pwc-archive/papers-with-abstracts huggingface dataset:
WaveCycleGAN: Synthetic-to-natural speech waveform conversion using cycle-consistent adversarial networks
Authors: Kou Tanaka, Takuhiro Kaneko, Nobukatsu Hojo, Hirokazu Kameoka
Date: 2018-09-25 00:00:00
Tasks: Speech Synthesis, Voice Conversion

We propose a learning-based filter that allows us to directly modify a
synthetic speech waveform into a natural speech waveform. Speech-processing
systems using a vocoder framework such as statistical parametric speech
synthesis and voice conversion are convenient especially for a limited number
of data because it is possible to represent and process interpretable acoustic
features over a compact space, such as the fundamental frequency (F0) and
mel-cepstrum. However, a well-known problem that leads to the quality
degradation of generated speech is an over-smoothing effect that eliminates
some detailed structure of generated/converted acoustic features. To address
this issue, we propose a synthetic-to-natural speech waveform conversion
technique that uses cycle-consistent adversarial networks and which does not
require any explicit assumption about speech waveform in adversarial learning.
In contrast to current techniques, since our modification is performed at the
waveform level, we expect that the proposed method will also make it possible
to generate `vocoder-less' sounding speech even if the input speech is
synthesized using a vocoder framework. The experimental results demonstrate
that our proposed method can 1) alleviate the over-smoothing effect of the
acoustic features despite the direct modification method used for the waveform
and 2) greatly improve the naturalness of the generated speech sounds.
Cylindrical Transform: 3D Semantic Segmentation of Kidneys With Limited Annotated Images
Authors: Hojjat Salehinejad, Sumeya Naqvi, Errol Colak, Joseph Barfett, Shahrokh Valaee
Date: 2018-09-24 00:00:00
Tasks: 3D Semantic Segmentation, Computed Tomography (CT), General Classification, Segmentation, Semantic Segmentation

In this paper, we propose a novel technique for sampling sequential images
using a cylindrical transform in a cylindrical coordinate system for kidney
semantic segmentation in abdominal computed tomography (CT). The images
generated from a cylindrical transform augment a limited annotated set of
images in three dimensions. This approach enables us to train contemporary
classification deep convolutional neural networks (DCNNs) instead of fully
convolutional networks (FCNs) for semantic segmentation. Typical semantic
segmentation models segment a sequential set of images (e.g. CT or video) by
segmenting each image independently. However, the proposed method not only
considers the spatial dependency in the x-y plane, but also the spatial
sequential dependency along the z-axis. The results show that classification
DCNNs, trained on cylindrical transformed images, can achieve a higher
segmentation performance value than FCNs using a limited number of annotated
images.
Autonomously and Simultaneously Refining Deep Neural Network Parameters by a Bi-Generative Adversarial Network Aided Genetic Algorithm
Authors: Yantao Lu, Burak Kakillioglu, Senem Velipasalar
Date: 2018-09-24 00:00:00
Tasks: Generative Adversarial Network

The choice of parameters, and the design of the network architecture are
important factors affecting the performance of deep neural networks. Genetic
Algorithms (GA) have been used before to determine parameters of a network.
Yet, GAs perform a finite search over a discrete set of pre-defined candidates,
and cannot, in general, generate unseen configurations. In this paper, to move
from exploration to exploitation, we propose a novel and systematic method that
autonomously and simultaneously optimizes multiple parameters of any deep
neural network by using a GA aided by a bi-generative adversarial network
(Bi-GAN). The proposed Bi-GAN allows the autonomous exploitation and choice of
the number of neurons, for fully-connected layers, and number of filters, for
convolutional layers, from a large range of values. Our proposed Bi-GAN
involves two generators, and two different models compete and improve each
other progressively with a GAN-based strategy to optimize the networks during
GA evolution. Our proposed approach can be used to autonomously refine the
number of convolutional layers and dense layers, number and size of kernels,
and the number of neurons for the dense layers; choose the type of the
activation function; and decide whether to use dropout and batch normalization
or not, to improve the accuracy of different deep neural network architectures.
Without loss of generality, the proposed method has been tested with the
ModelNet database, and compared with the 3D Shapenets and two GA-only methods.
The results show that the presented approach can simultaneously and
successfully optimize multiple neural network parameters, and achieve higher
accuracy even with shallower networks.
Segmentation of Skin Lesions and their Attributes Using Multi-Scale Convolutional Neural Networks and Domain Specific Augmentations
Authors: Mostafa Jahanifar, Neda Zamani Tajeddin, Navid Alemi Koohbanani, Ali Gooya, Nasir Rajpoot
Date: 2018-09-23 00:00:00
Tasks: Attribute, Decoder, General Classification, Lesion Classification, Transfer Learning

