pwc-archive/papers-with-abstracts huggingface dataset:
SMASH: One-Shot Model Architecture Search through HyperNetworks
Authors: Andrew Brock, Theodore Lim, J. M. Ritchie, Nick Weston
Date: 2017-08-17 00:00:00
Proceeding: ICLR 2018 1
Tasks: model, Neural Architecture Search

Designing architectures for deep neural networks requires expert knowledge
and substantial computation time. We propose a technique to accelerate
architecture selection by learning an auxiliary HyperNet that generates the
weights of a main model conditioned on that model's architecture. By comparing
the relative validation performance of networks with HyperNet-generated
weights, we can effectively search over a wide range of architectures at the
cost of a single training run. To facilitate this search, we develop a flexible
mechanism based on memory read-writes that allows us to define a wide range of
network connectivity patterns, with ResNet, DenseNet, and FractalNet blocks as
special cases. We validate our method (SMASH) on CIFAR-10 and CIFAR-100,
STL-10, ModelNet10, and Imagenet32x32, achieving competitive performance with
similarly-sized hand-designed networks. Our code is available at
https://github.com/ajbrock/SMASH
Robust Registration and Geometry Estimation from Unstructured Facial Scans
Authors: Maxim Bazik, Daniel Crispell
Date: 2017-08-17 00:00:00
Tasks: Pose Estimation

Commercial off the shelf (COTS) 3D scanners are capable of generating point
clouds covering visible portions of a face with sub-millimeter accuracy at
close range, but lack the coverage and specialized anatomic registration
provided by more expensive 3D facial scanners. We demonstrate an effective
pipeline for joint alignment of multiple unstructured 3D point clouds and
registration to a parameterized 3D model which represents shape variation of
the human head. Most algorithms separate the problems of pose estimation and
mesh warping, however we propose a new iterative method where these steps are
interwoven. Error decreases with each iteration, showing the proposed approach
is effective in improving geometry and alignment. The approach described is
used to align the NDOff-2007 dataset, which contains 7,358 individual scans at
various poses of 396 subjects. The dataset has a number of full profile scans
which are correctly aligned and contribute directly to the associated mesh
geometry. The dataset in its raw form contains a significant number of
mislabeled scans, which are identified and corrected based on alignment error
using the proposed algorithm. The average point to surface distance between the
aligned scans and the produced geometries is one half millimeter.
Learning Musical Relations using Gated Autoencoders
Authors: Stefan Lattner, Maarten Grachten, Gerhard Widmer
Date: 2017-08-17 00:00:00
Tasks: Open-Ended Question Answering, Rhythm

Music is usually highly structured and it is still an open question how to
design models which can successfully learn to recognize and represent musical
structure. A fundamental problem is that structurally related patterns can have
very distinct appearances, because the structural relationships are often based
on transformations of musical material, like chromatic or diatonic
transposition, inversion, retrograde, or rhythm change. In this preliminary
work, we study the potential of two unsupervised learning techniques -
Restricted Boltzmann Machines (RBMs) and Gated Autoencoders (GAEs) - to capture
pre-defined transformations from constructed data pairs. We evaluate the models
by using the learned representations as inputs in a discriminative task where
for a given type of transformation (e.g. diatonic transposition), the specific
relation between two musical patterns must be recognized (e.g. an upward
transposition of diatonic steps). Furthermore, we measure the reconstruction
error of models when reconstructing musical transformed patterns. Lastly, we
test the models in an analogy-making task. We find that it is difficult to
learn musical transformations with the RBM and that the GAE is much more
adequate for this task, since it is able to learn representations of specific
transformations that are largely content-invariant. We believe these results
show that models such as GAEs may provide the basis for more encompassing music
analysis systems, by endowing them with a better understanding of the
structures underlying music.
Incorporating Copying Mechanism in Image Captioning for Learning Novel Objects
Authors: Ting Yao, Yingwei Pan, Yehao Li, Tao Mei
Date: 2017-08-17 00:00:00
Proceeding: CVPR 2017 7
Tasks: Decoder, Image Captioning, Object Recognition, Sentence

