pwc-archive/papers-with-abstracts huggingface dataset:
SUPRA: Open Source Software Defined Ultrasound Processing for Real-Time Applications
Authors: Rüdiger Göbl, Nassir Navab, Christoph Hennersperger
Date: 2017-11-16 00:00:00

Research in ultrasound imaging is limited in reproducibility by two factors:
First, many existing ultrasound pipelines are protected by intellectual
property, rendering exchange of code difficult. Second, most pipelines are
implemented in special hardware, resulting in limited flexibility of
implemented processing steps on such platforms.
  Methods: With SUPRA we propose an open-source pipeline for fully Software
Defined Ultrasound Processing for Real-time Applications to alleviate these
problems. Covering all steps from beamforming to output of B-mode images, SUPRA
can help improve the reproducibility of results and make modifications to the
image acquisition mode accessible to the research community. We evaluate the
pipeline qualitatively, quantitatively, and regarding its run-time.
  Results: The pipeline shows image quality comparable to a clinical system and
backed by point-spread function measurements a comparable resolution. Including
all processing stages of a usual ultrasound pipeline, the run-time analysis
shows that it can be executed in 2D and 3D on consumer GPUs in real-time.
  Conclusions: Our software ultrasound pipeline opens up the research in image
acquisition. Given access to ultrasound data from early stages (raw channel
data, radiofrequency data) it simplifies the development in imaging.
Furthermore, it tackles the reproducibility of research results, as code can be
shared easily and even be executed without dedicated ultrasound hardware.
Deep Nets: What have they ever done for Vision?
Authors: Alan L. Yuille, Chenxi Liu
Date: 2018-05-10 00:00:00
Tasks: Benchmarking

This is an opinion paper about the strengths and weaknesses of Deep Nets for vision. They are at the heart of the enormous recent progress in artificial intelligence and are of growing importance in cognitive science and neuroscience. They have had many successes but also have several limitations and there is limited understanding of their inner workings. At present Deep Nets perform very well on specific visual tasks with benchmark datasets but they are much less general purpose, flexible, and adaptive than the human visual system. We argue that Deep Nets in their current form are unlikely to be able to overcome the fundamental problem of computer vision, namely how to deal with the combinatorial explosion, caused by the enormous complexity of natural images, and obtain the rich understanding of visual scenes that the human visual achieves. We argue that this combinatorial explosion takes us into a regime where "big data is not enough" and where we need to rethink our methods for benchmarking performance and evaluating vision algorithms. We stress that, as vision algorithms are increasingly used in real world applications, that performance evaluation is not merely an academic exercise but has important consequences in the real world. It is impractical to review the entire Deep Net literature so we restrict ourselves to a limited range of topics and references which are intended as entry points into the literature. The views expressed in this paper are our own and do not necessarily represent those of anybody else in the computer vision community.
Automatic Estimation of Simultaneous Interpreter Performance
Authors: Craig Stewart, Nikolai Vogler, Junjie Hu, Jordan Boyd-Graber, Graham Neubig
Date: 2018-05-10 00:00:00
Proceeding: ACL 2018 7
Tasks: Machine Translation, Translation

Simultaneous interpretation, translation of the spoken word in real-time, is
both highly challenging and physically demanding. Methods to predict
interpreter confidence and the adequacy of the interpreted message have a
number of potential applications, such as in computer-assisted interpretation
interfaces or pedagogical tools. We propose the task of predicting simultaneous
interpreter performance by building on existing methodology for quality
estimation (QE) of machine translation output. In experiments over five
settings in three language pairs, we extend a QE pipeline to estimate
interpreter performance (as approximated by the METEOR evaluation metric) and
propose novel features reflecting interpretation strategy and evaluation
measures that further improve prediction accuracy.
Dense and Diverse Capsule Networks: Making the Capsules Learn Better
Authors: Sai Samarth R. Phaye, Apoorva Sikka, Abhinav Dhall, Deepti Bathula
Date: 2018-05-10 00:00:00
Tasks: General Classification, image-classification, Image Classification, Object Recognition

