pwc-archive/papers-with-abstracts huggingface dataset:
Causal Embeddings for Recommendation
Authors: Stephen Bonner, Flavian vasile
Date: 2017-06-23 00:00:00
Tasks: Domain Adaptation

Many current applications use recommendations in order to modify the natural
user behavior, such as to increase the number of sales or the time spent on a
website. This results in a gap between the final recommendation objective and
the classical setup where recommendation candidates are evaluated by their
coherence with past user behavior, by predicting either the missing entries in
the user-item matrix, or the most likely next event. To bridge this gap, we
optimize a recommendation policy for the task of increasing the desired outcome
versus the organic user behavior. We show this is equivalent to learning to
predict recommendation outcomes under a fully random recommendation policy. To
this end, we propose a new domain adaptation algorithm that learns from logged
data containing outcomes from a biased recommendation policy and predicts
recommendation outcomes according to random exposure. We compare our method
against state-of-the-art factorization methods and new approaches of causal
recommendation and show significant improvements.
Meta-learning autoencoders for few-shot prediction
Authors: Tailin Wu, John Peurifoy, Isaac L. Chuang, Max Tegmark
Date: 2018-07-26 00:00:00
Tasks: Meta-Learning, Prediction

Compared to humans, machine learning models generally require significantly
more training examples and fail to extrapolate from experience to solve
previously unseen challenges. To help close this performance gap, we augment
single-task neural networks with a meta-recognition model which learns a
succinct model code via its autoencoder structure, using just a few informative
examples. The model code is then employed by a meta-generative model to
construct parameters for the task-specific model. We demonstrate that for
previously unseen tasks, without additional training, this Meta-Learning
Autoencoder (MeLA) framework can build models that closely match the true
underlying models, with loss significantly lower than given by fine-tuned
baseline networks, and performance that compares favorably with
state-of-the-art meta-learning algorithms. MeLA also adds the ability to
identify influential training examples and predict which additional data will
be most valuable to acquire to improve model prediction.
BSAS: Beetle Swarm Antennae Search Algorithm for Optimization Problems
Authors: Jiangyu Wang, Huanxin Chen
Date: 2018-07-27 00:00:00
Tasks: Position

Beetle antennae search (BAS) is an efficient meta-heuristic algorithm.
However, the convergent results of BAS rely heavily on the random beetle
direction in every iterations. More specifically, different random seeds may
cause different optimized results. Besides, the step-size update algorithm of
BAS cannot guarantee objective become smaller in iterative process. In order to
solve these problems, this paper proposes Beetle Swarm Antennae Search
Algorithm (BSAS) which combines swarm intelligence algorithm with
feedback-based step-size update strategy. BSAS employs k beetles to find more
optimal position in each moving rather than one beetle. The step-size updates
only when k beetles return without better choices. Experiments are carried out
on building system identification. The results reveal the efficacy of the BSAS
algorithm to avoid influence of random direction of Beetle. In addition, the
estimation errors decrease as the beetles number goes up.
Attention-based Active Visual Search for Mobile Robots
Authors: Amir Rasouli, Pablo Lanillos, Gordon Cheng, John K. Tsotsos
Date: 2018-07-27 00:00:00
Tasks: Decision Making

We present an active visual search model for finding objects in unknown
environments. The proposed algorithm guides the robot towards the sought object
using the relevant stimuli provided by the visual sensors. Existing search
strategies are either purely reactive or use simplified sensor models that do
not exploit all the visual information available. In this paper, we propose a
new model that actively extracts visual information via visual attention
techniques and, in conjunction with a non-myopic decision-making algorithm,
leads the robot to search more relevant areas of the environment. The attention
module couples both top-down and bottom-up attention models enabling the robot
to search regions with higher importance first.
  The proposed algorithm is evaluated on a mobile robot platform in a 3D
simulated environment. The results indicate that the use of visual attention
significantly improves search, but the degree of improvement depends on the
nature of the task and the complexity of the environment. In our experiments,
we found that performance enhancements of up to 42\% in structured and 38\% in
highly unstructured cluttered environments can be achieved using visual
attention mechanisms.
A small Griko-Italian speech translation corpus
Authors: Marcely Zanon Boito, Antonios Anastasopoulos, Marika Lekakou, Aline Villavicencio, Laurent Besacier
Date: 2018-07-27 00:00:00
Tasks: Diversity, Translation

