pwc-archive/papers-with-abstracts huggingface dataset:
CNN-based Landmark Detection in Cardiac CTA Scans
Authors: Julia M. H. Noothout, Bob D. de Vos, Jelmer M. Wolterink, Tim Leiner, Ivana Išgum
Date: 2018-04-13 00:00:00
Tasks: Anatomical Landmark Detection, Classification, General Classification, Medical Image Analysis, regression

Fast and accurate anatomical landmark detection can benefit many medical
image analysis methods. Here, we propose a method to automatically detect
anatomical landmarks in medical images. Automatic landmark detection is
performed with a patch-based fully convolutional neural network (FCNN) that
combines regression and classification. For any given image patch, regression
is used to predict the 3D displacement vector from the image patch to the
landmark. Simultaneously, classification is used to identify patches that
contain the landmark. Under the assumption that patches close to a landmark can
determine the landmark location more precisely than patches farther from it,
only those patches that contain the landmark according to classification are
used to determine the landmark location. The landmark location is obtained by
calculating the average landmark location using the computed 3D displacement
vectors. The method is evaluated using detection of six clinically relevant
landmarks in coronary CT angiography (CCTA) scans: the right and left ostium,
the bifurcation of the left main coronary artery (LM) into the left anterior
descending and the left circumflex artery, and the origin of the right,
non-coronary, and left aortic valve commissure. The proposed method achieved an
average Euclidean distance error of 2.19 mm and 2.88 mm for the right and left
ostium respectively, 3.78 mm for the bifurcation of the LM, and 1.82 mm, 2.10
mm and 1.89 mm for the origin of the right, non-coronary, and left aortic valve
commissure respectively, demonstrating accurate performance. The proposed
combination of regression and classification can be used to accurately detect
landmarks in CCTA scans.
Unsupervised Machine Translation Using Monolingual Corpora Only
Authors: Guillaume Lample, Alexis Conneau, Ludovic Denoyer, Marc'Aurelio Ranzato
Date: 2017-10-31 00:00:00
Proceeding: ICLR 2018 1
Tasks: Machine Translation, Sentence, Translation, Unsupervised Machine Translation

Machine translation has recently achieved impressive performance thanks to
recent advances in deep learning and the availability of large-scale parallel
corpora. There have been numerous attempts to extend these successes to
low-resource language pairs, yet requiring tens of thousands of parallel
sentences. In this work, we take this research direction to the extreme and
investigate whether it is possible to learn to translate even without any
parallel data. We propose a model that takes sentences from monolingual corpora
in two different languages and maps them into the same latent space. By
learning to reconstruct in both languages from this shared feature space, the
model effectively learns to translate without using any labeled data. We
demonstrate our model on two widely used datasets and two language pairs,
reporting BLEU scores of 32.8 and 15.1 on the Multi30k and WMT English-French
datasets, without using even a single parallel sentence at training time.
Modified Frank-Wolfe Algorithm for Enhanced Sparsity in Support Vector Machine Classifiers
Authors: Carlos M. Alaíz, Johan A. K. Suykens
Date: 2017-06-19 00:00:00

This work proposes a new algorithm for training a re-weighted L2 Support
Vector Machine (SVM), inspired on the re-weighted Lasso algorithm of Cand\`es
et al. and on the equivalence between Lasso and SVM shown recently by Jaggi. In
particular, the margin required for each training vector is set independently,
defining a new weighted SVM model. These weights are selected to be binary, and
they are automatically adapted during the training of the model, resulting in a
variation of the Frank-Wolfe optimization algorithm with essentially the same
computational complexity as the original algorithm. As shown experimentally,
this algorithm is computationally cheaper to apply since it requires less
iterations to converge, and it produces models with a sparser representation in
terms of support vectors and which are more stable with respect to the
selection of the regularization hyper-parameter.
PIMKL: Pathway Induced Multiple Kernel Learning
Authors: Matteo Manica, Joris Cadow, Roland Mathis, María Rodríguez Martínez
Date: 2018-03-29 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Transfer Learning

