pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning with Sets and Point Clouds
Authors: Siamak Ravanbakhsh, Jeff Schneider, Barnabas Poczos
Date: 2016-11-14 00:00:00
Tasks: Clustering, Deep Learning, General Classification, Outlier Detection

We introduce a simple permutation equivariant layer for deep learning with
set structure.This type of layer, obtained by parameter-sharing, has a simple
implementation and linear-time complexity in the size of each set. We use deep
permutation-invariant networks to perform point-could classification and
MNIST-digit summation, where in both cases the output is invariant to
permutations of the input. In a semi-supervised setting, where the goal is make
predictions for each instance within a set, we demonstrate the usefulness of
this type of layer in set-outlier detection as well as semi-supervised learning
with clustering side-information.
Bandits with Movement Costs and Adaptive Pricing
Authors: Tomer Koren, Roi Livni, Yishay Mansour
Date: 2017-02-24 00:00:00

We extend the model of Multi-armed Bandit with unit switching cost to
incorporate a metric between the actions. We consider the case where the metric
over the actions can be modeled by a complete binary tree, and the distance
between two leaves is the size of the subtree of their least common ancestor,
which abstracts the case that the actions are points on the continuous interval
$[0,1]$ and the switching cost is their distance. In this setting, we give a
new algorithm that establishes a regret of $\widetilde{O}(\sqrt{kT} + T/k)$,
where $k$ is the number of actions and $T$ is the time horizon. When the set of
actions corresponds to whole $[0,1]$ interval we can exploit our method for the
task of bandit learning with Lipschitz loss functions, where our algorithm
achieves an optimal regret rate of $\widetilde{\Theta}(T^{2/3})$, which is the
same rate one obtains when there is no penalty for movements. As our main
application, we use our new algorithm to solve an adaptive pricing problem.
Specifically, we consider the case of a single seller faced with a stream of
patient buyers. Each buyer has a private value and a window of time in which
they are interested in buying, and they buy at the lowest price in the window,
if it is below their value. We show that with an appropriate discretization of
the prices, the seller can achieve a regret of $\widetilde{O}(T^{2/3})$
compared to the best fixed price in hindsight, which outperform the previous
regret bound of $\widetilde{O}(T^{3/4})$ for the problem.
Calibrating Energy-based Generative Adversarial Networks
Authors: Zihang Dai, Amjad Almahairi, Philip Bachman, Eduard Hovy, Aaron Courville
Date: 2017-02-06 00:00:00
Tasks: Image Generation

In this paper, we propose to equip Generative Adversarial Networks with the
ability to produce direct energy estimates for samples.Specifically, we propose
a flexible adversarial training framework, and prove this framework not only
ensures the generator converges to the true data distribution, but also enables
the discriminator to retain the density information at the global optimal. We
derive the analytic form of the induced solution, and analyze the properties.
In order to make the proposed framework trainable in practice, we introduce two
effective approximation techniques. Empirically, the experiment results closely
match our theoretical analysis, verifying the discriminator is able to recover
the energy of data distribution.
Multi-Context Attention for Human Pose Estimation
Authors: Xiao Chu, Wei Yang, Wanli Ouyang, Cheng Ma, Alan L. Yuille, Xiaogang Wang
Date: 2017-02-24 00:00:00
Proceeding: CVPR 2017 7
Tasks: Pose Estimation

In this paper, we propose to incorporate convolutional neural networks with a
multi-context attention mechanism into an end-to-end framework for human pose
estimation. We adopt stacked hourglass networks to generate attention maps from
features at multiple resolutions with various semantics. The Conditional Random
Field (CRF) is utilized to model the correlations among neighboring regions in
the attention map. We further combine the holistic attention model, which
focuses on the global consistency of the full human body, and the body part
attention model, which focuses on the detailed description for different body
parts. Hence our model has the ability to focus on different granularity from
local salient regions to global semantic-consistent spaces. Additionally, we
design novel Hourglass Residual Units (HRUs) to increase the receptive field of
the network. These units are extensions of residual units with a side branch
incorporating filters with larger receptive fields, hence features with various
scales are learned and combined within the HRUs. The effectiveness of the
proposed multi-context attention mechanism and the hourglass residual units is
evaluated on two widely used human pose estimation benchmarks. Our approach
outperforms all existing methods on both benchmarks over all the body parts.
Control of the Correlation of Spontaneous Neuron Activity in Biological and Noise-activated CMOS Artificial Neural Microcircuits
Authors: Ramin M. Hasani, Giorgio Ferrari, Hideaki Yamamoto, Sho Kono, Koji Ishihara, Soya Fujimori, Takashi Tanii, Enrico Prati
Date: 2017-02-24 00:00:00