This paper presents an extension to a very low-resource parallel corpus
collected in an endangered language, Griko, making it useful for computational
research. The corpus consists of 330 utterances (about 20 minutes of speech)
which have been transcribed and translated in Italian, with annotations for
word-level speech-to-transcription and speech-to-translation alignments. The
corpus also includes morphosyntactic tags and word-level glosses. Applying an
automatic unit discovery method, pseudo-phones were also generated. We detail
how the corpus was collected, cleaned and processed, and we illustrate its use
on zero-resource tasks by presenting some baseline results for the task of
speech-to-translation alignment and unsupervised word discovery. The dataset is
available online, aiming to encourage replicability and diversity in
computational language documentation experiments.
An Algorithm for Learning Shape and Appearance Models without Annotations
Authors: John Ashburner, Mikael Brudfors, Kevin Bronik, Yael Balbastre
Date: 2018-07-27 00:00:00
Tasks: Image Registration, Privacy Preserving

This paper presents a framework for automatically learning shape and
appearance models for medical (and certain other) images. It is based on the
idea that having a more accurate shape and appearance model leads to more
accurate image registration, which in turn leads to a more accurate shape and
appearance model. This leads naturally to an iterative scheme, which is based
on a probabilistic generative model that is fit using Gauss-Newton updates
within an EM-like framework. It was developed with the aim of enabling
distributed privacy-preserving analysis of brain image data, such that shared
information (shape and appearance basis functions) may be passed across sites,
whereas latent variables that encode individual images remain secure within
each site. These latent variables are proposed as features for
privacy-preserving data mining applications.
  The approach is demonstrated qualitatively on the KDEF dataset of 2D face
images, showing that it can align images that traditionally require shape and
appearance models trained using manually annotated data (manually defined
landmarks etc.). It is applied to MNIST dataset of handwritten digits to show
its potential for machine learning applications, particularly when training
data is limited. The model is able to handle ``missing data'', which allows it
to be cross-validated according to how well it can predict left-out voxels. The
suitability of the derived features for classifying individuals into patient
groups was assessed by applying it to a dataset of over 1,900 segmented
T1-weighted MR images, which included images from the COBRE and ABIDE datasets.
Deep PDF: Probabilistic Surface Optimization and Density Estimation
Authors: Dmitry Kopitkov, Vadim Indelman
Date: 2018-07-27 00:00:00
Tasks: Decision Making, Density Estimation, Reinforcement Learning, Stochastic Optimization

A probability density function (pdf) encodes the entire stochastic knowledge
about data distribution, where data may represent stochastic observations in
robotics, transition state pairs in reinforcement learning or any other
empirically acquired modality. Inferring data pdf is of prime importance,
allowing to analyze various model hypotheses and perform smart decision making.
However, most density estimation techniques are limited in their representation
expressiveness to specific kernel type or predetermined distribution family,
and have other restrictions. For example, kernel density estimation (KDE)
methods require meticulous parameter search and are extremely slow at querying
new points. In this paper we present a novel non-parametric density estimation
approach, DeepPDF, that uses a neural network to approximate a target pdf given
samples from thereof. Such a representation provides high inference accuracy
for a wide range of target pdfs using a relatively simple network structure,
making our method highly statistically robust. This is done via a new
stochastic optimization algorithm, \emph{Probabilistic Surface Optimization}
(PSO), that turns to advantage the stochastic nature of sample points in order
to force network output to be identical to the output of a target pdf. Once
trained, query point evaluation can be efficiently done in DeepPDF by a simple
network forward pass, with linear complexity in the number of query points.
Moreover, the PSO algorithm is capable of inferring the frequency of data
samples and may also be used in other statistical tasks such as conditional
estimation and distribution transformation. We compare the derived approach
with KDE methods showing its superior performance and accuracy.
Few Shot Learning with Simplex
Authors: Bowen Zhang, Xifan Zhang, Fan Cheng, Deli Zhao
Date: 2018-07-27 00:00:00
Tasks: Few-Shot Learning

