pwc-archive/papers-with-abstracts huggingface dataset:
Classification of Breast Cancer Histology using Deep Learning
Authors: Aditya Golatkar, Deepak Anand, Amit Sethi
Date: 2018-02-22 00:00:00
Tasks: Classification, Deep Learning, General Classification

Breast Cancer is a major cause of death worldwide among women. Hematoxylin
and Eosin (H&E) stained breast tissue samples from biopsies are observed under
microscopes for the primary diagnosis of breast cancer. In this paper, we
propose a deep learning-based method for classification of H&E stained breast
tissue images released for BACH challenge 2018 by fine-tuning Inception-v3
convolutional neural network (CNN) proposed by Szegedy et al. These images are
to be classified into four classes namely, i) normal tissue, ii) benign tumor,
iii) in-situ carcinoma and iv) invasive carcinoma. Our strategy is to extract
patches based on nuclei density instead of random or grid sampling, along with
rejection of patches that are not rich in nuclei (non-epithelial) regions for
training and testing. Every patch (nuclei-dense region) in an image is
classified in one of the four above mentioned categories. The class of the
entire image is determined using majority voting over the nuclear classes. We
obtained an average four class accuracy of 85% and an average two class
(non-cancer vs. carcinoma) accuracy of 93%, which improves upon a previous
benchmark by Araujo et al.
Investigating the Evolvability of Web Page Load Time
Authors: Brendan Cody-Kenny, Umberto Manganiello, John Farrelly, Adrian Ronayne, Eoghan Considine, Thomas McGuire, Michael O'Neill
Date: 2018-02-22 00:00:00

Client-side Javascript execution environments (browsers) allow anonymous
functions and event-based programming concepts such as callbacks. We
investigate whether a mutate-and-test approach can be used to optimise web page
load time in these environments. First, we characterise a web page load issue
in a benchmark web page and derive performance metrics from page load event
traces. We parse Javascript source code to an AST and make changes to method
calls which appear in a web page load event trace. We present an operator based
solely on code deletion and evaluate an existing "community-contributed"
performance optimising code transform. By exploring Javascript code changes and
exploiting combinations of non-destructive changes, we can optimise page load
time by 41% in our benchmark web page.
MagnifyMe: Aiding Cross Resolution Face Recognition via Identity Aware Synthesis
Authors: Maneet Singh, Shruti Nagpal, Richa Singh, Mayank Vatsa, Angshul Majumdar
Date: 2018-02-22 00:00:00
Tasks: Face Identification, Face Recognition, Image Generation, Super-Resolution

Enhancing low resolution images via super-resolution or image synthesis for
cross-resolution face recognition has been well studied. Several image
processing and machine learning paradigms have been explored for addressing the
same. In this research, we propose Synthesis via Deep Sparse Representation
algorithm for synthesizing a high resolution face image from a low resolution
input image. The proposed algorithm learns multi-level sparse representation
for both high and low resolution gallery images, along with an identity aware
dictionary and a transformation function between the two representations for
face identification scenarios. With low resolution test data as input, the high
resolution test image is synthesized using the identity aware dictionary and
transformation which is then used for face recognition. The performance of the
proposed SDSR algorithm is evaluated on four databases, including one real
world dataset. Experimental results and comparison with existing seven
algorithms demonstrate the efficacy of the proposed algorithm in terms of both
face identification and image quality measures.
Bayesian Approaches to Distribution Regression
Authors: Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic, Seth Flaxman
Date: 2017-05-11 00:00:00
Tasks: regression

Distribution regression has recently attracted much interest as a generic solution to the problem of supervised learning where labels are available at the group level, rather than at the individual level. Current approaches, however, do not propagate the uncertainty in observations due to sampling variability in the groups. This effectively assumes that small and large groups are estimated equally well, and should have equal weight in the final regression. We account for this uncertainty with a Bayesian distribution regression formalism, improving the robustness and performance of the model when group sizes vary. We frame our models in a neural network style, allowing for simple MAP inference using backpropagation to learn the parameters, as well as MCMC-based inference which can fully propagate uncertainty. We demonstrate our approach on illustrative toy datasets, as well as on a challenging problem of predicting age from images.
Diversity regularization in deep ensembles
Authors: Changjian Shui, Azadeh Sadat Mozafari, Jonathan Marek, Ihsen Hedhli, Christian Gagné
Date: 2018-02-22 00:00:00
Tasks: Diversity, Prediction

