pwc-archive/papers-with-abstracts huggingface dataset:
Associative Memories to Accelerate Approximate Nearest Neighbor Search
Authors: Vincent Gripon, Matthias Löwe, Franck Vermet
Date: 2016-11-10 00:00:00
Tasks: Quantization, Retrieval

Nearest neighbor search is a very active field in machine learning for it
appears in many application cases, including classification and object
retrieval. In its canonical version, the complexity of the search is linear
with both the dimension and the cardinal of the collection of vectors the
search is performed in. Recently many works have focused on reducing the
dimension of vectors using quantization techniques or hashing, while providing
an approximate result. In this paper we focus instead on tackling the cardinal
of the collection of vectors. Namely, we introduce a technique that partitions
the collection of vectors and stores each part in its own associative memory.
When a query vector is given to the system, associative memories are polled to
identify which one contain the closest match. Then an exhaustive search is
conducted only on the part of vectors stored in the selected associative
memory. We study the effectiveness of the system when messages to store are
generated from i.i.d. uniform $\pm$1 random variables or 0-1 sparse i.i.d.
random variables. We also conduct experiment on both synthetic data and real
data and show it is possible to achieve interesting trade-offs between
complexity and accuracy.
Improving Content-Invariance in Gated Autoencoders for 2D and 3D Object Rotation
Authors: Stefan Lattner, Maarten Grachten
Date: 2017-07-05 00:00:00

Content-invariance in mapping codes learned by GAEs is a useful feature for
various relation learning tasks. In this paper we show that the
content-invariance of mapping codes for images of 2D and 3D rotated objects can
be substantially improved by extending the standard GAE loss (symmetric
reconstruction error) with a regularization term that penalizes the symmetric
cross-reconstruction error. This error term involves reconstruction of pairs
with mapping codes obtained from other pairs exhibiting similar
transformations. Although this would principally require knowledge of the
transformations exhibited by training pairs, our experiments show that a
bootstrapping approach can sidestep this issue, and that the regularization
term can effectively be used in an unsupervised setting.
Applying Deep Machine Learning for psycho-demographic profiling of Internet users using O.C.E.A.N. model of personality
Authors: Iaroslav Omelianenko
Date: 2017-03-07 00:00:00
Tasks: BIG-bench Machine Learning, Prediction

In the modern era, each Internet user leaves enormous amounts of auxiliary
digital residuals (footprints) by using a variety of on-line services. All this
data is already collected and stored for many years. In recent works, it was
demonstrated that it's possible to apply simple machine learning methods to
analyze collected digital footprints and to create psycho-demographic profiles
of individuals. However, while these works clearly demonstrated the
applicability of machine learning methods for such an analysis, created simple
prediction models still lacks accuracy necessary to be successfully applied for
practical needs. We have assumed that using advanced deep machine learning
methods may considerably increase the accuracy of predictions. We started with
simple machine learning methods to estimate basic prediction performance and
moved further by applying advanced methods based on shallow and deep neural
networks. Then we compared prediction power of studied models and made
conclusions about its performance. Finally, we made hypotheses how prediction
accuracy can be further improved. As result of this work, we provide full
source code used in the experiments for all interested researchers and
practitioners in corresponding GitHub repository. We believe that applying deep
machine learning for psycho-demographic profiling may have an enormous impact
on the society (for good or worse) and provides means for Artificial
Intelligence (AI) systems to better understand humans by creating their
psychological profiles. Thus AI agents may achieve the human-like ability to
participate in conversation (communication) flow by anticipating human
opponents' reactions, expectations, and behavior.
Generative diffeomorphic atlas construction from brain and spinal cord MRI data
Authors: Claudia Blaiotta, Patrick Freund, M. Jorge Cardoso, John Ashburner
Date: 2017-07-05 00:00:00

In this paper we will focus on the potential and on the challenges associated
with the development of an integrated brain and spinal cord modelling framework
for processing MR neuroimaging data. The aim of the work is to explore how a
hierarchical generative model of imaging data, which captures simultaneously
the distribution of signal intensities and the variability of anatomical shapes
across a large population of subjects, can serve to quantitatively investigate,
in vivo, the morphology of the central nervous system (CNS). In fact, the
generality of the proposed Bayesian approach, which extends the hierarchical
structure of the segmentation method implemented in the SPM software, allows
processing simultaneously information relative to different compartments of the
CNS, namely the brain and the spinal cord, without having to resort to organ
specific solutions (e.g. tools optimised only for the brain, or only for the
spinal cord), which are inevitably harder to integrate and generalise.
Efficient differentially private learning improves drug sensitivity prediction
Authors: Antti Honkela, Mrinal Das, Arttu Nieminen, Onur Dikmen, Samuel Kaski
Date: 2016-06-07 00:00:00
Tasks: Prediction, Sensitivity