Deep learning has made remarkable achievement in many fields. However,
learning the parameters of neural networks usually demands a large amount of
labeled data. The algorithms of deep learning, therefore, encounter
difficulties when applied to supervised learning where only little data are
available. This specific task is called few-shot learning. To address it, we
propose a novel algorithm for few-shot learning using discrete geometry, in the
sense that the samples in a class are modeled as a reduced simplex. The volume
of the simplex is used for the measurement of class scatter. During testing,
combined with the test sample and the points in the class, a new simplex is
formed. Then the similarity between the test sample and the class can be
quantized with the ratio of volumes of the new simplex to the original class
simplex. Moreover, we present an approach to constructing simplices using local
regions of feature maps yielded by convolutional neural networks. Experiments
on Omniglot and miniImageNet verify the effectiveness of our simplex algorithm
on few-shot learning.
Semi-convolutional Operators for Instance Segmentation
Authors: David Novotny, Samuel Albanie, Diane Larlus, Andrea Vedaldi
Date: 2018-07-27 00:00:00
Proceeding: ECCV 2018 9
Tasks: Instance Segmentation, object-detection, Object Detection, Segmentation, Semantic Segmentation

Object detection and instance segmentation are dominated by region-based
methods such as Mask RCNN. However, there is a growing interest in reducing
these problems to pixel labeling tasks, as the latter could be more efficient,
could be integrated seamlessly in image-to-image network architectures as used
in many other tasks, and could be more accurate for objects that are not well
approximated by bounding boxes. In this paper we show theoretically and
empirically that constructing dense pixel embeddings that can separate object
instances cannot be easily achieved using convolutional operators. At the same
time, we show that simple modifications, which we call semi-convolutional, have
a much better chance of succeeding at this task. We use the latter to show a
connection to Hough voting as well as to a variant of the bilateral kernel that
is spatially steered by a convolutional network. We demonstrate that these
operators can also be used to improve approaches such as Mask RCNN,
demonstrating better segmentation of complex biological shapes and PASCAL VOC
categories than achievable by Mask RCNN alone.
Deep Learning Methods and Applications for Region of Interest Detection in Dermoscopic Images
Authors: Manu Goyal, Moi Hoon Yap, Saeed Hassanpour
Date: 2018-07-27 00:00:00
Tasks: Data Augmentation, object-detection, Object Detection, Object Localization, Segmentation

Rapid growth in the development of medical imaging analysis technology has been propelled by the great interest in improving computer-aided diagnosis and detection (CAD) systems for three popular image visualization tasks: classification, segmentation, and Region of Interest (ROI) detection. However, a limited number of datasets with ground truth annotations are available for developing segmentation and ROI detection of lesions, as expert annotations are laborious and expensive. Detecting the ROI is vital to locate lesions accurately. In this paper, we propose the use of two deep object detection meta-architectures (Faster R-CNN Inception-V2 and SSD Inception-V2) to develop robust ROI detection of skin lesions in dermoscopic datasets (2017 ISIC Challenge, PH2, and HAM10000), and compared the performance with state-of-the-art segmentation algorithm (DeeplabV3+). To further demonstrate the potential of our work, we built a smartphone application for real-time automated detection of skin lesions based on this methodology. In addition, we developed an automated natural data-augmentation method from ROI detection to produce augmented copies of dermoscopic images, as a pre-processing step in the segmentation of skin lesions to further improve the performance of the current state-of-the-art deep learning algorithm. Our proposed ROI detection has the potential to more appropriately streamline dermatology referrals and reduce unnecessary biopsies in the diagnosis of skin cancer.
End-to-end Deep Learning from Raw Sensor Data: Atrial Fibrillation Detection using Wearables
Authors: Igor Gotlibovych, Stuart Crawford, Dileep Goyal, Jiaqi Liu, Yaniv Kerem, David Benaron, Defne Yilmaz, Gregory Marcus, Yihan, Li
Date: 2018-07-27 00:00:00
Tasks: Atrial Fibrillation Detection, Feature Engineering, General Classification, Photoplethysmography (PPG)

