pwc-archive/papers-with-abstracts huggingface dataset:
Generative Adversarial Learning for Spectrum Sensing
Authors: Kemal Davaslioglu, Yalin E. Sagduyu
Date: 2018-04-02 00:00:00
Tasks: BIG-bench Machine Learning, Data Augmentation, Domain Adaptation, Generative Adversarial Network

A novel approach of training data augmentation and domain adaptation is
presented to support machine learning applications for cognitive radio. Machine
learning provides effective tools to automate cognitive radio functionalities
by reliably extracting and learning intrinsic spectrum dynamics. However, there
are two important challenges to overcome, in order to fully utilize the machine
learning benefits with cognitive radios. First, machine learning requires
significant amount of truthed data to capture complex channel and emitter
characteristics, and train the underlying algorithm (e.g., a classifier).
Second, the training data that has been identified for one spectrum environment
cannot be used for another one (e.g., after channel and emitter conditions
change). To address these challenges, a generative adversarial network (GAN)
with deep learning structures is used to 1)~generate additional synthetic
training data to improve classifier accuracy, and 2) adapt training data to
spectrum dynamics. This approach is applied to spectrum sensing by assuming
only limited training data without knowledge of spectrum statistics. Machine
learning classifiers are trained with limited, augmented and adapted training
data to detect signals. Results show that training data augmentation increases
the classifier accuracy significantly and this increase is sustained with
domain adaptation as spectrum conditions change.
Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data
Authors: Bao Wang, Xiyang Luo, Fangbo Zhang, Baichuan Yuan, Andrea L. Bertozzi, P. Jeffrey Brantingham
Date: 2018-04-02 00:00:00

We present a generic framework for spatio-temporal (ST) data modeling,
analysis, and forecasting, with a special focus on data that is sparse in both
space and time. Our multi-scaled framework is a seamless coupling of two major
components: a self-exciting point process that models the macroscale
statistical behaviors of the ST data and a graph structured recurrent neural
network (GSRNN) to discover the microscale patterns of the ST data on the
inferred graph. This novel deep neural network (DNN) incorporates the real time
interactions of the graph nodes to enable more accurate real time forecasting.
The effectiveness of our method is demonstrated on both crime and traffic
forecasting.
Stochastic EM for Shuffled Linear Regression
Authors: Abubakar Abid, James Zou
Date: 2018-04-02 00:00:00
Tasks: regression

We consider the problem of inference in a linear regression model in which
the relative ordering of the input features and output labels is not known.
Such datasets naturally arise from experiments in which the samples are
shuffled or permuted during the protocol. In this work, we propose a framework
that treats the unknown permutation as a latent variable. We maximize the
likelihood of observations using a stochastic expectation-maximization (EM)
approach. We compare this to the dominant approach in the literature, which
corresponds to hard EM in our framework. We show on synthetic data that the
stochastic EM algorithm we develop has several advantages, including lower
parameter error, less sensitivity to the choice of initialization, and
significantly better performance on datasets that are only partially shuffled.
We conclude by performing two experiments on real datasets that have been
partially shuffled, in which we show that the stochastic EM algorithm can
recover the weights with modest error.
Multimodal Registration of Retinal Images Using Domain-Specific Landmarks and Vessel Enhancement
Authors: Álvaro S. Hervella, José Rouco, Jorge Novo, Marcos Ortega
Date: 2018-03-02 00:00:00

The analysis of different image modalities is frequently performed in
ophthalmology as it provides complementary information for the diagnosis and
follow-up of relevant diseases, like hypertension or diabetes. This work
presents a hybrid method for the multimodal registration of color fundus
retinography and fluorescein angiography. The proposed method combines a
feature-based approach, using domain-specific landmarks, with an
intensity-based approach that employs a domain-adapted similarity metric. The
methodology is tested on a dataset of 59 image pairs containing both healthy
and pathological cases. The results show a satisfactory performance of the
proposed combined approach in this multimodal scenario, improving the
registration accuracy achieved by the feature-based and the intensity-based
approaches.
Interactive Hand Pose Estimation: Boosting accuracy in localizing extended finger joints
Authors: Cairong Zhang, Guijin Wang, Hengkai Guo, Xinghao Chen, Fei Qiao, Huazhong Yang
Date: 2018-04-02 00:00:00
Tasks: 3D Hand Pose Estimation, Hand Pose Estimation, Pose Estimation