There are several indications that brain is organized not on a basis of
individual unreliable neurons, but on a micro-circuital scale providing Lego
blocks employed to create complex architectures. At such an intermediate scale,
the firing activity in the microcircuits is governed by collective effects
emerging by the background noise soliciting spontaneous firing, the degree of
mutual connections between the neurons, and the topology of the connections. We
compare spontaneous firing activity of small populations of neurons adhering to
an engineered scaffold with simulations of biologically plausible CMOS
artificial neuron populations whose spontaneous activity is ignited by tailored
background noise. We provide a full set of flexible and low-power consuming
silicon blocks including neurons, excitatory and inhibitory synapses, and both
white and pink noise generators for spontaneous firing activation. We achieve a
comparable degree of correlation of the firing activity of the biological
neurons by controlling the kind and the number of connection among the silicon
neurons. The correlation between groups of neurons, organized as a ring of four
distinct populations connected by the equivalent of interneurons, is triggered
more effectively by adding multiple synapses to the connections than increasing
the number of independent point-to-point connections. The comparison between
the biological and the artificial systems suggests that a considerable number
of synapses is active also in biological populations adhering to engineered
scaffolds.
Building Usage Profiles Using Deep Neural Nets
Authors: Domenic Curro, Konstantinos G. Derpanis, Andriy V. Miranskyy
Date: 2017-02-23 00:00:00

To improve software quality, one needs to build test scenarios resembling the
usage of a software product in the field. This task is rendered challenging
when a product's customer base is large and diverse. In this scenario, existing
profiling approaches, such as operational profiling, are difficult to apply. In
this work, we consider publicly available video tutorials of a product to
profile usage. Our goal is to construct an automatic approach to extract
information about user actions from instructional videos. To achieve this goal,
we use a Deep Convolutional Neural Network (DCNN) to recognize user actions.
Our pilot study shows that a DCNN trained to recognize user actions in video
can classify five different actions in a collection of 236 publicly available
Microsoft Word tutorial videos (published on YouTube). In our empirical
evaluation we report a mean average precision of 94.42% across all actions.
This study demonstrates the efficacy of DCNN-based methods for extracting
software usage information from videos. Moreover, this approach may aid in
other software engineering activities that require information about customer
usage of a product.
GapTV: Accurate and Interpretable Low-Dimensional Regression and Classification
Authors: Wesley Tansey, James G. Scott
Date: 2017-02-23 00:00:00
Tasks: Additive models, Classification, General Classification, regression

We consider the problem of estimating a regression function in the common
situation where the number of features is small, where interpretability of the
model is a high priority, and where simple linear or additive models fail to
provide adequate performance. To address this problem, we present GapTV, an
approach that is conceptually related both to CART and to the more recent CRISP
algorithm, a state-of-the-art alternative method for interpretable nonlinear
regression. GapTV divides the feature space into blocks of constant value and
fits the value of all blocks jointly via a convex optimization routine. Our
method is fully data-adaptive, in that it incorporates highly robust routines
for tuning all hyperparameters automatically. We compare our approach against
CART and CRISP and demonstrate that GapTV finds a much better trade-off between
accuracy and interpretability.
Improving high-pass fusion method using wavelets
Authors: Hamid Reza Shahdoosti
Date: 2017-02-23 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In an appropriate image fusion method, spatial information of the
panchromatic image is injected into the multispectral images such that the
spectral information is not distorted. The high-pass modulation method is a
successful method in image fusion. However, the main drawback of this method is
that this technique uses the boxcar filter to extract the high frequency
information of the panchromatic image. Using the boxcar filter introduces the
ringing effect into the fused image. To cope with this problem, we use the
wavelet transform instead of boxcar filters. Then, the results of the proposed
method and those of other methods such as, Brovey, IHS, and PCA ones are
compared. Experiments show the superiority of the proposed method in terms of
correlation coefficient and mutual information.
Toward Streaming Synapse Detection with Compositional ConvNets
Authors: Shibani Santurkar, David Budden, Alexander Matveev, Heather Berlin, Hayk Saribekyan, Yaron Meirovitch, Nir Shavit
Date: 2017-02-23 00:00:00
Tasks: CPU