We present a convolutional-recurrent neural network architecture with long
short-term memory for real-time processing and classification of digital sensor
data. The network implicitly performs typical signal processing tasks such as
filtering and peak detection, and learns time-resolved embeddings of the input
signal. We use a prototype multi-sensor wearable device to collect over 180h of
photoplethysmography (PPG) data sampled at 20Hz, of which 36h are during atrial
fibrillation (AFib). We use end-to-end learning to achieve state-of-the-art
results in detecting AFib from raw PPG data. For classification labels output
every 0.8s, we demonstrate an area under ROC curve of 0.9999, with false
positive and false negative rates both below $2\times 10^{-3}$. This
constitutes a significant improvement on previous results utilising
domain-specific feature engineering, such as heart rate extraction, and brings
large-scale atrial fibrillation screenings within imminent reach.
Diagnosing Error in Temporal Action Detectors
Authors: Humam Alwassel, Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanem
Date: 2018-07-27 00:00:00
Proceeding: ECCV 2018 9
Tasks: Action Localization, Diagnostic, Temporal Action Localization, Video Understanding

Despite the recent progress in video understanding and the continuous rate of
improvement in temporal action localization throughout the years, it is still
unclear how far (or close?) we are to solving the problem. To this end, we
introduce a new diagnostic tool to analyze the performance of temporal action
detectors in videos and compare different methods beyond a single scalar
metric. We exemplify the use of our tool by analyzing the performance of the
top rewarded entries in the latest ActivityNet action localization challenge.
Our analysis shows that the most impactful areas to work on are: strategies to
better handle temporal context around the instances, improving the robustness
w.r.t. the instance absolute and relative size, and strategies to reduce the
localization errors. Moreover, our experimental analysis finds the lack of
agreement among annotator is not a major roadblock to attain progress in the
field. Our diagnostic tool is publicly available to keep fueling the minds of
other researchers with additional insights about their algorithms.
Quantized Single-Ion-Channel Hodgkin-Huxley Model for Quantum Neurons
Authors: Tasio Gonzalez-Raya, Xiao-Hang Cheng, Iñigo L. Egusquiza, Xi Chen, Mikel Sanz, Enrique Solano
Date: 2018-07-27 00:00:00
Tasks: Quantum Machine Learning

The Hodgkin-Huxley model describes the behavior of the cell membrane in neurons, treating each part of it as an electric circuit element, namely capacitors, memristors, and voltage sources. We focus on the activation channel of potassium ions, due to its simplicity, while keeping most of the features displayed by the original model. This reduced version is essentially a classical memristor, a resistor whose resistance depends on the history of electric signals that have crossed it, coupled to a voltage source and a capacitor. Here, we will consider a quantized Hodgkin-Huxley model based on a quantum memristor formalism. We compare the behavior of the membrane voltage and the potassium channel conductance, when the circuit is subjected to AC sources, in both classical and quantum realms. Numerical simulations show an expected adaptation of the considered channel conductance depending on the signal history in all regimes. Remarkably, the computation of higher moments of the voltage manifest purely quantum features related to the circuit zero-point energy. Finally, we study the implementation of the Hodgkin-Huxley quantum memristor as an asymmetric rf SQUID in superconducting circuits. This study may allow the construction of quantum neuron networks inspired in the brain function, as well as the design of neuromorphic quantum architectures for quantum machine learning.
FPGA-Based CNN Inference Accelerator Synthesized from Multi-Threaded C Software
Authors: Jin Hee Kim, Brett Grady, Ruolong Lian, John Brothers, Jason H. Anderson
Date: 2018-07-27 00:00:00
Tasks: High-Level Synthesis