Accurate 3D hand pose estimation plays an important role in Human Machine
Interaction (HMI). In the reality of HMI, joints in fingers stretching out,
especially corresponding fingertips, are much more important than other joints.
We propose a novel method to refine stretching-out finger joint locations after
obtaining rough hand pose estimation. It first detects which fingers are
stretching out, then neighbor pixels of certain joint vote for its new location
based on random forests. The algorithm is tested on two public datasets: MSRA15
and ICVL. After the refinement stage of stretching-out fingers, errors of
predicted HMI finger joint locations are significantly reduced. Mean error of
all fingertips reduces around 5mm (relatively more than 20%). Stretching-out
fingertip locations are even more precise, which in MSRA15 reduces 10.51mm
(relatively 41.4%).
DeepMVS: Learning Multi-view Stereopsis
Authors: Po-Han Huang, Kevin Matzen, Johannes Kopf, Narendra Ahuja, Jia-Bin Huang
Date: 2018-04-02 00:00:00
Proceeding: CVPR 2018 6

We present DeepMVS, a deep convolutional neural network (ConvNet) for
multi-view stereo reconstruction. Taking an arbitrary number of posed images as
input, we first produce a set of plane-sweep volumes and use the proposed
DeepMVS network to predict high-quality disparity maps. The key contributions
that enable these results are (1) supervised pretraining on a photorealistic
synthetic dataset, (2) an effective method for aggregating information across a
set of unordered images, and (3) integrating multi-layer feature activations
from the pre-trained VGG-19 network. We validate the efficacy of DeepMVS using
the ETH3D Benchmark. Our results show that DeepMVS compares favorably against
state-of-the-art conventional MVS algorithms and other ConvNet based methods,
particularly for near-textureless regions and thin structures.
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches
Authors: Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse
Date: 2018-03-12 00:00:00
Proceeding: ICLR 2018 1
Tasks: CPU, GPU, Reinforcement Learning

Stochastic neural net weights are used in a variety of contexts, including
regularization, Bayesian neural nets, exploration in reinforcement learning,
and evolution strategies. Unfortunately, due to the large number of weights,
all the examples in a mini-batch typically share the same weight perturbation,
thereby limiting the variance reduction effect of large mini-batches. We
introduce flipout, an efficient method for decorrelating the gradients within a
mini-batch by implicitly sampling pseudo-independent weight perturbations for
each example. Empirically, flipout achieves the ideal linear variance reduction
for fully connected networks, convolutional networks, and RNNs. We find
significant speedups in training neural networks with multiplicative Gaussian
perturbations. We show that flipout is effective at regularizing LSTMs, and
outperforms previous methods. Flipout also enables us to vectorize evolution
strategies: in our experiments, a single GPU with flipout can handle the same
throughput as at least 40 CPU cores using existing methods, equivalent to a
factor-of-4 cost reduction on Amazon Web Services.
Adversarial Teacher-Student Learning for Unsupervised Domain Adaptation
Authors: Zhong Meng, Jinyu Li, Yifan Gong, Biing-Hwang, Juang
Date: 2018-04-02 00:00:00
Tasks: Domain Adaptation, Transfer Learning, Unsupervised Domain Adaptation