Connectomics is an emerging field in neuroscience that aims to reconstruct
the 3-dimensional morphology of neurons from electron microscopy (EM) images.
Recent studies have successfully demonstrated the use of convolutional neural
networks (ConvNets) for segmenting cell membranes to individuate neurons.
However, there has been comparatively little success in high-throughput
identification of the intercellular synaptic connections required for deriving
connectivity graphs.
  In this study, we take a compositional approach to segmenting synapses,
modeling them explicitly as an intercellular cleft co-located with an
asymmetric vesicle density along a cell membrane. Instead of requiring a deep
network to learn all natural combinations of this compositionality, we train
lighter networks to model the simpler marginal distributions of membranes,
clefts and vesicles from just 100 electron microscopy samples. These feature
maps are then combined with simple rules-based heuristics derived from prior
biological knowledge.
  Our approach to synapse detection is both more accurate than previous
state-of-the-art (7% higher recall and 5% higher F1-score) and yields a 20-fold
speed-up compared to the previous fastest implementations. We demonstrate by
reconstructing the first complete, directed connectome from the largest
available anisotropic microscopy dataset (245 GB) of mouse somatosensory cortex
(S1) in just 9.7 hours on a single shared-memory CPU system. We believe that
this work marks an important step toward the goal of a microscope-pace
streaming connectomics pipeline.
Feasibility of Principal Component Analysis in hand gesture recognition system
Authors: Tanu Srivastava, Raj Shree Singh, Sunil Kumar, Pavan Chakraborty
Date: 2017-02-23 00:00:00
Tasks: Dimensionality Reduction, Face Detection, Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition

Nowadays actions are increasingly being handled in electronic ways, instead
of physical interaction. From earlier times biometrics is used in the
authentication of a person. It recognizes a person by using a human trait
associated with it like eyes (by calculating the distance between the eyes) and
using hand gestures, fingerprint detection, face detection etc. Advantages of
using these traits for identification are that they uniquely identify a person
and cannot be forgotten or lost. These are unique features of a human being
which are being used widely to make the human life simpler. Hand gesture
recognition system is a powerful tool that supports efficient interaction
between the user and the computer. The main moto of hand gesture recognition
research is to create a system which can recognise specific hand gestures and
use them to convey useful information for device control. This paper presents
an experimental study over the feasibility of principal component analysis in
hand gesture recognition system. PCA is a powerful tool for analyzing data. The
primary goal of PCA is dimensionality reduction. Frames are extracted from the
Sheffield KInect Gesture (SKIG) dataset. The implementation is done by creating
a training set and then training the recognizer. It uses Eigen space by
processing the eigenvalues and eigenvectors of the images in training set.
Euclidean distance with the threshold value is used as similarity metric to
recognize the gestures. The experimental results show that PCA is feasible to
be used for hand gesture recognition system.
Stochastic Newton and Quasi-Newton Methods for Large Linear Least-squares Problems
Authors: Julianne Chung, Matthias Chung, J. Tanner Slagel, Luis Tenorio
Date: 2017-02-23 00:00:00