Calibrating the confidence of supervised learning models is important for a
variety of contexts where the certainty over predictions should be reliable.
However, it has been reported that deep neural network models are often too
poorly calibrated for achieving complex tasks requiring reliable uncertainty
estimates in their prediction. In this work, we are proposing a strategy for
training deep ensembles with a diversity function regularization, which
improves the calibration property while maintaining a similar prediction
accuracy.
Complex-valued Neural Networks with Non-parametric Activation Functions
Authors: Simone Scardapane, Steven Van Vaerenbergh, Amir Hussain, Aurelio Uncini
Date: 2018-02-22 00:00:00

Complex-valued neural networks (CVNNs) are a powerful modeling tool for
domains where data can be naturally interpreted in terms of complex numbers.
However, several analytical properties of the complex domain (e.g.,
holomorphicity) make the design of CVNNs a more challenging task than their
real counterpart. In this paper, we consider the problem of flexible activation
functions (AFs) in the complex domain, i.e., AFs endowed with sufficient
degrees of freedom to adapt their shape given the training data. While this
problem has received considerable attention in the real case, a very limited
literature exists for CVNNs, where most activation functions are generally
developed in a split fashion (i.e., by considering the real and imaginary parts
of the activation separately) or with simple phase-amplitude techniques.
Leveraging over the recently proposed kernel activation functions (KAFs), and
related advances in the design of complex-valued kernels, we propose the first
fully complex, non-parametric activation function for CVNNs, which is based on
a kernel expansion with a fixed dictionary that can be implemented efficiently
on vectorized hardware. Several experiments on common use cases, including
prediction and channel equalization, validate our proposal when compared to
real-valued neural networks and CVNNs with fixed activation functions.
SparCML: High-Performance Sparse Communication for Machine Learning
Authors: Cedric Renggli, Saleh Ashkboos, Mehdi Aghagolzadeh, Dan Alistarh, Torsten Hoefler
Date: 2018-02-22 00:00:00
Tasks: BIG-bench Machine Learning, Blocking, Vocal Bursts Intensity Prediction

Applying machine learning techniques to the quickly growing data in science and industry requires highly-scalable algorithms. Large datasets are most commonly processed "data parallel" distributed across many nodes. Each node's contribution to the overall gradient is summed using a global allreduce. This allreduce is the single communication and thus scalability bottleneck for most machine learning workloads. We observe that frequently, many gradient values are (close to) zero, leading to sparse of sparsifyable communications. To exploit this insight, we analyze, design, and implement a set of communication-efficient protocols for sparse input data, in conjunction with efficient machine learning algorithms which can leverage these primitives. Our communication protocols generalize standard collective operations, by allowing processes to contribute arbitrary sparse input data vectors. Our generic communication library, SparCML, extends MPI to support additional features, such as non-blocking (asynchronous) operations and low-precision data representations. As such, SparCML and its techniques will form the basis of future highly-scalable machine learning frameworks.
The problem of the development ontology-driven architecture of intellectual software systems
Authors: A. V. Palagin, N. G. Petrenko, V. Yu. Velychko, K. S. Malakhov
Date: 2018-02-17 00:00:00
Tasks: Management

The paper describes the architecture of the intelligence system for automated
design of ontological knowledge bases of domain areas and the software model of
the management GUI (Graphical User Interface) subsystem
An Unsupervised Method for Estimating the Global Horizontal Irradiance from Photovoltaic Power Measurements
Authors: Lorenzo Nespoli, Vasco Medici
Date: 2017-06-21 00:00:00
Tasks: valid