The teacher-student (T/S) learning has been shown effective in unsupervised
domain adaptation [1]. It is a form of transfer learning, not in terms of the
transfer of recognition decisions, but the knowledge of posteriori
probabilities in the source domain as evaluated by the teacher model. It learns
to handle the speaker and environment variability inherent in and restricted to
the speech signal in the target domain without proactively addressing the
robustness to other likely conditions. Performance degradation may thus ensue.
In this work, we advance T/S learning by proposing adversarial T/S learning to
explicitly achieve condition-robust unsupervised domain adaptation. In this
method, a student acoustic model and a condition classifier are jointly
optimized to minimize the Kullback-Leibler divergence between the output
distributions of the teacher and student models, and simultaneously, to
min-maximize the condition classification loss. A condition-invariant deep
feature is learned in the adapted student model through this procedure. We
further propose multi-factorial adversarial T/S learning which suppresses
condition variabilities caused by multiple factors simultaneously. Evaluated
with the noisy CHiME-3 test set, the proposed methods achieve relative word
error rate improvements of 44.60% and 5.38%, respectively, over a clean source
model and a strong T/S learning baseline model.
3D Registration of Curves and Surfaces using Local Differential Information
Authors: Carolina Raposo, Joao P. Barreto
Date: 2018-04-02 00:00:00
Proceeding: CVPR 2018 6

This article presents for the first time a global method for registering 3D
curves with 3D surfaces without requiring an initialization. The algorithm
works with 2-tuples point+vector that consist in pairs of points augmented with
the information of their tangents or normals. A closed-form solution for
determining the alignment transformation from a pair of matching 2-tuples is
proposed. In addition, the set of necessary conditions for two 2-tuples to
match is derived. This allows fast search of correspondences that are used in
an hypothesise-and-test framework for accomplishing global registration.
Comparative experiments demonstrate that the proposed algorithm is the first
effective solution for curve vs surface registration, with the method achieving
accurate alignment in situations of small overlap and large percentage of
outliers in a fraction of a second. The proposed framework is extended to the
cases of curve vs curve and surface vs surface registration, with the former
being particularly relevant since it is also a largely unsolved problem.
Updating the generator in PPGN-h with gradients flowing through the encoder
Authors: Hesam Pakdaman
Date: 2018-04-02 00:00:00
Tasks: Generative Adversarial Network

The Generative Adversarial Network framework has shown success in implicitly
modeling data distributions and is able to generate realistic samples. Its
architecture is comprised of a generator, which produces fake data that
superficially seem to belong to the real data distribution, and a discriminator
which is to distinguish fake from genuine samples. The Noiseless Joint Plug &
Play model offers an extension to the framework by simultaneously training
autoencoders. This model uses a pre-trained encoder as a feature extractor,
feeding the generator with global information. Using the Plug & Play network as
baseline, we design a new model by adding discriminators to the Plug & Play
architecture. These additional discriminators are trained to discern real and
fake latent codes, which are the output of the encoder using genuine and
generated inputs, respectively. We proceed to investigate whether this approach
is viable. Experiments conducted for the MNIST manifold show that this indeed
is the case.
Shannon Entropy Estimation in $\infty$-Alphabets from Convergence Results
Authors: Jorge F. Silva
Date: 2017-08-27 00:00:00

The problem of Shannon entropy estimation in countable infinite alphabets is
addressed from the study and use of convergence results of the entropy
functional, which is known to be discontinuous with respect to the total
variation distance in $\infty$-alphabets. Sufficient conditions for the
convergence of the entropy are used, including scenarios with both finitely and
infinitely supported assumptions on the distributions. From this new
perspective, four plug-in histogram-based estimators are studied showing that
convergence results are instrumental to derive new strong consistency and rate
of convergences results. Different scenarios and conditions are used on both
the estimators and the underlying distribution, considering for example finite
and unknown supported assumptions and summable tail bounded conditions.
On denoising modulo 1 samples of a function
Authors: Mihai Cucuringu, Hemant Tyagi
Date: 2017-10-27 00:00:00
Tasks: Denoising