Computer-aided diagnosis systems for classification of different type of skin
lesions have been an active field of research in recent decades. It has been
shown that introducing lesions and their attributes masks into lesion
classification pipeline can greatly improve the performance. In this paper, we
propose a framework by incorporating transfer learning for segmenting lesions
and their attributes based on the convolutional neural networks. The proposed
framework is based on the encoder-decoder architecture which utilizes a variety
of pre-trained networks in the encoding path and generates the prediction map
by combining multi-scale information in decoding path using a pyramid pooling
manner. To address the lack of training data and increase the proposed model
generalization, an extensive set of novel domain-specific augmentation routines
have been applied to simulate the real variations in dermoscopy images.
Finally, by performing broad experiments on three different data sets obtained
from International Skin Imaging Collaboration archive (ISIC2016, ISIC2017, and
ISIC2018 challenges data sets), we show that the proposed method outperforms
other state-of-the-art approaches for ISIC2016 and ISIC2017 segmentation task
and achieved the first rank on the leader-board of ISIC2018 attribute detection
task.
Addressing Training Bias via Automated Image Annotation
Authors: Zhujun Xiao, Yanzi Zhu, Yuxin Chen, Ben Y. Zhao, Junchen Jiang, Hai-Tao Zheng
Date: 2018-09-22 00:00:00
Tasks: vehicle detection

Build accurate DNN models requires training on large labeled, context
specific datasets, especially those matching the target scenario. We believe
advances in wireless localization, working in unison with cameras, can produce
automated annotation of targets on images and videos captured in the wild.
Using pedestrian and vehicle detection as examples, we demonstrate the
feasibility, benefits, and challenges of an automatic image annotation system.
Our work calls for new technical development on passive localization, mobile
data analytics, and error-resilient ML models, as well as design issues in user
privacy policies.
Classifying Mammographic Breast Density by Residual Learning
Authors: Jingxu Xu, Cheng Li, Yongjin Zhou, Lisha Mou, Hairong Zheng, Shan-Shan Wang
Date: 2018-09-21 00:00:00
Tasks: Classification, General Classification

Mammographic breast density, a parameter used to describe the proportion of
breast tissue fibrosis, is widely adopted as an evaluation characteristic of
the likelihood of breast cancer incidence. In this study, we present a
radiomics approach based on residual learning for the classification of
mammographic breast densities. Our method possesses several encouraging
properties such as being almost fully automatic, possessing big model capacity
and flexibility. It can obtain outstanding classification results without the
necessity of result compensation using mammographs taken from different views.
The proposed method was instantiated with the INbreast dataset and
classification accuracies of 92.6% and 96.8% were obtained for the four BI-RADS
(Breast Imaging and Reporting Data System) category task and the two BI-RADS
category task,respectively. The superior performances achieved compared to the
existing state-of-the-art methods along with its encouraging properties
indicate that our method has a great potential to be applied as a
computer-aided diagnosis tool.
A neural attention model for speech command recognition
Authors: Douglas Coimbra de Andrade, Sabato Leo, Martin Loesener Da Silva Viana, Christoph Bernkopf
Date: 2018-08-27 00:00:00
Tasks: Image Captioning, model