In this paper, we present a method to determine the global horizontal
irradiance (GHI) from the power measurements of one or more PV systems, located
in the same neighborhood. The method is completely unsupervised and is based on
a physical model of a PV plant. The precise assessment of solar irradiance is
pivotal for the forecast of the electric power generated by photovoltaic (PV)
plants. However, on-ground measurements are expensive and are generally not
performed for small and medium-sized PV plants. Satellite-based services
represent a valid alternative to on site measurements, but their space-time
resolution is limited. Results from two case studies located in Switzerland are
presented. The performance of the proposed method at assessing GHI is compared
with that of free and commercial satellite services. Our results show that the
presented method is generally better than satellite-based services, especially
at high temporal resolutions.
Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks
Authors: Daniel Tanneberg, Jan Peters, Elmar Rueckert
Date: 2018-02-22 00:00:00
Tasks: Lifelong learning, Motion Planning

Autonomous robots need to interact with unknown, unstructured and changing
environments, constantly facing novel challenges. Therefore, continuous online
adaptation for lifelong-learning and the need of sample-efficient mechanisms to
adapt to changes in the environment, the constraints, the tasks, or the robot
itself are crucial. In this work, we propose a novel framework for
probabilistic online motion planning with online adaptation based on a
bio-inspired stochastic recurrent neural network. By using learning signals
which mimic the intrinsic motivation signalcognitive dissonance in addition
with a mental replay strategy to intensify experiences, the stochastic
recurrent network can learn from few physical interactions and adapts to novel
environments in seconds. We evaluate our online planning and adaptation
framework on an anthropomorphic KUKA LWR arm. The rapid online adaptation is
shown by learning unknown workspace constraints sample-efficiently from few
physical interactions while following given way points.
Towards an Understanding of Entity-Oriented Search Intents
Authors: Darío Garigliotti, Krisztian Balog
Date: 2018-02-22 00:00:00

Entity-oriented search deals with a wide variety of information needs, from
displaying direct answers to interacting with services. In this work, we aim to
understand what are prominent entity-oriented search intents and how they can
be fulfilled. We develop a scheme of entity intent categories, and use them to
annotate a sample of queries. Specifically, we annotate unique query refiners
on the level of entity types. We observe that, on average, over half of those
refiners seek to interact with a service, while over a quarter of the refiners
search for information that may be looked up in a knowledge base.
Iterate averaging as regularization for stochastic gradient descent
Authors: Gergely Neu, Lorenzo Rosasco
Date: 2018-02-22 00:00:00
Tasks: regression

We propose and analyze a variant of the classic Polyak-Ruppert averaging
scheme, broadly used in stochastic gradient methods. Rather than a uniform
average of the iterates, we consider a weighted average, with weights decaying
in a geometric fashion. In the context of linear least squares regression, we
show that this averaging scheme has a the same regularizing effect, and indeed
is asymptotically equivalent, to ridge regression. In particular, we derive
finite-sample bounds for the proposed approach that match the best known
results for regularized stochastic gradient methods.
Sounderfeit: Cloning a Physical Model with Conditional Adversarial Autoencoders
Authors: Stephen Sinclair
Date: 2018-02-22 00:00:00
Tasks: parameter estimation, Resynthesis

An adversarial autoencoder conditioned on known parameters of a physical
modeling bowed string synthesizer is evaluated for use in parameter estimation
and resynthesis tasks. Latent dimensions are provided to capture variance not
explained by the conditional parameters. Results are compared with and without
the adversarial training, and a system capable of "copying" a given
parameter-signal bidirectional relationship is examined. A real-time synthesis
system built on a generative, conditioned and regularized neural network is
presented, allowing to construct engaging sound synthesizers based purely on
recorded data.
End-to-end Audiovisual Speech Recognition
Authors: Stavros Petridis, Themos Stafylakis, Pingchuan Ma, Feipeng Cai, Georgios Tzimiropoulos, Maja Pantic
Date: 2018-02-18 00:00:00
Proceeding: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2018 9
Tasks: Lipreading, speech-recognition, Speech Recognition