Image captioning often requires a large set of training image-sentence pairs.
In practice, however, acquiring sufficient training pairs is always expensive,
making the recent captioning models limited in their ability to describe
objects outside of training corpora (i.e., novel objects). In this paper, we
present Long Short-Term Memory with Copying Mechanism (LSTM-C) --- a new
architecture that incorporates copying into the Convolutional Neural Networks
(CNN) plus Recurrent Neural Networks (RNN) image captioning framework, for
describing novel objects in captions. Specifically, freely available object
recognition datasets are leveraged to develop classifiers for novel objects.
Our LSTM-C then nicely integrates the standard word-by-word sentence generation
by a decoder RNN with copying mechanism which may instead select words from
novel objects at proper places in the output sentence. Extensive experiments
are conducted on both MSCOCO image captioning and ImageNet datasets,
demonstrating the ability of our proposed LSTM-C architecture to describe novel
objects. Furthermore, superior results are reported when compared to
state-of-the-art deep models.
Towards Syntactic Iberian Polarity Classification
Authors: David Vilares, Marcos Garcia, Miguel A. Alonso, Carlos Gómez-Rodríguez
Date: 2017-08-17 00:00:00
Proceeding: WS 2017 9
Tasks: Classification, General Classification

Lexicon-based methods using syntactic rules for polarity classification rely
on parsers that are dependent on the language and on treebank guidelines. Thus,
rules are also dependent and require adaptation, especially in multilingual
scenarios. We tackle this challenge in the context of the Iberian Peninsula,
releasing the first symbolic syntax-based Iberian system with rules shared
across five official languages: Basque, Catalan, Galician, Portuguese and
Spanish. The model is made available.
A Generative Parser with a Discriminative Recognition Algorithm
Authors: Jianpeng Cheng, Adam Lopez, Mirella Lapata
Date: 2017-08-01 00:00:00
Proceeding: ACL 2017 7
Tasks: Constituency Parsing, Decoder, Language Modeling, Language Modelling, Variational Inference

Generative models defining joint distributions over parse trees and sentences
are useful for parsing and language modeling, but impose restrictions on the
scope of features and are often outperformed by discriminative models. We
propose a framework for parsing and language modeling which marries a
generative model with a discriminative recognition model in an encoder-decoder
setting. We provide interpretations of the framework based on expectation
maximization and variational inference, and show that it enables parsing and
language modeling within a single implementation. On the English Penn
Treen-bank, our framework obtains competitive performance on constituency
parsing while matching the state-of-the-art single-model language modeling
score.
Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flacco
Authors: Pascal Kerschke
Date: 2017-08-17 00:00:00

Choosing the best-performing optimizer(s) out of a portfolio of optimization
algorithms is usually a difficult and complex task. It gets even worse, if the
underlying functions are unknown, i.e., so-called Black-Box problems, and
function evaluations are considered to be expensive. In the case of continuous
single-objective optimization problems, Exploratory Landscape Analysis (ELA) -
a sophisticated and effective approach for characterizing the landscapes of
such problems by means of numerical values before actually performing the
optimization task itself - is advantageous. Unfortunately, until now it has
been quite complicated to compute multiple ELA features simultaneously, as the
corresponding code has been - if at all - spread across multiple platforms or
at least across several packages within these platforms.
  This article presents a broad summary of existing ELA approaches and
introduces flacco, an R-package for feature-based landscape analysis of
continuous and constrained optimization problems. Although its functions
neither solve the optimization problem itself nor the related "Algorithm
Selection Problem (ASP)", it offers easy access to an essential ingredient of
the ASP by providing a wide collection of ELA features on a single platform -
even within a single package. In addition, flacco provides multiple
visualization techniques, which enhance the understanding of some of these
numerical features, and thereby make certain landscape properties more
comprehensible. On top of that, we will introduce the package's build-in, as
well as web-hosted and hence platform-independent, graphical user interface
(GUI), which facilitates the usage of the package - especially for people who
are not familiar with R - making it a very convenient toolbox when working
towards algorithm selection of continuous single-objective optimization
problems.
SCNet: Learning Semantic Correspondence
Authors: Kai Han, Rafael S. Rezende, Bumsub Ham, Kwan-Yee K. Wong, Minsu Cho, Cordelia Schmid, Jean Ponce
Date: 2017-05-11 00:00:00
Proceeding: ICCV 2017 10
Tasks: Semantic correspondence