Past few years have witnessed exponential growth of interest in deep learning
methodologies with rapidly improving accuracies and reduced computational
complexity. In particular, architectures using Convolutional Neural Networks
(CNNs) have produced state-of-the-art performances for image classification and
object recognition tasks. Recently, Capsule Networks (CapsNet) achieved
significant increase in performance by addressing an inherent limitation of
CNNs in encoding pose and deformation. Inspired by such advancement, we asked
ourselves, can we do better? We propose Dense Capsule Networks (DCNet) and
Diverse Capsule Networks (DCNet++). The two proposed frameworks customize the
CapsNet by replacing the standard convolutional layers with densely connected
convolutions. This helps in incorporating feature maps learned by different
layers in forming the primary capsules. DCNet, essentially adds a deeper
convolution network, which leads to learning of discriminative feature maps.
Additionally, DCNet++ uses a hierarchical architecture to learn capsules that
represent spatial information in a fine-to-coarser manner, which makes it more
efficient for learning complex data. Experiments on image classification task
using benchmark datasets demonstrate the efficacy of the proposed
architectures. DCNet achieves state-of-the-art performance (99.75%) on MNIST
dataset with twenty fold decrease in total training iterations, over the
conventional CapsNet. Furthermore, DCNet++ performs better than CapsNet on SVHN
dataset (96.90%), and outperforms the ensemble of seven CapsNet models on
CIFAR-10 by 0.31% with seven fold decrease in number of parameters.
ABMOF: A Novel Optical Flow Algorithm for Dynamic Vision Sensors
Authors: Min Liu, Tobi Delbruck
Date: 2018-05-10 00:00:00
Tasks: Optical Flow Estimation

Dynamic Vision Sensors (DVS), which output asynchronous log intensity change
events, have potential applications in high-speed robotics, autonomous cars and
drones. The precise event timing, sparse output, and wide dynamic range of the
events are well suited for optical flow, but conventional optical flow (OF)
algorithms are not well matched to the event stream data. This paper proposes
an event-driven OF algorithm called adaptive block-matching optical flow
(ABMOF). ABMOF uses time slices of accumulated DVS events. The time slices are
adaptively rotated based on the input events and OF results. Compared with
other methods such as gradient-based OF, ABMOF can efficiently be implemented
in compact logic circuits. Results show that ABMOF achieves comparable accuracy
to conventional standards such as Lucas-Kanade (LK). The main contributions of
our paper are new adaptive time-slice rotation methods that ensure the
generated slices have sufficient features for matching,including a feedback
mechanism that controls the generated slices to have average slice displacement
within the block search range. An LK method using our adapted slices is also
implemented. The ABMOF accuracy is compared with this LK method on natural
scene data including sparse and dense texture, high dynamic range, and fast
motion exceeding 30,000 pixels per second.The paper dataset and source code are
available from http://sensors.ini.uzh.ch/databases.html.
A Deep Learning Approach for Forecasting Air Pollution in South Korea Using LSTM
Authors: Tien-Cuong Bui, Van-Duc Le, Sang-Kyun Cha
Date: 2018-04-21 00:00:00
Tasks: Decoder, Reading Comprehension, Time Series, Time Series Analysis

Tackling air pollution is an imperative problem in South Korea, especially in
urban areas, over the last few years. More specially, South Korea has joined
the ranks of the world's most polluted countries alongside with other Asian
capitals, such as Beijing or Delhi. Much research is being conducted in
environmental science to evaluate the dangerous impact of particulate matters
on public health. Besides that, deterministic models of air pollutant behavior
are also generated; however, this is both complex and often inaccurate. On the
contrary, deep recurrent neural network reveals potent potential on forecasting
out-comes of time-series data and has become more prevalent. This paper uses
Recurrent Neural Network (RNN) with Long Short-Term Memory units as a framework
for leveraging knowledge from time-series data of air pollution and
meteorological information in Daegu, Seoul, Beijing, and Shenyang.
Additionally, we use encoder-decoder model, which is similar to machine
comprehension problems, as a crucial part of our prediction machine. Finally,
we investigate the prediction accuracy of various configurations. Our
experiments prevent the efficiency of integrating multiple layers of RNN on
prediction model when forecasting far timesteps ahead. This research is a
significant motivation for not only continuing researching on urban air quality
but also help the government leverage that insight to enact beneficial policies
Automatic Academic Paper Rating Based on Modularized Hierarchical Convolutional Neural Network
Authors: Pengcheng Yang, Xu sun, Wei Li, Shuming Ma
Date: 2018-05-10 00:00:00
Proceeding: ACL 2018 7