This paper introduces a convolutional recurrent network with attention for
speech command recognition. Attention models are powerful tools to improve
performance on natural language, image captioning and speech tasks. The
proposed model establishes a new state-of-the-art accuracy of 94.1% on Google
Speech Commands dataset V1 and 94.5% on V2 (for the 20-commands recognition
task), while still keeping a small footprint of only 202K trainable parameters.
Results are compared with previous convolutional implementations on 5 different
tasks (20 commands recognition (V1 and V2), 12 commands recognition (V1), 35
word recognition (V1) and left-right (V1)). We show detailed performance
results and demonstrate that the proposed attention mechanism not only improves
performance but also allows inspecting what regions of the audio were taken
into consideration by the network when outputting a given category.
Gossip Learning with Linear Models on Fully Distributed Data
Authors: Róbert Ormándi, István Hegedüs, Márk Jelasity
Date: 2011-09-07 00:00:00
Tasks: Ensemble Learning

Machine learning over fully distributed data poses an important problem in
peer-to-peer (P2P) applications. In this model we have one data record at each
network node, but without the possibility to move raw data due to privacy
considerations. For example, user profiles, ratings, history, or sensor
readings can represent this case. This problem is difficult, because there is
no possibility to learn local models, the system model offers almost no
guarantees for reliability, yet the communication cost needs to be kept low.
Here we propose gossip learning, a generic approach that is based on multiple
models taking random walks over the network in parallel, while applying an
online learning algorithm to improve themselves, and getting combined via
ensemble learning methods. We present an instantiation of this approach for the
case of classification with linear models. Our main contribution is an ensemble
learning method which---through the continuous combination of the models in the
network---implements a virtual weighted voting mechanism over an exponential
number of models at practically no extra cost as compared to independent random
walks. We prove the convergence of the method theoretically, and perform
extensive experiments on benchmark datasets. Our experimental analysis
demonstrates the performance and robustness of the proposed approach.
Learning short-term past as predictor of human behavior in commercial buildings
Authors: Romana Markovic, Jérôme Frisch, Christoph van Treeck
Date: 2018-09-17 00:00:00

This paper addresses the question of identifying the time-window in
short-term past from which the information regarding the future occupant's
window opening actions and resulting window states in buildings can be
predicted. The addressed sequence duration was in the range between 30 and 240
time-steps of indoor climate data, where the applied temporal discretization
was one minute. For that purpose, a deep neural network is trained to predict
the window states, where the input sequence duration is handled as an
additional hyperparameter. Eventually, the relationship between the prediction
accuracy and the time-lag of the predicted window state in future is analyzed.
The results pointed out, that the optimal predictive performance was achieved
for the case where 60 time-steps of the indoor climate data were used as input.
Additionally, the results showed that very long sequences (120-240 time-steps)
could be addressed efficiently, given the right hyperprameters. Hence, the use
of the memory over previous hours of high-resolution indoor climate data did
not improve the predictive performance, when compared to the case where 30/60
minutes indoor sequences were used. The analysis of the prediction accuracy in
the form of F1 score for the different time-lag of future window states dropped
from 0.51 to 0.27, when shifting the prediction target from 10 to 60 minutes in
future.
Propagation Networks for Model-Based Control Under Partial Observation
Authors: Yunzhu Li, Jiajun Wu, Jun-Yan Zhu, Joshua B. Tenenbaum, Antonio Torralba, Russ Tedrake
Date: 2018-09-28 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning

There has been an increasing interest in learning dynamics simulators for
model-based control. Compared with off-the-shelf physics engines, a learnable
simulator can quickly adapt to unseen objects, scenes, and tasks. However,
existing models like interaction networks only work for fully observable
systems; they also only consider pairwise interactions within a single time
step, both restricting their use in practical systems. We introduce Propagation
Networks (PropNet), a differentiable, learnable dynamics model that handles
partially observable scenarios and enables instantaneous propagation of signals
beyond pairwise interactions. Experiments show that our propagation networks
not only outperform current learnable physics engines in forward simulation,
but also achieve superior performance on various control tasks. Compared with
existing model-free deep reinforcement learning algorithms, model-based control
with propagation networks is more accurate, efficient, and generalizable to
new, partially observable scenes and tasks.
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
Authors: Jacob R. Gardner, Geoff Pleiss, David Bindel, Kilian Q. Weinberger, Andrew Gordon Wilson
Date: 2018-09-28 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Gaussian Processes, GPU