This paper addresses the problem of establishing semantic correspondences
between images depicting different instances of the same object or scene
category. Previous approaches focus on either combining a spatial regularizer
with hand-crafted features, or learning a correspondence model for appearance
only. We propose instead a convolutional neural network architecture, called
SCNet, for learning a geometrically plausible model for semantic
correspondence. SCNet uses region proposals as matching primitives, and
explicitly incorporates geometric consistency in its loss function. It is
trained on image pairs obtained from the PASCAL VOC 2007 keypoint dataset, and
a comparative evaluation on several standard benchmarks demonstrates that the
proposed approach substantially outperforms both recent deep learning
architectures and previous methods based on hand-crafted features.
Auxiliary Variables for Multi-Dirichlet Priors
Authors: Christoph Carl Kling
Date: 2017-08-17 00:00:00

Bayesian models that mix multiple Dirichlet prior parameters, called
Multi-Dirichlet priors (MD) in this paper, are gaining popularity. Inferring
mixing weights and parameters of mixed prior distributions seems tricky, as
sums over Dirichlet parameters complicate the joint distribution of model
parameters.
  This paper shows a novel auxiliary variable scheme which helps to simplify
the inference for models involving hierarchical MDs and MDPs. Using this
scheme, it is easy to derive fully collapsed inference schemes which allow for
an efficient inference.
Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data
Authors: Thorsten Kurth, Jian Zhang, Nadathur Satish, Ioannis Mitliagkas, Evan Racah, Mostofa Ali Patwary, Tareq Malas, Narayanan Sundaram, Wahid Bhimji, Mikhail Smorkalov, Jack Deslippe, Mikhail Shiryaev, Srinivas Sridharan, Prabhat, Pradeep Dubey
Date: 2017-08-17 00:00:00
Tasks: General Classification

This paper presents the first, 15-PetaFLOP Deep Learning system for solving
scientific pattern classification problems on contemporary HPC architectures.
We develop supervised convolutional architectures for discriminating signals in
high-energy physics data as well as semi-supervised architectures for
localizing and classifying extreme weather in climate data. Our
Intelcaffe-based implementation obtains $\sim$2TFLOP/s on a single Cori
Phase-II Xeon-Phi node. We use a hybrid strategy employing synchronous
node-groups, while using asynchronous communication across groups. We use this
strategy to scale training of a single model to $\sim$9600 Xeon-Phi nodes;
obtaining peak performance of 11.73-15.07 PFLOP/s and sustained performance of
11.41-13.27 PFLOP/s. At scale, our HEP architecture produces state-of-the-art
classification accuracy on a dataset with 10M images, exceeding that achieved
by selections on high-level physics-motivated features. Our semi-supervised
architecture successfully extracts weather patterns in a 15TB climate dataset.
Our results demonstrate that Deep Learning can be optimized and scaled
effectively on many-core, HPC systems.
Adaptive Clustering Using Kernel Density Estimators
Authors: Ingo Steinwart, Bharath K. Sriperumbudur, Philipp Thomann
Date: 2017-08-17 00:00:00
Tasks: Clustering