Reliable identification of molecular biomarkers is essential for accurate
patient stratification. While state-of-the-art machine learning approaches for
sample classification continue to push boundaries in terms of performance, most
of these methods are not able to integrate different data types and lack
generalization power, limiting their application in a clinical setting.
Furthermore, many methods behave as black boxes, and we have very little
understanding about the mechanisms that lead to the prediction. While
opaqueness concerning machine behaviour might not be a problem in deterministic
domains, in health care, providing explanations about the molecular factors and
phenotypes that are driving the classification is crucial to build trust in the
performance of the predictive system. We propose Pathway Induced Multiple
Kernel Learning (PIMKL), a novel methodology to reliably classify samples that
can also help gain insights into the molecular mechanisms that underlie the
classification. PIMKL exploits prior knowledge in the form of a molecular
interaction network and annotated gene sets, by optimizing a mixture of
pathway-induced kernels using a Multiple Kernel Learning (MKL) algorithm, an
approach that has demonstrated excellent performance in different machine
learning applications. After optimizing the combination of kernels for
prediction of a specific phenotype, the model provides a stable molecular
signature that can be interpreted in the light of the ingested prior knowledge
and that can be used in transfer learning tasks.
Pose estimation of a single circle using default intrinsic calibration
Authors: Mariyanayagam Damien, Gurdjos Pierre, Chambon Sylvie, Brunet Florent, Charvillat Vincent
Date: 2018-04-13 00:00:00
Tasks: Pose Estimation

Circular markers are planar markers which offer great performances for
detection and pose estimation. For an uncalibrated camera with an unknown focal
length, at least the images of at least two coplanar circles are generally
required to recover their poses. Unfortunately, detecting more than one ellipse
in the image must be tricky and time-consuming, especially regarding concentric
circles. On the other hand, when the camera is calibrated, one circle suffices
but the solution is twofold and can hardly be disambiguated. Our contribution
is to put beyond this limit by dealing with the uncalibrated case of a camera
seeing one circle and discussing how to remove the ambiguity. We propose a new
problem formulation that enables to show how to detect geometric configurations
in which the ambiguity can be removed. Furthermore, we introduce the notion of
default camera intrinsics and show, using intensive empirical works, the
surprising observation that very approximate calibration can lead to accurate
circle pose estimation.
Shift-Net: Image Inpainting via Deep Feature Rearrangement
Authors: Zhaoyi Yan, Xiaoming Li, Mu Li, WangMeng Zuo, Shiguang Shan
Date: 2018-01-29 00:00:00
Proceeding: ECCV 2018 9
Tasks: Decoder, Image Inpainting

Deep convolutional networks (CNNs) have exhibited their potential in image
inpainting for producing plausible results. However, in most existing methods,
e.g., context encoder, the missing parts are predicted by propagating the
surrounding convolutional features through a fully connected layer, which
intends to produce semantically plausible but blurry result. In this paper, we
introduce a special shift-connection layer to the U-Net architecture, namely
Shift-Net, for filling in missing regions of any shape with sharp structures
and fine-detailed textures. To this end, the encoder feature of the known
region is shifted to serve as an estimation of the missing parts. A guidance
loss is introduced on decoder feature to minimize the distance between the
decoder feature after fully connected layer and the ground-truth encoder
feature of the missing parts. With such constraint, the decoder feature in
missing region can be used to guide the shift of encoder feature in known
region. An end-to-end learning algorithm is further developed to train the
Shift-Net. Experiments on the Paris StreetView and Places datasets demonstrate
the efficiency and effectiveness of our Shift-Net in producing sharper,
fine-detailed, and visually plausible results. The codes and pre-trained models
are available at https://github.com/Zhaoyi-Yan/Shift-Net.
Taking gradients through experiments: LSTMs and memory proximal policy optimization for black-box quantum control
Authors: Moritz August, José Miguel Hernández-Lobato
Date: 2018-02-12 00:00:00
Tasks: Reinforcement Learning