Several end-to-end deep learning approaches have been recently presented
which extract either audio or visual features from the input images or audio
signals and perform speech recognition. However, research on end-to-end
audiovisual models is very limited. In this work, we present an end-to-end
audiovisual model based on residual networks and Bidirectional Gated Recurrent
Units (BGRUs). To the best of our knowledge, this is the first audiovisual
fusion model which simultaneously learns to extract features directly from the
image pixels and audio waveforms and performs within-context word recognition
on a large publicly available dataset (LRW). The model consists of two streams,
one for each modality, which extract features directly from mouth regions and
raw waveforms. The temporal dynamics in each stream/modality are modeled by a
2-layer BGRU and the fusion of multiple streams/modalities takes place via
another 2-layer BGRU. A slight improvement in the classification rate over an
end-to-end audio-only and MFCC-based model is reported in clean audio
conditions and low levels of noise. In presence of high levels of noise, the
end-to-end audiovisual model significantly outperforms both audio-only models.
Generating High-Quality Query Suggestion Candidates for Task-Based Search
Authors: Heng Ding, Shuo Zhang, Darío Garigliotti, Krisztian Balog
Date: 2018-02-22 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We address the task of generating query suggestions for task-based search.
The current state of the art relies heavily on suggestions provided by a major
search engine. In this paper, we solve the task without reliance on search
engines. Specifically, we focus on the first step of a two-stage pipeline
approach, which is dedicated to the generation of query suggestion candidates.
We present three methods for generating candidate suggestions and apply them on
multiple information sources. Using a purpose-built test collection, we find
that these methods are able to generate high-quality suggestion candidates.
Persistence Codebooks for Topological Data Analysis
Authors: Bartosz Zielinski, Michal Lipinski, Mateusz Juda, Matthias Zeppelzauer, Pawel Dlotko
Date: 2018-02-13 00:00:00
Tasks: BIG-bench Machine Learning, Quantization, Topological Data Analysis

Persistent homology (PH) is a rigorous mathematical theory that provides a robust descriptor of data in the form of persistence diagrams (PDs) which are 2D multisets of points. Their variable size makes them, however, difficult to combine with typical machine learning workflows. In this paper we introduce persistence codebooks, a novel expressive and discriminative fixed-size vectorized representation of PDs. To this end, we adapt bag-of-words (BoW), vectors of locally aggregated descriptors (VLAD) and Fischer vectors (FV) for the quantization of PDs. Persistence codebooks represent PDs in a convenient way for machine learning and statistical analysis and have a number of favorable practical and theoretical properties including 1-Wasserstein stability. We evaluate the presented representations on several heterogeneous datasets and show their (high) discriminative power. Our approach achieves state-of-the-art performance and beyond in much less time than alternative approaches.
Learning to Route with Sparse Trajectory Sets---Extended Version
Authors: Chenjuan Guo, Bin Yang, Jilin Hu, Christian S. Jensen
Date: 2018-02-22 00:00:00
Tasks: Clustering

Motivated by the increasing availability of vehicle trajectory data, we
propose learn-to-route, a comprehensive trajectory-based routing solution.
Specifically, we first construct a graph-like structure from trajectories as
the routing infrastructure. Second, we enable trajectory-based routing given an
arbitrary (source, destination) pair.
  In the first step, given a road network and a collection of trajectories, we
propose a trajectory-based clustering method that identifies regions in a road
network. If a pair of regions are connected by trajectories, we maintain the
paths used by these trajectories and learn a routing preference for travel
between the regions. As trajectories are skewed and sparse, many region pairs
are not connected by trajectories. We thus transfer routing preferences from
region pairs with sufficient trajectories to such region pairs and then use the
transferred preferences to identify paths between the regions. In the second
step, we exploit the above graph-like structure to achieve a comprehensive
trajectory-based routing solution. Empirical studies with two substantial
trajectory data sets offer insight into the proposed solution, indicating that
it is practical. A comparison with a leading routing service offers evidence
that the paper's proposal is able to enhance routing quality.
  This is an extended version of "Learning to Route with Sparse Trajectory
Sets" [1], to appear in IEEE ICDE 2018.
Robustness of classifiers to uniform $\ell\_p$ and Gaussian noise
Authors: Jean-Yves Franceschi, Alhussein Fawzi, Omar Fawzi
Date: 2018-02-22 00:00:00