As more and more academic papers are being submitted to conferences and
journals, evaluating all these papers by professionals is time-consuming and
can cause inequality due to the personal factors of the reviewers. In this
paper, in order to assist professionals in evaluating academic papers, we
propose a novel task: automatic academic paper rating (AAPR), which
automatically determine whether to accept academic papers. We build a new
dataset for this task and propose a novel modularized hierarchical
convolutional neural network to achieve automatic academic paper rating.
Evaluation results show that the proposed model outperforms the baselines by a
large margin. The dataset and code are available at
\url{https://github.com/lancopku/AAPR}
Monotone Learning with Rectified Wire Networks
Authors: Veit Elser, Dan Schmidt, Jonathan Yedidia
Date: 2018-05-10 00:00:00

We introduce a new neural network model, together with a tractable and
monotone online learning algorithm. Our model describes feed-forward networks
for classification, with one output node for each class. The only nonlinear
operation is rectification using a ReLU function with a bias. However, there is
a rectifier on every edge rather than at the nodes of the network. There are
also weights, but these are positive, static, and associated with the nodes.
Our "rectified wire" networks are able to represent arbitrary Boolean
functions. Only the bias parameters, on the edges of the network, are learned.
Another departure in our approach, from standard neural networks, is that the
loss function is replaced by a constraint. This constraint is simply that the
value of the output node associated with the correct class should be zero. Our
model has the property that the exact norm-minimizing parameter update,
required to correctly classify a training item, is the solution to a quadratic
program that can be computed with a few passes through the network. We
demonstrate a training algorithm using this update, called sequential
deactivation (SDA), on MNIST and some synthetic datasets. Upon adopting a
natural choice for the nodal weights, SDA has no hyperparameters other than
those describing the network structure. Our experiments explore behavior with
respect to network size and depth in a family of sparse expander networks.
High-Resolution Deep Convolutional Generative Adversarial Networks
Authors: Joachim D. Curtó, Irene C. Zarza, Fernando de la Torre, Irwin King, Michael R. Lyu
Date: 2017-11-17 00:00:00
Tasks: GPU, Image Generation, MS-SSIM, SSIM, Vocal Bursts Intensity Prediction

Generative Adversarial Networks (GANs) convergence in a high-resolution
setting with a computational constrain of GPU memory capacity (from 12GB to 24
GB) has been beset with difficulty due to the known lack of convergence rate
stability. In order to boost network convergence of DCGAN (Deep Convolutional
Generative Adversarial Networks) and achieve good-looking high-resolution
results we propose a new layered network structure, HDCGAN, that incorporates
current state-of-the-art techniques for this effect. A novel dataset, Curt\'o &
Zarza, containing human faces from different ethnical groups in a wide variety
of illumination conditions and image resolutions is introduced. Curt\'o is
enhanced with HDCGAN synthetic images, thus being the first GAN augmented face
dataset. We conduct extensive experiments on CelebA (MS-SSIM 0.1978 and
Distance of Fr\'echet 8.77) and Curt\'o.
Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction
Authors: Lu-chen Liu, Jianhao Shen, Ming Zhang, Zichang Wang, Jian Tang
Date: 2018-03-13 00:00:00