In this work we introduce the application of black-box quantum control as an
interesting rein- forcement learning problem to the machine learning community.
We analyze the structure of the reinforcement learning problems arising in
quantum physics and argue that agents parameterized by long short-term memory
(LSTM) networks trained via stochastic policy gradients yield a general method
to solving them. In this context we introduce a variant of the proximal policy
optimization (PPO) algorithm called the memory proximal policy optimization
(MPPO) which is based on this analysis. We then show how it can be applied to
specific learning tasks and present results of nu- merical experiments showing
that our method achieves state-of-the-art results for several learning tasks in
quantum control with discrete and continouous control parameters.
sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo
Authors: Jack Baker, Paul Fearnhead, Emily B. Fox, Christopher Nemeth
Date: 2017-10-02 00:00:00
Tasks: Bayesian Inference

This paper introduces the R package sgmcmc; which can be used for Bayesian
inference on problems with large datasets using stochastic gradient Markov
chain Monte Carlo (SGMCMC). Traditional Markov chain Monte Carlo (MCMC)
methods, such as Metropolis-Hastings, are known to run prohibitively slowly as
the dataset size increases. SGMCMC solves this issue by only using a subset of
data at each iteration. SGMCMC requires calculating gradients of the log
likelihood and log priors, which can be time consuming and error prone to
perform by hand. The sgmcmc package calculates these gradients itself using
automatic differentiation, making the implementation of these methods much
easier. To do this, the package uses the software library TensorFlow, which has
a variety of statistical distributions and mathematical operations as standard,
meaning a wide class of models can be built using this framework. SGMCMC has
become widely adopted in the machine learning literature, but less so in the
statistics community. We believe this may be partly due to lack of software;
this package aims to bridge this gap.
Neural Trajectory Analysis of Recurrent Neural Network In Handwriting Synthesis
Authors: Kristof B. Charbonneau, Osamu Shouno
Date: 2018-04-13 00:00:00

Recurrent neural networks (RNNs) are capable of learning to generate highly
realistic, online handwritings in a wide variety of styles from a given text
sequence. Furthermore, the networks can generate handwritings in the style of a
particular writer when the network states are primed with a real sequence of
pen movements from the writer. However, how populations of neurons in the RNN
collectively achieve such performance still remains poorly understood. To
tackle this problem, we investigated learned representations in RNNs by
extracting low-dimensional, neural trajectories that summarize the activity of
a population of neurons in the network during individual syntheses of
handwritings. The neural trajectories show that different writing styles are
encoded in different subspaces inside an internal space of the network. Within
each subspace, different characters of the same style are represented as
different state dynamics. These results demonstrate the effectiveness of
analyzing the neural trajectory for intuitive understanding of how the RNNs
work.
Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features
Authors: Minh-Nghia Nguyen, Ngo Anh Vien
Date: 2018-04-13 00:00:00
Tasks: Anomaly Detection, Decision Making, Dimensionality Reduction, Representation Learning, Unsupervised Anomaly Detection

One-class support vector machine (OC-SVM) for a long time has been one of the
most effective anomaly detection methods and extensively adopted in both
research as well as industrial applications. The biggest issue for OC-SVM is
yet the capability to operate with large and high-dimensional datasets due to
optimization complexity. Those problems might be mitigated via dimensionality
reduction techniques such as manifold learning or autoencoder. However,
previous work often treats representation learning and anomaly prediction
separately. In this paper, we propose autoencoder based one-class support
vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier
features to approximate the radial basis kernel, into deep learning context by
combining it with a representation learning architecture and jointly exploit
stochastic gradient descent to obtain end-to-end training. Interestingly, this
also opens up the possible use of gradient-based attribution methods to explain
the decision making for anomaly detection, which has ever been challenging as a
result of the implicit mappings between the input space and the kernel space.
To the best of our knowledge, this is the first work to study the
interpretability of deep learning in anomaly detection. We evaluate our method
on a wide range of unsupervised anomaly detection tasks in which our end-to-end
training architecture achieves a performance significantly better than the
previous work using separate training.
On the Geometric Ergodicity of Hamiltonian Monte Carlo
Authors: Samuel Livingstone, Michael Betancourt, Simon Byrne, Mark Girolami
Date: 2016-01-29 00:00:00
Tasks: Position