We derive and analyze a generic, recursive algorithm for estimating all splits in a finite cluster tree as well as the corresponding clusters. We further investigate statistical properties of this generic clustering algorithm when it receives level set estimates from a kernel density estimator. In particular, we derive finite sample guarantees, consistency, rates of convergence, and an adaptive data-driven strategy for choosing the kernel bandwidth. For these results we do not need continuity assumptions on the density such as H\"{o}lder continuity, but only require intuitive geometric assumptions of non-parametric nature.
Neural Ctrl-F: Segmentation-free Query-by-String Word Spotting in Handwritten Manuscript Collections
Authors: Tomas Wilkinson, Jonas Lindström, Anders Brun
Date: 2017-03-22 00:00:00
Proceeding: ICCV 2017 10
Tasks: Segmentation

In this paper, we approach the problem of segmentation-free query-by-string
word spotting for handwritten documents. In other words, we use methods
inspired from computer vision and machine learning to search for words in large
collections of digitized manuscripts. In particular, we are interested in
historical handwritten texts, which are often far more challenging than modern
printed documents. This task is important, as it provides people with a way to
quickly find what they are looking for in large collections that are tedious
and difficult to read manually. To this end, we introduce an end-to-end
trainable model based on deep neural networks that we call Ctrl-F-Net. Given a
full manuscript page, the model simultaneously generates region proposals, and
embeds these into a distributed word embedding space, where searches are
performed. We evaluate the model on common benchmarks for handwritten word
spotting, outperforming the previous state-of-the-art segmentation-free
approaches by a large margin, and in some cases even segmentation-based
approaches. One interesting real-life application of our approach is to help
historians to find and count specific words in court records that are related
to women's sustenance activities and division of labor. We provide promising
preliminary experiments that validate our method on this task.
Pseudo-extended Markov chain Monte Carlo
Authors: Christopher Nemeth, Fredrik Lindsten, Maurizio Filippone, James Hensman
Date: 2017-08-17 00:00:00
Proceeding: NeurIPS 2019 12

Sampling from posterior distributions using Markov chain Monte Carlo (MCMC) methods can require an exhaustive number of iterations, particularly when the posterior is multi-modal as the MCMC sampler can become trapped in a local mode for a large number of iterations. In this paper, we introduce the pseudo-extended MCMC method as a simple approach for improving the mixing of the MCMC sampler for multi-modal posterior distributions. The pseudo-extended method augments the state-space of the posterior using pseudo-samples as auxiliary variables. On the extended space, the modes of the posterior are connected, which allows the MCMC sampler to easily move between well-separated posterior modes. We demonstrate that the pseudo-extended approach delivers improved MCMC sampling over the Hamiltonian Monte Carlo algorithm on multi-modal posteriors, including Boltzmann machines and models with sparsity-inducing priors.
Human Uncertainty and Ranking Error -- The Secret of Successful Evaluation in Predictive Data Mining
Authors: Kevin Jasberg, Sergej Sizov
Date: 2017-08-17 00:00:00
Tasks: Recommendation Systems

One of the most crucial issues in data mining is to model human behaviour in
order to provide personalisation, adaptation and recommendation. This usually
involves implicit or explicit knowledge, either by observing user interactions,
or by asking users directly. But these sources of information are always
subject to the volatility of human decisions, making utilised data uncertain to
a particular extent. In this contribution, we elaborate on the impact of this
human uncertainty when it comes to comparative assessments of different data
mining approaches. In particular, we reveal two problems: (1) biasing effects
on various metrics of model-based prediction and (2) the propagation of
uncertainty and its thus induced error probabilities for algorithm rankings.
For this purpose, we introduce a probabilistic view and prove the existence of
those problems mathematically, as well as provide possible solution strategies.
We exemplify our theory mainly in the context of recommender systems along with
the metric RMSE as a prominent example of precision quality measures.
A Neural Parametric Singing Synthesizer
Authors: Merlijn Blaauw, Jordi Bonada
Date: 2017-04-12 00:00:00