Consider an unknown smooth function $f: [0,1] \rightarrow \mathbb{R}$, and
say we are given $n$ noisy$\mod 1$ samples of $f$, i.e., $y_i = (f(x_i) +
\eta_i)\mod 1$ for $x_i \in [0,1]$, where $\eta_i$ denotes noise. Given the
samples $(x_i,y_i)_{i=1}^{n}$ our goal is to recover smooth, robust estimates
of the clean samples $f(x_i) \bmod 1$. We formulate a natural approach for
solving this problem which works with representations of mod 1 values over the
unit circle. This amounts to solving a quadratically constrained quadratic
program (QCQP) with non-convex constraints involving points lying on the unit
circle. Our proposed approach is based on solving its relaxation which is a
trust-region sub-problem, and hence solvable efficiently. We demonstrate its
robustness to noise % of our approach via extensive simulations on several
synthetic examples, and provide a detailed theoretical analysis.
The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Authors: Philipp Tschandl, Cliff Rosendahl, Harald Kittler
Date: 2018-03-28 00:00:00
Tasks: BIG-bench Machine Learning, Diagnostic, Diversity

Training of neural networks for automated diagnosis of pigmented skin lesions
is hampered by the small size and lack of diversity of available datasets of
dermatoscopic images. We tackle this problem by releasing the HAM10000 ("Human
Against Machine with 10000 training images") dataset. We collected
dermatoscopic images from different populations acquired and stored by
different modalities. Given this diversity we had to apply different
acquisition and cleaning methods and developed semi-automatic workflows
utilizing specifically trained neural networks. The final dataset consists of
10015 dermatoscopic images which are released as a training set for academic
machine learning purposes and are publicly available through the ISIC archive.
This benchmark dataset can be used for machine learning and for comparisons
with human experts. Cases include a representative collection of all important
diagnostic categories in the realm of pigmented lesions. More than 50% of
lesions have been confirmed by pathology, while the ground truth for the rest
of the cases was either follow-up, expert consensus, or confirmation by in-vivo
confocal microscopy.
Specification-Driven Multi-Perspective Predictive Business Process Monitoring (Extended Version)
Authors: Ario Santoso
Date: 2018-04-02 00:00:00
Tasks: Prediction

Predictive analysis in business process monitoring aims at forecasting the
future information of a running business process. The prediction is typically
made based on the model extracted from historical process execution logs (event
logs). In practice, different business domains might require different kinds of
predictions. Hence, it is important to have a means for properly specifying the
desired prediction tasks, and a mechanism to deal with these various prediction
tasks. Although there have been many studies in this area, they mostly focus on
a specific prediction task. This work introduces a language for specifying the
desired prediction tasks, and this language allows us to express various kinds
of prediction tasks. This work also presents a mechanism for automatically
creating the corresponding prediction model based on the given specification.
Thus, different from previous studies, our approach enables us to deal with
various kinds of prediction tasks based on the given specification. A prototype
implementing our approach has been developed and experiments using a real-life
event log have been conducted.
Fast $ε$-free Inference of Simulation Models with Bayesian Conditional Density Estimation
Authors: George Papamakarios, Iain Murray
Date: 2016-05-20 00:00:00
Tasks: Density Estimation

Many statistical models can be simulated forwards but have intractable
likelihoods. Approximate Bayesian Computation (ABC) methods are used to infer
properties of these models from data. Traditionally these methods approximate
the posterior over parameters by conditioning on data being inside an
$\epsilon$-ball around the observed data, which is only correct in the limit
$\epsilon\!\rightarrow\!0$. Monte Carlo methods can then draw samples from the
approximate posterior to approximate predictions or error bars on parameters.
These algorithms critically slow down as $\epsilon\!\rightarrow\!0$, and in
practice draw samples from a broader distribution than the posterior. We
propose a new approach to likelihood-free inference based on Bayesian
conditional density estimation. Preliminary inferences based on limited
simulation data are used to guide later simulations. In some cases, learning an
accurate parametric representation of the entire true posterior distribution
requires fewer model simulations than Monte Carlo ABC methods need to produce a
single sample from an approximate posterior.
MegaDepth: Learning Single-View Depth Prediction from Internet Photos
Authors: Zhengqi Li, Noah Snavely
Date: 2018-04-02 00:00:00
Proceeding: CVPR 2018 6
Tasks: Depth Estimation, Depth Prediction, Prediction, Semantic Segmentation