We describe stochastic Newton and stochastic quasi-Newton approaches to
efficiently solve large linear least-squares problems where the very large data
sets present a significant computational burden (e.g., the size may exceed
computer memory or data are collected in real-time). In our proposed framework,
stochasticity is introduced in two different frameworks as a means to overcome
these computational limitations, and probability distributions that can exploit
structure and/or sparsity are considered. Theoretical results on consistency of
the approximations for both the stochastic Newton and the stochastic
quasi-Newton methods are provided. The results show, in particular, that
stochastic Newton iterates, in contrast to stochastic quasi-Newton iterates,
may not converge to the desired least-squares solution. Numerical examples,
including an example from extreme learning machines, demonstrate the potential
applications of these methods.
Causal Regularization
Authors: Mohammad Taha Bahadori, Krzysztof Chalupka, Edward Choi, Robert Chen, Walter F. Stewart, Jimeng Sun
Date: 2017-02-08 00:00:00
Tasks: Representation Learning

In application domains such as healthcare, we want accurate predictive models
that are also causally interpretable. In pursuit of such models, we propose a
causal regularizer to steer predictive models towards causally-interpretable
solutions and theoretically study its properties. In a large-scale analysis of
Electronic Health Records (EHR), our causally-regularized model outperforms its
L1-regularized counterpart in causal accuracy and is competitive in predictive
performance. We perform non-linear causality analysis by causally regularizing
a special neural network architecture. We also show that the proposed causal
regularizer can be used together with neural representation learning algorithms
to yield up to 20% improvement over multilayer perceptron in detecting
multivariate causation, a situation common in healthcare, where many causal
factors should occur simultaneously to have an effect on the target variable.
k-Means Clustering and Ensemble of Regressions: An Algorithm for the ISIC 2017 Skin Lesion Segmentation Challenge
Authors: David Alvarez, Monica Iglesias
Date: 2017-02-23 00:00:00
Tasks: Clustering, Image Segmentation, Lesion Detection, Lesion Segmentation, regression, Segmentation, Semantic Segmentation, Skin Lesion Segmentation

This abstract briefly describes a segmentation algorithm developed for the
ISIC 2017 Skin Lesion Detection Competition hosted at [ref]. The objective of
the competition is to perform a segmentation (in the form of a binary mask
image) of skin lesions in dermoscopic images as close as possible to a
segmentation performed by trained clinicians, which is taken as ground truth.
This project only takes part in the segmentation phase of the challenge. The
other phases of the competition (feature extraction and lesion identification)
are not considered.
  The proposed algorithm consists of 4 steps: (1) lesion image preprocessing,
(2) image segmentation using k-means clustering of pixel colors, (3)
calculation of a set of features describing the properties of each segmented
region, and (4) calculation of a final score for each region, representing the
likelihood of corresponding to a suitable lesion segmentation. The scores in
step (4) are obtained by averaging the results of 2 different regression models
using the scores of each region as input. Before using the algorithm these
regression models must be trained using the training set of images and ground
truth masks provided by the Competition. Steps 2 to 4 are repeated with an
increasing number of clusters (and therefore the image is segmented into more
regions) until there is no further improvement of the calculated scores.
Inherent Biases of Recurrent Neural Networks for Phonological Assimilation and Dissimilation
Authors: Amanda Doucette
Date: 2017-02-23 00:00:00
Proceeding: WS 2017 4

A recurrent neural network model of phonological pattern learning is
proposed. The model is a relatively simple neural network with one recurrent
layer, and displays biases in learning that mimic observed biases in human
learning. Single-feature patterns are learned faster than two-feature patterns,
and vowel or consonant-only patterns are learned faster than patterns involving
vowels and consonants, mimicking the results of laboratory learning
experiments. In non-recurrent models, capturing these biases requires the use
of alpha features or some other representation of repeated features, but with a
recurrent neural network, these elaborations are not necessary.
Causal Discovery Using Proxy Variables
Authors: Mateo Rojas-Carulla, Marco Baroni, David Lopez-Paz
Date: 2017-02-23 00:00:00
Tasks: Causal Discovery, Relation