The availability of a large amount of electronic health records (EHR)
provides huge opportunities to improve health care service by mining these
data. One important application is clinical endpoint prediction, which aims to
predict whether a disease, a symptom or an abnormal lab test will happen in the
future according to patients' history records. This paper develops deep
learning techniques for clinical endpoint prediction, which are effective in
many practical applications. However, the problem is very challenging since
patients' history records contain multiple heterogeneous temporal events such
as lab tests, diagnosis, and drug administrations. The visiting patterns of
different types of events vary significantly, and there exist complex nonlinear
relationships between different events. In this paper, we propose a novel model
for learning the joint representation of heterogeneous temporal events. The
model adds a new gate to control the visiting rates of different events which
effectively models the irregular patterns of different events and their
nonlinear correlations. Experiment results with real-world clinical data on the
tasks of predicting death and abnormal lab tests prove the effectiveness of our
proposed approach over competitive baselines.
Text-mining and ontologies: new approaches to knowledge discovery of microbial diversity
Authors: Claire Nédellec, Robert Bossy, Estelle Chaix, Louise Deléger
Date: 2018-05-10 00:00:00
Tasks: Diversity

Microbiology research has access to a very large amount of public information
on the habitats of microorganisms. Many areas of microbiology research uses
this information, primarily in biodiversity studies. However the habitat
information is expressed in unstructured natural language form, which hinders
its exploitation at large-scale. It is very common for similar habitats to be
described by different terms, which makes them hard to compare automatically,
e.g. intestine and gut. The use of a common reference to standardize these
habitat descriptions as claimed by (Ivana et al., 2010) is a necessity. We
propose the ontology called OntoBiotope that we have been developing since
2010. The OntoBiotope ontology is in a formal machine-readable representation
that enables indexing of information as well as conceptualization and
reasoning.
Loss-aware Weight Quantization of Deep Networks
Authors: Lu Hou, James T. Kwok
Date: 2018-02-23 00:00:00
Proceeding: ICLR 2018 1
Tasks: Binarization, Quantization

The huge size of deep networks hinders their use in small computing devices.
In this paper, we consider compressing the network by weight quantization. We
extend a recently proposed loss-aware weight binarization scheme to
ternarization, with possibly different scaling parameters for the positive and
negative weights, and m-bit (where m > 2) quantization. Experiments on
feedforward and recurrent neural networks show that the proposed scheme
outperforms state-of-the-art weight quantization algorithms, and is as accurate
(or even more accurate) than the full-precision network.
Loss-aware Binarization of Deep Networks
Authors: Lu Hou, Quanming Yao, James T. Kwok
Date: 2016-11-05 00:00:00
Tasks: Binarization

Deep neural network models, though very powerful and highly successful, are
computationally expensive in terms of space and time. Recently, there have been
a number of attempts on binarizing the network weights and activations. This
greatly reduces the network size, and replaces the underlying multiplications
to additions or even XNOR bit operations. However, existing binarization
schemes are based on simple matrix approximation and ignore the effect of
binarization on the loss. In this paper, we propose a proximal Newton algorithm
with diagonal Hessian approximation that directly minimizes the loss w.r.t. the
binarized weights. The underlying proximal step has an efficient closed-form
solution, and the second-order information can be efficiently obtained from the
second moments already computed by the Adam optimizer. Experiments on both
feedforward and recurrent networks show that the proposed loss-aware
binarization algorithm outperforms existing binarization schemes, and is also
more robust for wide and deep networks.
Ensemble Soft-Margin Softmax Loss for Image Classification
Authors: Xiaobo Wang, Shifeng Zhang, Zhen Lei, Si Liu, Xiaojie Guo, Stan Z. Li
Date: 2018-05-10 00:00:00
Tasks: Classification, Diversity, General Classification, image-classification, Image Classification