We present a new model for singing synthesis based on a modified version of
the WaveNet architecture. Instead of modeling raw waveform, we model features
produced by a parametric vocoder that separates the influence of pitch and
timbre. This allows conveniently modifying pitch to match any target melody,
facilitates training on more modest dataset sizes, and significantly reduces
training and generation times. Our model makes frame-wise predictions using
mixture density outputs rather than categorical outputs in order to reduce the
required parameter count. As we found overfitting to be an issue with the
relatively small datasets used in our experiments, we propose a method to
regularize the model and make the autoregressive generation process more robust
to prediction errors. Using a simple multi-stream architecture, harmonic,
aperiodic and voiced/unvoiced components can all be predicted in a coherent
manner. We compare our method to existing parametric statistical and
state-of-the-art concatenative methods using quantitative metrics and a
listening test. While naive implementations of the autoregressive generation
algorithm tend to be inefficient, using a smart algorithm we can greatly speed
up the process and obtain a system that's competitive in both speed and
quality.
Conditional Adversarial Network for Semantic Segmentation of Brain Tumor
Authors: Mina Rezaei, Konstantin Harmuth, Willi Gierke, Thomas Kellermeier, Martin Fischer, Haojin Yang, Christoph Meinel
Date: 2017-08-17 00:00:00
Tasks: Generative Adversarial Network, Medical Image Analysis, Segmentation, Semantic Segmentation, Specificity

Automated medical image analysis has a significant value in diagnosis and
treatment of lesions. Brain tumors segmentation has a special importance and
difficulty due to the difference in appearances and shapes of the different
tumor regions in magnetic resonance images. Additionally, the data sets are
heterogeneous and usually limited in size in comparison with the computer
vision problems. The recently proposed adversarial training has shown promising
results in generative image modeling. In this paper, we propose a novel
end-to-end trainable architecture for brain tumor semantic segmentation through
conditional adversarial training. We exploit conditional Generative Adversarial
Network (cGAN) and train a semantic segmentation Convolution Neural Network
(CNN) along with an adversarial network that discriminates segmentation maps
coming from the ground truth or from the segmentation network for BraTS 2017
segmentation task[15, 4, 2, 3]. We also propose an end-to-end trainable CNN for
survival day prediction based on deep learning techniques for BraTS 2017
prediction task [15, 4, 2, 3]. The experimental results demonstrate the
superior ability of the proposed approach for both tasks. The proposed model
achieves on validation data a DICE score, Sensitivity and Specificity
respectively 0.68, 0.99 and 0.98 for the whole tumor, regarding online judgment
system.
Deep Neural Network with l2-norm Unit for Brain Lesions Detection
Authors: Mina Rezaei, Haojin Yang, Christoph Meinel
Date: 2017-08-17 00:00:00
Tasks: Diagnostic

Automated brain lesions detection is an important and very challenging
clinical diagnostic task because the lesions have different sizes, shapes,
contrasts, and locations. Deep Learning recently has shown promising progress
in many application fields, which motivates us to apply this technology for
such important problem. In this paper, we propose a novel and end-to-end
trainable approach for brain lesions classification and detection by using deep
Convolutional Neural Network (CNN). In order to investigate the applicability,
we applied our approach on several brain diseases including high and low-grade
glioma tumor, ischemic stroke, Alzheimer diseases, by which the brain Magnetic
Resonance Images (MRI) have been applied as an input for the analysis. We
proposed a new operating unit which receives features from several projections
of a subset units of the bottom layer and computes a normalized l2-norm for
next layer. We evaluated the proposed approach on two different CNN
architectures and number of popular benchmark datasets. The experimental
results demonstrate the superior ability of the proposed approach.
Deep Learning for Medical Image Analysis
Authors: Mina Rezaei, Haojin Yang, Christoph Meinel
Date: 2017-08-17 00:00:00
Tasks: Anomaly Detection, Deep Learning, Medical Image Analysis

This report describes my research activities in the Hasso Plattner Institute
and summarizes my Ph.D. plan and several novels, end-to-end trainable
approaches for analyzing medical images using deep learning algorithm. In this
report, as an example, we explore different novel methods based on deep
learning for brain abnormality detection, recognition, and segmentation. This
report prepared for the doctoral consortium in the AIME-2017 conference.