A deep-learning inference accelerator is synthesized from a C-language
software program parallelized with Pthreads. The software implementation uses
the well-known producer/consumer model with parallel threads interconnected by
FIFO queues. The LegUp high-level synthesis (HLS) tool synthesizes threads into
parallel FPGA hardware, translating software parallelism into spatial
parallelism. A complete system is generated where convolution, pooling and
padding are realized in the synthesized accelerator, with remaining tasks
executing on an embedded ARM processor. The accelerator incorporates reduced
precision, and a novel approach for zero-weight-skipping in convolution. On a
mid-sized Intel Arria 10 SoC FPGA, peak performance on VGG-16 is 138 effective
GOPS.
Infinite Mixture of Inverted Dirichlet Distributions
Authors: Zhanyu Ma, Yuping Lai
Date: 2018-07-27 00:00:00
Tasks: Variational Inference

In this work, we develop a novel Bayesian estimation method for the Dirichlet process (DP) mixture of the inverted Dirichlet distributions, which has been shown to be very flexible for modeling vectors with positive elements. The recently proposed extended variational inference (EVI) framework is adopted to derive an analytically tractable solution. The convergency of the proposed algorithm is theoretically guaranteed by introducing single lower bound approximation to the original objective function in the VI framework. In principle, the proposed model can be viewed as an infinite inverted Dirichelt mixture model (InIDMM) that allows the automatic determination of the number of mixture components from data. Therefore, the problem of pre-determining the optimal number of mixing components has been overcome. Moreover, the problems of over-fitting and under-fitting are avoided by the Bayesian estimation approach. Comparing with several recently proposed DP-related methods, the good performance and effectiveness of the proposed method have been demonstrated with both synthesized data and real data evaluations.
Learnable: Theory vs Applications
Authors: Marina Sapir
Date: 2018-07-27 00:00:00
Tasks: BIG-bench Machine Learning, Misconceptions, PAC learning

Two different views on machine learning problem: Applied learning (machine
learning with business applications) and Agnostic PAC learning are formalized
and compared here. I show that, under some conditions, the theory of PAC
Learnable provides a way to solve the Applied learning problem. However, the
theory requires to have the training sets so large, that it would make the
learning practically useless. I suggest shedding some theoretical
misconceptions about learning to make the theory more aligned with the needs
and experience of practitioners.
Combining Restricted Boltzmann Machines with Neural Networks for Latent Truth Discovery
Authors: Klaus Broelemann, Gjergji Kasneci
Date: 2018-07-27 00:00:00

Latent truth discovery, LTD for short, refers to the problem of aggregating
ltiple claims from various sources in order to estimate the plausibility of
atements about entities. In the absence of a ground truth, this problem is
highly challenging, when some sources provide conflicting claims and others no
claims at all. In this work we provide an unsupervised stochastic inference
procedure on top of a model that combines restricted Boltzmann machines with
feed-forward neural networks to accurately infer the reliability of sources as
well as the plausibility of statements about entities. In comparison to prior
work our approach stands out (1) by allowing the incorporation of arbitrary
features about sources and claims, (2) by generalizing from reliability per
source towards a reliability function, and thus (3) enabling the estimation of
source reliability even for sources that have provided no or very few claims,
(4) by building on efficient and scalable stochastic inference algorithms, and
(5) by outperforming the state-of-the-art by a considerable margin.
On the overfly algorithm in deep learning of neural networks
Authors: Alexei Tsygvintsev
Date: 2018-07-27 00:00:00
Tasks: Deep Learning

In this paper we investigate the supervised backpropagation training of
multilayer neural networks from a dynamical systems point of view. We discuss
some links with the qualitative theory of differential equations and introduce
the overfly algorithm to tackle the local minima problem. Our approach is based
on the existence of first integrals of the generalised gradient system with
build-in dissipation.
Concept Tagging for Natural Language Understanding: Two Decadelong Algorithm Development
Authors: Jacopo Gobbi, Evgeny Stepanov, Giuseppe Riccardi
Date: 2018-07-27 00:00:00
Tasks: Natural Language Understanding, Vocal Bursts Valence Prediction

Concept tagging is a type of structured learning needed for natural language
understanding (NLU) systems. In this task, meaning labels from a domain
ontology are assigned to word sequences. In this paper, we review the
algorithms developed over the last twenty five years. We perform a comparative
evaluation of generative, discriminative and deep learning methods on two
public datasets. We report on the statistical variability performance
measurements. The third contribution is the release of a repository of the
algorithms, datasets and recipes for NLU evaluation.