Softmax loss is arguably one of the most popular losses to train CNN models
for image classification. However, recent works have exposed its limitation on
feature discriminability. This paper casts a new viewpoint on the weakness of
softmax loss. On the one hand, the CNN features learned using the softmax loss
are often inadequately discriminative. We hence introduce a soft-margin softmax
function to explicitly encourage the discrimination between different classes.
On the other hand, the learned classifier of softmax loss is weak. We propose
to assemble multiple these weak classifiers to a strong one, inspired by the
recognition that the diversity among weak classifiers is critical to a good
ensemble. To achieve the diversity, we adopt the Hilbert-Schmidt Independence
Criterion (HSIC). Considering these two aspects in one framework, we design a
novel loss, named as Ensemble soft-Margin Softmax (EM-Softmax). Extensive
experiments on benchmark datasets are conducted to show the superiority of our
design over the baseline softmax loss and several state-of-the-art
alternatives.
Synthesizing Efficient Solutions for Patrolling Problems in the Internet Environment
Authors: Tomáš Brázdil, Antonín Kučera, Vojtěch Řehák
Date: 2018-05-08 00:00:00

We propose an algorithm for constructing efficient patrolling strategies in
the Internet environment, where the protected targets are nodes connected to
the network and the patrollers are software agents capable of
detecting/preventing undesirable activities on the nodes. The algorithm is
based on a novel compositional principle designed for a special class of
strategies, and it can quickly construct (sub)optimal solutions even if the
number of targets reaches hundreds of millions.
SalNet360: Saliency Maps for omni-directional images with CNN
Authors: Rafael Monroy, Sebastian Lutz, Tejo Chalasani, Aljosa Smolic
Date: 2017-09-19 00:00:00
Tasks: Prediction, Saliency Prediction

The prediction of Visual Attention data from any kind of media is of valuable
use to content creators and used to efficiently drive encoding algorithms. With
the current trend in the Virtual Reality (VR) field, adapting known techniques
to this new kind of media is starting to gain momentum. In this paper, we
present an architectural extension to any Convolutional Neural Network (CNN) to
fine-tune traditional 2D saliency prediction to Omnidirectional Images (ODIs)
in an end-to-end manner. We show that each step in the proposed pipeline works
towards making the generated saliency map more accurate with respect to ground
truth data.
Towards a universal neural network encoder for time series
Authors: Joan Serrà, Santiago Pascual, Alexandros Karatzoglou
Date: 2018-05-10 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Classification

We study the use of a time series encoder to learn representations that are
useful on data set types with which it has not been trained on. The encoder is
formed of a convolutional neural network whose temporal output is summarized by
a convolutional attention mechanism. This way, we obtain a compact,
fixed-length representation from longer, variable-length time series. We
evaluate the performance of the proposed approach on a well-known time series
classification benchmark, considering full adaptation, partial adaptation, and
no adaptation of the encoder to the new data type. Results show that such
strategies are competitive with the state-of-the-art, often outperforming
conceptually-matching approaches. Besides accuracy scores, the facility of
adaptation and the efficiency of pre-trained encoders make them an appealing
option for the processing of scarcely- or non-labeled time series.
Loss-Calibrated Approximate Inference in Bayesian Neural Networks
Authors: Adam D. Cobb, Stephen J. Roberts, Yarin Gal
Date: 2018-05-10 00:00:00
Tasks: Autonomous Driving, Semantic Segmentation

Current approaches in approximate inference for Bayesian neural networks
minimise the Kullback-Leibler divergence to approximate the true posterior over
the weights. However, this approximation is without knowledge of the final
application, and therefore cannot guarantee optimal predictions for a given
task. To make more suitable task-specific approximations, we introduce a new
loss-calibrated evidence lower bound for Bayesian neural networks in the
context of supervised learning, informed by Bayesian decision theory. By
introducing a lower bound that depends on a utility function, we ensure that
our approximation achieves higher utility than traditional methods for
applications that have asymmetric utility functions. Furthermore, in using
dropout inference, we highlight that our new objective is identical to that of
standard dropout neural networks, with an additional utility-dependent penalty
term. We demonstrate our new loss-calibrated model with an illustrative medical
example and a restricted model capacity experiment, and highlight failure modes
of the comparable weighted cross entropy approach. Lastly, we demonstrate the
scalability of our method to real world applications with per-pixel semantic
segmentation on an autonomous driving data set.