We establish general conditions under which Markov chains produced by the
Hamiltonian Monte Carlo method will and will not be geometrically ergodic. We
consider implementations with both position-independent and position-dependent
integration times. In the former case we find that the conditions for geometric
ergodicity are essentially a gradient of the log-density which asymptotically
points towards the centre of the space and grows no faster than linearly. In an
idealised scenario in which the integration time is allowed to change in
different regions of the space, we show that geometric ergodicity can be
recovered for a much broader class of tail behaviours, leading to some
guidelines for the choice of this free parameter in practice.
Learning to Exploit the Prior Network Knowledge for Weakly-Supervised Semantic Segmentation
Authors: Carolina Redondo-Cabrera, Marcos Baptista-Ríos, Roberto J. López-Sastre
Date: 2018-04-13 00:00:00
Tasks: Segmentation, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Training a Convolutional Neural Network (CNN) for semantic segmentation
typically requires to collect a large amount of accurate pixel-level
annotations, a hard and expensive task. In contrast, simple image tags are
easier to gather. With this paper we introduce a novel weakly-supervised
semantic segmentation model able to learn from image labels, and just image
labels. Our model uses the prior knowledge of a network trained for image
recognition, employing these image annotations as an attention mechanism to
identify semantic regions in the images. We then present a methodology that
builds accurate class-specific segmentation masks from these regions, where
neither external objectness nor saliency algorithms are required. We describe
how to incorporate this mask generation strategy into a fully end-to-end
trainable process where the network jointly learns to classify and segment
images. Our experiments on PASCAL VOC 2012 dataset show that exploiting these
generated class-specific masks in conjunction with our novel end-to-end
learning process outperforms several recent weakly-supervised semantic
segmentation methods that use image tags only, and even some models that
leverage additional supervision or training data.
Learning Contracting Vector Fields For Stable Imitation Learning
Authors: Vikas Sindhwani, Stephen Tu, Mohi Khansari
Date: 2018-04-13 00:00:00
Tasks: Imitation Learning

We propose a new non-parametric framework for learning incrementally stable
dynamical systems x' = f(x) from a set of sampled trajectories. We construct a
rich family of smooth vector fields induced by certain classes of matrix-valued
kernels, whose equilibria are placed exactly at a desired set of locations and
whose local contraction and curvature properties at various points can be
explicitly controlled using convex optimization. With curl-free kernels, our
framework may also be viewed as a mechanism to learn potential fields and
gradient flows. We develop large-scale techniques using randomized kernel
approximations in this context. We demonstrate our approach, called contracting
vector fields (CVF), on imitation learning tasks involving complex
point-to-point human handwriting motions.
Group Anomaly Detection using Deep Generative Models
Authors: Raghavendra Chalapathy, Edward Toth, Sanjay Chawla
Date: 2018-04-13 00:00:00
Tasks: Anomaly Detection, Group Anomaly Detection

Unlike conventional anomaly detection research that focuses on point
anomalies, our goal is to detect anomalous collections of individual data
points. In particular, we perform group anomaly detection (GAD) with an
emphasis on irregular group distributions (e.g. irregular mixtures of image
pixels). GAD is an important task in detecting unusual and anomalous phenomena
in real-world applications such as high energy particle physics, social media,
and medical imaging. In this paper, we take a generative approach by proposing
deep generative models: Adversarial autoencoder (AAE) and variational
autoencoder (VAE) for group anomaly detection. Both AAE and VAE detect group
anomalies using point-wise input data where group memberships are known a
priori. We conduct extensive experiments to evaluate our models on real-world
datasets. The empirical results demonstrate that our approach is effective and
robust in detecting group anomalies.
Local Word Vectors Guiding Keyphrase Extraction
Authors: Eirini Papagiannopoulou, Grigorios Tsoumakas
Date: 2017-10-20 00:00:00
Tasks: Keyphrase Extraction, Word Embeddings