We study the robustness of classifiers to various kinds of random noise
models. In particular, we consider noise drawn uniformly from the $\ell\_p$
ball for $p \in [1, \infty]$ and Gaussian noise with an arbitrary covariance
matrix. We characterize this robustness to random noise in terms of the
distance to the decision boundary of the classifier. This analysis applies to
linear classifiers as well as classifiers with locally approximately flat
decision boundaries, a condition which is satisfied by state-of-the-art deep
neural networks. The predicted robustness is verified experimentally.
Non-rigid Object Tracking via Deep Multi-scale Spatial-temporal Discriminative Saliency Maps
Authors: Pingping Zhang, Wei Liu, Dong Wang, Yinjie Lei, Hongyu Wang, Chunhua Shen, Huchuan Lu
Date: 2018-02-22 00:00:00
Tasks: Object, Object Tracking, Saliency Detection, Visual Tracking

In this paper, we propose a novel effective non-rigid object tracking
framework based on the spatial-temporal consistent saliency detection. In
contrast to most existing trackers that utilize a bounding box to specify the
tracked target, the proposed framework can extract accurate regions of the
target as tracking outputs. It achieves a better description of the non-rigid
objects and reduces the background pollution for the tracking model.
Furthermore, our model has several unique features. First, a tailored fully
convolutional neural network (TFCN) is developed to model the local saliency
prior for a given image region, which not only provides the pixel-wise outputs
but also integrates the semantic information. Second, a novel multi-scale
multi-region mechanism is proposed to generate local saliency maps that
effectively consider visual perceptions with different spatial layouts and
scale variations. Subsequently, local saliency maps are fused via a weighted
entropy method, resulting in a final discriminative saliency map. Finally, we
present a non-rigid object tracking algorithm based on the predicted saliency
maps. By utilizing a spatial-temporal consistent saliency map (STCSM), we
conduct target-background classification and use a simple fine-tuning scheme
for online updating. Extensive experiments demonstrate that the proposed
algorithm achieves competitive performance in both saliency detection and
visual tracking, especially outperforming other related trackers on the
non-rigid object tracking datasets.
Stereo obstacle detection for unmanned surface vehicles by IMU-assisted semantic segmentation
Authors: Borja Bovcon, Rok Mandeljc, Janez Perš, Matej Kristan
Date: 2018-02-22 00:00:00
Tasks: Edge Detection, Segmentation, Semantic Segmentation

A new obstacle detection algorithm for unmanned surface vehicles (USVs) is
presented. A state-of-the-art graphical model for semantic segmentation is
extended to incorporate boat pitch and roll measurements from the on-board
inertial measurement unit (IMU), and a stereo verification algorithm that
consolidates tentative detections obtained from the segmentation is proposed.
The IMU readings are used to estimate the location of horizon line in the
image, which automatically adjusts the priors in the probabilistic semantic
segmentation model. We derive the equations for projecting the horizon into
images, propose an efficient optimization algorithm for the extended graphical
model, and offer a practical IMU-camera-USV calibration procedure. Using an USV
equipped with multiple synchronized sensors, we captured a new challenging
multi-modal dataset, and annotated its images with water edge and obstacles.
Experimental results show that the proposed algorithm significantly outperforms
the state of the art, with nearly 30% improvement in water-edge detection
accuracy, an over 21% reduction of false positive rate, an almost 60% reduction
of false negative rate, and an over 65% increase of true positive rate, while
its Matlab implementation runs in real-time.
The State of the Art in Integrating Machine Learning into Visual Analytics
Authors: A. Endert, W. Ribarsky, C. Turkay, W Wong, I. Nabney, I Díaz Blanco, Fabrice Rossi
Date: 2018-02-22 00:00:00
Tasks: BIG-bench Machine Learning, Data Visualization

Visual analytics systems combine machine learning or other analytic
techniques with interactive data visualization to promote sensemaking and
analytical reasoning. It is through such techniques that people can make sense
of large, complex data. While progress has been made, the tactful combination
of machine learning and data visualization is still under-explored. This
state-of-the-art report presents a summary of the progress that has been made
by highlighting and synthesizing select research advances. Further, it presents
opportunities and challenges to enhance the synergy between machine learning
and visual analytics for impactful future research directions.