Despite advances in scalable models, the inference tools used for Gaussian processes (GPs) have yet to fully capitalize on developments in computing hardware. We present an efficient and general approach to GP inference based on Blackbox Matrix-Matrix multiplication (BBMM). BBMM inference uses a modified batched version of the conjugate gradients algorithm to derive all terms for training and inference in a single call. BBMM reduces the asymptotic complexity of exact GP inference from $O(n^3)$ to $O(n^2)$. Adapting this algorithm to scalable approximations and complex GP models simply requires a routine for efficient matrix-matrix multiplication with the kernel and its derivative. In addition, BBMM uses a specialized preconditioner to substantially speed up convergence. In experiments we show that BBMM effectively uses GPU hardware to dramatically accelerate both exact GP inference and scalable approximations. Additionally, we provide GPyTorch, a software platform for scalable GP inference via BBMM, built on PyTorch.
Formal Context Generation using Dirichlet Distributions
Authors: Maximilian Felde, Tom Hanika
Date: 2018-09-28 00:00:00

We suggest an improved way to randomly generate formal contexts based on
Dirichlet distributions. For this purpose we investigate the predominant way to
generate formal contexts, a coin-tossing model, recapitulate some of its
shortcomings and examine its stochastic model. Building up on this we propose
our Dirichlet model and develop an algorithm employing this idea. By comparing
our generation model to a coin-tossing model we show that our approach is a
significant improvement with respect to the variety of contexts generated.
Finally, we outline a possible application in null model generation for formal
contexts.
SALSA-TEXT : self attentive latent space based adversarial text generation
Authors: Jules Gagnon-Marchand, Hamed Sadeghi, Md. Akmal Haidar, Mehdi Rezagholizadeh
Date: 2018-09-28 00:00:00
Tasks: Adversarial Text, Image Generation, Sentence, Sentence Compression, Text Generation

Inspired by the success of self attention mechanism and Transformer
architecture in sequence transduction and image generation applications, we
propose novel self attention-based architectures to improve the performance of
adversarial latent code- based schemes in text generation. Adversarial latent
code-based text generation has recently gained a lot of attention due to their
promising results. In this paper, we take a step to fortify the architectures
used in these setups, specifically AAE and ARAE. We benchmark two latent
code-based methods (AAE and ARAE) designed based on adversarial setups. In our
experiments, the Google sentence compression dataset is utilized to compare our
method with these methods using various objective and subjective measures. The
experiments demonstrate the proposed (self) attention-based models outperform
the state-of-the-art in adversarial code-based text generation.
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
Authors: Chao Ma, Sebastian Tschiatschek, Konstantina Palla, José Miguel Hernández-Lobato, Sebastian Nowozin, Cheng Zhang
Date: 2018-09-28 00:00:00
Proceeding: ICLR 2019 5
Tasks: Decision Making, Diagnostic, Experimental Design, Vocal Bursts Intensity Prediction

Many real-life decision-making situations allow further relevant information to be acquired at a specific cost, for example, in assessing the health status of a patient we may decide to take additional measurements such as diagnostic tests or imaging scans before making a final assessment. Acquiring more relevant information enables better decision making, but may be costly. How can we trade off the desire to make good decisions by acquiring further information with the cost of performing that acquisition? To this end, we propose a principled framework, named EDDI (Efficient Dynamic Discovery of high-value Information), based on the theory of Bayesian experimental design. In EDDI, we propose a novel partial variational autoencoder (Partial VAE) to predict missing data entries problematically given any subset of the observed ones, and combine it with an acquisition function that maximizes expected information gain on a set of target variables. We show cost reduction at the same decision quality and improved decision quality at the same cost in multiple machine learning benchmarks and two real-world health-care applications.
Channel-wise and Spatial Feature Modulation Network for Single Image Super-Resolution
Authors: Yanting Hu, Jie Li, Yuanfei Huang, Xinbo Gao
Date: 2018-09-28 00:00:00
Tasks: Image Reconstruction, Image Super-Resolution, Super-Resolution