Discovering causal relations is fundamental to reasoning and intelligence. In
particular, observational causal discovery algorithms estimate the cause-effect
relation between two random entities $X$ and $Y$, given $n$ samples from
$P(X,Y)$.
  In this paper, we develop a framework to estimate the cause-effect relation
between two static entities $x$ and $y$: for instance, an art masterpiece $x$
and its fraudulent copy $y$. To this end, we introduce the notion of proxy
variables, which allow the construction of a pair of random entities $(A,B)$
from the pair of static entities $(x,y)$. Then, estimating the cause-effect
relation between $A$ and $B$ using an observational causal discovery algorithm
leads to an estimation of the cause-effect relation between $x$ and $y$. For
example, our framework detects the causal relation between unprocessed
photographs and their modifications, and orders in time a set of shuffled
frames from a video.
  As our main case study, we introduce a human-elicited dataset of 10,000 pairs
of casually-linked pairs of words from natural language. Our methods discover
75% of these causal relations. Finally, we discuss the role of proxy variables
in machine learning, as a general tool to incorporate static knowledge into
prediction tasks.
Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm
Authors: Simon Fischer, Ingo Steinwart
Date: 2017-02-23 00:00:00
Tasks: regression

Learning rates for least-squares regression are typically expressed in terms of $L_2$-norms. In this paper we extend these rates to norms stronger than the $L_2$-norm without requiring the regression function to be contained in the hypothesis space. In the special case of Sobolev reproducing kernel Hilbert spaces used as hypotheses spaces, these stronger norms coincide with fractional Sobolev norms between the used Sobolev space and $L_2$. As a consequence, not only the target function but also some of its derivatives can be estimated without changing the algorithm. From a technical point of view, we combine the well-known integral operator techniques with an embedding property, which so far has only been used in combination with empirical process arguments. This combination results in new finite sample bounds with respect to the stronger norms. From these finite sample bounds our rates easily follow. Finally, we prove the asymptotic optimality of our results in many cases.
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks
Authors: Patrick Ferdinand Christ, Florian Ettlinger, Felix Grün, Mohamed Ezzeldin A. Elshaera, Jana Lipkova, Sebastian Schlecht, Freba Ahmaddy, Sunil Tatavarty, Marc Bickel, Patrick Bilic, Markus Rempfler, Felix Hofmann, Melvin D Anastasi, Seyed-Ahmad Ahmadi, Georgios Kaissis, Julian Holch, Wieland Sommer, Rickmer Braren, Volker Heinemann, Bjoern Menze
Date: 2017-02-20 00:00:00
Tasks: Automatic Liver And Tumor Segmentation, Lesion Segmentation, Segmentation, Tumor Segmentation

Automatic segmentation of the liver and hepatic lesions is an important step
towards deriving quantitative biomarkers for accurate clinical diagnosis and
computer-aided decision support systems. This paper presents a method to
automatically segment liver and lesions in CT and MRI abdomen images using
cascaded fully convolutional neural networks (CFCNs) enabling the segmentation
of a large-scale medical trial or quantitative image analysis. We train and
cascade two FCNs for a combined segmentation of the liver and its lesions. In
the first step, we train a FCN to segment the liver as ROI input for a second
FCN. The second FCN solely segments lesions within the predicted liver ROIs of
step 1. CFCN models were trained on an abdominal CT dataset comprising 100
hepatic tumor volumes. Validations on further datasets show that CFCN-based
semantic liver and lesion segmentation achieves Dice scores over 94% for liver
with computation times below 100s per volume. We further experimentally
demonstrate the robustness of the proposed method on an 38 MRI liver tumor
volumes and the public 3DIRCAD dataset.
Ontologies in System Engineering: a Field Report
Authors: Marco Menapace, Armando Tacchella
Date: 2017-02-23 00:00:00

In recent years ontologies enjoyed a growing popularity outside specialized
AI communities. System engineering is no exception to this trend, with
ontologies being proposed as a basis for several tasks in complex industrial
implements, including system design, monitoring and diagnosis. In this paper,
we consider four different contributions to system engineering wherein
ontologies are instrumental to provide enhancements over traditional ad-hoc
techniques. For each application, we briefly report the methodologies, the
tools and the results obtained with the goal to provide an assessment of merits
and limits of ontologies in such domains.