaptic site is associated with multiple post- synaptic elements. Here we
propose a 3D U-Net architecture to directly identify pairs of voxels that are
pre- and postsynaptic to each other. To that end, we formulate the problem of
synaptic partner identification as a classification problem on long-range edges
between voxels to encode both the presence of a synaptic pair and its
direction. This formulation allows us to directly learn from synaptic point
annotations instead of more ex- pensive voxel-based synaptic cleft or vesicle
annotations. We evaluate our method on the MICCAI 2016 CREMI challenge and
improve over the current state of the art, producing 3% fewer errors than the
next best method.
Metadata Enrichment of Multi-Disciplinary Digital Library: A Semantic-based Approach
Authors: Hussein T. Al-Natsheh, Lucie Martinet, Fabrice Muhlenbach, Fabien Rico, Djamel A. Zighed
Date: 2018-06-21 00:00:00
Tasks: Articles, Information Retrieval, Retrieval

In the scientific digital libraries, some papers from different research
communities can be described by community-dependent keywords even if they share
a semantically similar topic. Articles that are not tagged with enough keyword
variations are poorly indexed in any information retrieval system which limits
potentially fruitful exchanges between scientific disciplines. In this paper,
we introduce a novel experimentally designed pipeline for multi-label
semantic-based tagging developed for open-access metadata digital libraries.
The approach starts by learning from a standard scientific categorization and a
sample of topic tagged articles to find semantically relevant articles and
enrich its metadata accordingly. Our proposed pipeline aims to enable
researchers reaching articles from various disciplines that tend to use
different terminologies. It allows retrieving semantically relevant articles
given a limited known variation of search terms. In addition to achieving an
accuracy that is higher than an expanded query based method using a topic
synonym set extracted from a semantic network, our experiments also show a
higher computational scalability versus other comparable techniques. We created
a new benchmark extracted from the open-access metadata of a scientific digital
library and published it along with the experiment code to allow further
research in the topic.
DPP-Net: Device-aware Progressive Search for Pareto-optimal Neural Architectures
Authors: Jin-Dong Dong, An-Chieh Cheng, Da-Cheng Juan, Wei Wei, Min Sun
Date: 2018-06-21 00:00:00
Proceeding: ECCV 2018 9
Tasks: GPU, image-classification, Image Classification, Language Modeling, Language Modelling

Recent breakthroughs in Neural Architectural Search (NAS) have achieved
state-of-the-art performances in applications such as image classification and
language modeling. However, these techniques typically ignore device-related
objectives such as inference time, memory usage, and power consumption.
Optimizing neural architecture for device-related objectives is immensely
crucial for deploying deep networks on portable devices with limited computing
resources. We propose DPP-Net: Device-aware Progressive Search for
Pareto-optimal Neural Architectures, optimizing for both device-related (e.g.,
inference time and memory usage) and device-agnostic (e.g., accuracy and model
size) objectives. DPP-Net employs a compact search space inspired by current
state-of-the-art mobile CNNs, and further improves search efficiency by
adopting progressive search (Liu et al. 2017). Experimental results on CIFAR-10
are poised to demonstrate the effectiveness of Pareto-optimal networks found by
DPP-Net, for three different devices: (1) a workstation with Titan X GPU, (2)
NVIDIA Jetson TX1 embedded system, and (3) mobile phone with ARM Cortex-A53.
Compared to CondenseNet and NASNet (Mobile), DPP-Net achieves better
performances: higher accuracy and shorter inference time on various devices.
Additional experimental results show that models found by DPP-Net also achieve
considerably-good performance on ImageNet as well.
Probabilistic PARAFAC2
Authors: Philip J. H. Jørgensen, Søren F. V. Nielsen, Jesper L. Hinrich, Mikkel N. Schmidt, Kristoffer H. Madsen, Morten Mørup
Date: 2018-06-21 00:00:00

The PARAFAC2 is a multimodal factor analysis model suitable for analyzing
multi-way data when one of the modes has incomparable observation units, for
example because of differences in signal sampling or batch sizes. A fully
probabilistic treatment of the PARAFAC2 is desirable in order to improve
robustness to noise and provide a well founded principle for determining the
number of factors, but challenging because the factor loadings are constrained
to be orthogonal. We develop two probabilistic formulations of the PARAFAC2
along with variational procedures for inference: In the one approach, the mean
values of the factor loadings are orthogonal leading to closed form variational
updates, and in the other, the factor loadings themselves are orthogonal using
a matrix Von Mises-Fisher distribution. We contrast our probabilistic
formulation to the conventional direct fitting algorithm based on maximum
likelihood. On simulated data and real fluorescence spectroscopy and gas
chromatography-mass spectrometry data, we compare our approach to the
conventional PARAFAC2 model estimation and find that the probabilistic
formulation is more robust to noise and model order misspecification. The
probabilistic PARAFAC2 thus forms a promising framework for modeling multi-way
data accounting for uncertainty.