The performance of single image super-resolution has achieved significant
improvement by utilizing deep convolutional neural networks (CNNs). The
features in deep CNN contain different types of information which make
different contributions to image reconstruction. However, most CNN-based models
lack discriminative ability for different types of information and deal with
them equally, which results in the representational capacity of the models
being limited. On the other hand, as the depth of neural networks grows, the
long-term information coming from preceding layers is easy to be weaken or lost
in late layers, which is adverse to super-resolving image. To capture more
informative features and maintain long-term information for image
super-resolution, we propose a channel-wise and spatial feature modulation
(CSFM) network in which a sequence of feature-modulation memory (FMM) modules
is cascaded with a densely connected structure to transform low-resolution
features to high informative features. In each FMM module, we construct a set
of channel-wise and spatial attention residual (CSAR) blocks and stack them in
a chain structure to dynamically modulate multi-level features in a
global-and-local manner. This feature modulation strategy enables the high
contribution information to be enhanced and the redundant information to be
suppressed. Meanwhile, for long-term information persistence, a gated fusion
(GF) node is attached at the end of the FMM module to adaptively fuse
hierarchical features and distill more effective information via the dense skip
connections and the gating mechanism. Extensive quantitative and qualitative
evaluations on benchmark datasets illustrate the superiority of our proposed
method over the state-of-the-art methods.
Weighted Spectral Embedding of Graphs
Authors: Thomas Bonald, Alexandre Hollocou, Marc Lelarge
Date: 2018-09-28 00:00:00

We present a novel spectral embedding of graphs that incorporates weights
assigned to the nodes, quantifying their relative importance. This spectral
embedding is based on the first eigenvectors of some properly normalized
version of the Laplacian. We prove that these eigenvectors correspond to the
configurations of lowest energy of an equivalent physical system, either
mechanical or electrical, in which the weight of each node can be interpreted
as its mass or its capacitance, respectively. Experiments on a real dataset
illustrate the impact of weighting on the embedding.
Rethinking Self-driving: Multi-task Knowledge for Better Generalization and Accident Explanation Ability
Authors: Zhihao Li, Toshiyuki Motoyoshi, Kazuma Sasaki, Tetsuya OGATA, Shigeki SUGANO
Date: 2018-09-28 00:00:00
Tasks: Diversity

Current end-to-end deep learning driving models have two problems: (1) Poor
generalization ability of unobserved driving environment when diversity of
training driving dataset is limited (2) Lack of accident explanation ability
when driving models don't work as expected. To tackle these two problems,
rooted on the believe that knowledge of associated easy task is benificial for
addressing difficult task, we proposed a new driving model which is composed of
perception module for \textit{see and think} and driving module for
\textit{behave}, and trained it with multi-task perception-related basic
knowledge and driving knowledge stepwisely.
  Specifically segmentation map and depth map (pixel level understanding of
images) were considered as \textit{what \& where} and \textit{how far}
knowledge for tackling easier driving-related perception problems before
generating final control commands for difficult driving task. The results of
experiments demonstrated the effectiveness of multi-task perception knowledge
for better generalization and accident explanation ability. With our method the
average sucess rate of finishing most difficult navigation tasks in untrained
city of CoRL test surpassed current benchmark method for 15 percent in trained
weather and 20 percent in untrained weathers. Demonstration video link is:
https://www.youtube.com/watch?v=N7ePnnZZwdE
Which Knowledge Graph Is Best for Me?
Authors: Michael Färber, Achim Rettinger
Date: 2018-09-28 00:00:00
Tasks: Knowledge Graphs, Survey

In recent years, DBpedia, Freebase, OpenCyc, Wikidata, and YAGO have been
published as noteworthy large, cross-domain, and freely available knowledge
graphs. Although extensively in use, these knowledge graphs are hard to compare
against each other in a given setting. Thus, it is a challenge for researchers
and developers to pick the best knowledge graph for their individual needs. In
our recent survey, we devised and applied data quality criteria to the
above-mentioned knowledge graphs. Furthermore, we proposed a framework for
finding the most suitable knowledge graph for a given setting. With this paper
we intend to ease the access to our in-depth survey by presenting simplified
rules that map individual data quality requirements to specific knowledge
graphs. However, this paper does not intend to replace our previously
introduced decision-support framework. For an informed decision on which KG is
best for you we still refer to our in-depth survey.