Automated keyphrase extraction is a fundamental textual information
processing task concerned with the selection of representative phrases from a
document that summarize its content. This work presents a novel unsupervised
method for keyphrase extraction, whose main innovation is the use of local word
embeddings (in particular GloVe vectors), i.e., embeddings trained from the
single document under consideration. We argue that such local representation of
words and keyphrases are able to accurately capture their semantics in the
context of the document they are part of, and therefore can help in improving
keyphrase extraction quality. Empirical results offer evidence that indeed
local representations lead to better keyphrase extraction results compared to
both embeddings trained on very large third corpora or larger corpora
consisting of several documents of the same scientific field and to other
state-of-the-art unsupervised keyphrase extraction methods.
BodyNet: Volumetric Inference of 3D Human Body Shapes
Authors: Gül Varol, Duygu Ceylan, Bryan Russell, Jimei Yang, Ersin Yumer, Ivan Laptev, Cordelia Schmid
Date: 2018-04-13 00:00:00
Proceeding: ECCV 2018 9
Tasks: 3D Human Pose Estimation, Segmentation, Video Editing

Human shape estimation is an important task for video editing, animation and
fashion industry. Predicting 3D human body shape from natural images, however,
is highly challenging due to factors such as variation in human bodies,
clothing and viewpoint. Prior methods addressing this problem typically attempt
to fit parametric body models with certain priors on pose and shape. In this
work we argue for an alternative representation and propose BodyNet, a neural
network for direct inference of volumetric body shape from a single image.
BodyNet is an end-to-end trainable network that benefits from (i) a volumetric
3D loss, (ii) a multi-view re-projection loss, and (iii) intermediate
supervision of 2D pose, 2D body part segmentation, and 3D pose. Each of them
results in performance improvement as demonstrated by our experiments. To
evaluate the method, we fit the SMPL model to our network output and show
state-of-the-art results on the SURREAL and Unite the People datasets,
outperforming recent approaches. Besides achieving state-of-the-art
performance, our method also enables volumetric body-part segmentation.
Nonparametric Bayesian label prediction on a large graph using truncated Laplacian regularization
Authors: Jarno Hartog, Harry van Zanten
Date: 2018-04-13 00:00:00
Tasks: Binary Classification, General Classification

This article describes an implementation of a nonparametric Bayesian approach
to solving binary classification problems on graphs. We consider a hierarchical
Bayesian approach with a prior that is constructed by truncating a series
expansion of the soft label function using the graph Laplacian eigenfunctions
as basis functions. We compare our truncated prior to the untruncated Laplacian
based prior in simulated and real data examples to illustrate the improved
scalability in terms of size of the underlying graph.
Summarization of User-Generated Sports Video by Using Deep Action Recognition Features
Authors: Antonio Tejero-de-Pablos, Yuta Nakashima, Tomokazu Sato, Naokazu Yokoya, Marko Linna, Esa Rahtu
Date: 2017-09-25 00:00:00
Tasks: Action Recognition, Temporal Action Localization, Video Summarization

Automatically generating a summary of sports video poses the challenge of
detecting interesting moments, or highlights, of a game. Traditional sports
video summarization methods leverage editing conventions of broadcast sports
video that facilitate the extraction of high-level semantics. However,
user-generated videos are not edited, and thus traditional methods are not
suitable to generate a summary. In order to solve this problem, this work
proposes a novel video summarization method that uses players' actions as a cue
to determine the highlights of the original video. A deep neural network-based
approach is used to extract two types of action-related features and to
classify video segments into interesting or uninteresting parts. The proposed
method can be applied to any sports in which games consist of a succession of
actions. Especially, this work considers the case of Kendo (Japanese fencing)
as an example of a sport to evaluate the proposed method. The method is trained
using Kendo videos with ground truth labels that indicate the video highlights.
The labels are provided by annotators possessing different experience with
respect to Kendo to demonstrate how the proposed method adapts to different
needs. The performance of the proposed method is compared with several
combinations of different features, and the results show that it outperforms
previous summarization methods.