Single-view depth prediction is a fundamental problem in computer vision.
Recently, deep learning methods have led to significant progress, but such
methods are limited by the available training data. Current datasets based on
3D sensors have key limitations, including indoor-only images (NYU), small
numbers of training examples (Make3D), and sparse sampling (KITTI). We propose
to use multi-view Internet photo collections, a virtually unlimited data
source, to generate training data via modern structure-from-motion and
multi-view stereo (MVS) methods, and present a large depth dataset called
MegaDepth based on this idea. Data derived from MVS comes with its own
challenges, including noise and unreconstructable objects. We address these
challenges with new data cleaning methods, as well as automatically augmenting
our data with ordinal depth relations generated using semantic segmentation. We
validate the use of large amounts of Internet data by showing that models
trained on MegaDepth exhibit strong generalization-not only to novel scenes,
but also to other diverse datasets including Make3D, KITTI, and DIW, even when
no images from those datasets are seen during training.
p-FP: Extraction, Classification, and Prediction of Website Fingerprints with Deep Learning
Authors: Se Eun Oh, Saikrishna Sunkam, Nicholas Hopper
Date: 2017-11-10 00:00:00
Tasks: Feature Engineering, General Classification, Website Fingerprinting Attacks

Recent advances in learning Deep Neural Network (DNN) architectures have
received a great deal of attention due to their ability to outperform
state-of-the-art classifiers across a wide range of applications, with little
or no feature engineering. In this paper, we broadly study the applicability of
deep learning to website fingerprinting. We show that unsupervised DNNs can be
used to extract low-dimensional feature vectors that improve the performance of
state-of-the-art website fingerprinting attacks. When used as classifiers, we
show that they can match or exceed performance of existing attacks across a
range of application scenarios, including fingerprinting Tor website traces,
fingerprinting search engine queries over Tor, defeating fingerprinting
defenses, and fingerprinting TLS-encrypted websites. Finally, we show that DNNs
can be used to predict the fingerprintability of a website based on its
contents, achieving 99% accuracy on a data set of 4500 website downloads.
TacticToe: Learning to Prove with Tactics
Authors: Thibault Gauthier, Cezary Kaliszyk, Josef Urban, Ramana Kumar, Michael Norrish
Date: 2018-04-02 00:00:00
Tasks: CPU

We implement a automated tactical prover TacticToe on top of the HOL4 interactive theorem prover. TacticToe learns from human proofs which mathematical technique is suitable in each proof situation. This knowledge is then used in a Monte Carlo tree search algorithm to explore promising tactic-level proof paths. On a single CPU, with a time limit of 60 seconds, TacticToe proves 66.4 percent of the 7164 theorems in HOL4's standard library, whereas E prover with auto-schedule solves 34.5 percent. The success rate rises to 69.0 percent by combining the results of TacticToe and E prover.
Learning to Reason with HOL4 tactics
Authors: Thibault Gauthier, Cezary Kaliszyk, Josef Urban
Date: 2018-04-02 00:00:00
Tasks: Translation

Techniques combining machine learning with translation to automated reasoning
have recently become an important component of formal proof assistants. Such
"hammer" tech- niques complement traditional proof assistant automation as
implemented by tactics and decision procedures. In this paper we present a
unified proof assistant automation approach which attempts to automate the
selection of appropriate tactics and tactic-sequences com- bined with an
optimized small-scale hammering approach. We implement the technique as a
tactic-level automation for HOL4: TacticToe. It implements a modified
A*-algorithm directly in HOL4 that explores different tactic-level proof paths,
guiding their selection by learning from a large number of previous
tactic-level proofs. Unlike the existing hammer methods, TacticToe avoids
translation to FOL, working directly on the HOL level. By combining tactic
prediction and premise selection, TacticToe is able to re-prove 39 percent of
7902 HOL4 theorems in 5 seconds whereas the best single HOL(y)Hammer strategy
solves 32 percent in the same amount of time.