Users of a personalised recommendation system face a dilemma: recommendations
can be improved by learning from data, but only if the other users are willing
to share their private information. Good personalised predictions are vitally
important in precision medicine, but genomic information on which the
predictions are based is also particularly sensitive, as it directly identifies
the patients and hence cannot easily be anonymised. Differential privacy has
emerged as a potentially promising solution: privacy is considered sufficient
if presence of individual patients cannot be distinguished. However,
differentially private learning with current methods does not improve
predictions with feasible data sizes and dimensionalities. Here we show that
useful predictors can be learned under powerful differential privacy
guarantees, and even from moderately-sized data sets, by demonstrating
significant improvements with a new robust private regression method in the
accuracy of private drug sensitivity prediction. The method combines two key
properties not present even in recent proposals, which can be generalised to
other predictors: we prove it is asymptotically consistently and efficiently
private, and demonstrate that it performs well on finite data. Good finite data
performance is achieved by limiting the sharing of private information by
decreasing the dimensionality and by projecting outliers to fit tighter bounds,
therefore needing to add less noise for equal privacy. As already the
simple-to-implement method shows promise on the challenging genomic data, we
anticipate rapid progress towards practical applications in many fields, such
as mobile sensing and social media, in addition to the badly needed precision
medicine solutions.
A dataset for Computer-Aided Detection of Pulmonary Embolism in CTA images
Authors: Mojtaba Masoudi, Hamid-Reza Pourreza, Mahdi Saadatmand Tarzjan, Fateme Shafiee Zargar, Masoud Pezeshki Rad, Noushin Eftekhari
Date: 2017-07-05 00:00:00

Todays, researchers in the field of Pulmonary Embolism (PE) analysis need to
use a publicly available dataset to assess and compare their methods. Different
systems have been designed for the detection of pulmonary embolism (PE), but
none of them have used any public datasets. All papers have used their own
private dataset. In order to fill this gap, we have collected 5160 slices of
computed tomography angiography (CTA) images acquired from 20 patients, and
after labeling the image by experts in this field, we provided a reliable
dataset which is now publicly available. In some situation, PE detection can be
difficult, for example when it occurs in the peripheral branches or when
patients have pulmonary diseases (such as parenchymal disease). Therefore, the
efficiency of CAD systems highly depends on the dataset. In the given dataset,
66% of PE are located in peripheral branches, and different pulmonary diseases
are also included.
Maximum Resilience of Artificial Neural Networks
Authors: Chih-Hong Cheng, Georg Nührenberg, Harald Ruess
Date: 2017-04-28 00:00:00

The deployment of Artificial Neural Networks (ANNs) in safety-critical
applications poses a number of new verification and certification challenges.
In particular, for ANN-enabled self-driving vehicles it is important to
establish properties about the resilience of ANNs to noisy or even maliciously
manipulated sensory input. We are addressing these challenges by defining
resilience properties of ANN-based classifiers as the maximal amount of input
or sensor perturbation which is still tolerated. This problem of computing
maximal perturbation bounds for ANNs is then reduced to solving mixed integer
optimization problems (MIP). A number of MIP encoding heuristics are developed
for drastically reducing MIP-solver runtimes, and using parallelization of
MIP-solvers results in an almost linear speed-up in the number (up to a certain
limit) of computing cores in our experiments. We demonstrate the effectiveness
and scalability of our approach by means of computing maximal resilience bounds
for a number of ANN benchmark sets ranging from typical image recognition
scenarios to the autonomous maneuvering of robots.
Automated Experiment Design for Data-Efficient Verification of Parametric Markov Decision Processes
Authors: Elizabeth Polgreen, Viraj Wijesuriya, Sofie Haesaert, Alessandro Abate
Date: 2017-07-05 00:00:00

We present a new method for statistical verification of quantitative
properties over a partially unknown system with actions, utilising a
parameterised model (in this work, a parametric Markov decision process) and
data collected from experiments performed on the underlying system. We obtain
the confidence that the underlying system satisfies a given property, and show
that the method uses data efficiently and thus is robust to the amount of data
available. These characteristics are achieved by firstly exploiting parameter
synthesis to establish a feasible set of parameters for which the underlying
system will satisfy the property; secondly, by actively synthesising
experiments to increase amount of information in the collected data that is
relevant to the property; and finally propagating this information over the
model parameters, obtaining a confidence that reflects our belief whether or
not the system parameters lie in the feasible set, thereby solving the
verification problem.
The Influence of Feature Representation of Text on the Performance of Document Classification
Authors: Sanda Martinčić-Ipšić, Tanja Miličić, Ljupčo Todorovski
Date: 2017-07-05 00:00:00
Tasks: Document Classification, General Classification

In this paper we perform a comparative analysis of three models for feature
representation of text documents in the context of document classification. In
particular, we consider the most often used family of models bag-of-words,
recently proposed continuous space models word2vec and doc2vec, and the model
based on the representation of text documents as language networks. While the
bag-of-word models have been extensively used for the document classification
task, the performance of the other two models for the same task have not been
well understood. This is especially true for the network-based model that have
been rarely considered for representation of text documents for classification.
In this study, we measure the performance of the document classifiers trained
using the method of random forests for features generated the three models and
their variants. The results of the empirical comparison show that the commonly
used bag-of-words model has performance comparable to the one obtained by the
emerging continuous-space model of doc2vec. In particular, the low-dimensional
variants of doc2vec generating up to 75 features are among the top-performing
document representation models. The results finally point out that doc2vec
shows a superior performance in the tasks of classifying large documents.
Robust Multi-Image HDR Reconstruction for the Modulo Camera
Authors: Florian Lang, Tobias Plötz, Stefan Roth
Date: 2017-07-05 00:00:00
Tasks: HDR Reconstruction

Photographing scenes with high dynamic range (HDR) poses great challenges to
consumer cameras with their limited sensor bit depth. To address this, Zhao et
al. recently proposed a novel sensor concept - the modulo camera - which
captures the least significant bits of the recorded scene instead of going into
saturation. Similar to conventional pipelines, HDR images can be reconstructed
from multiple exposures, but significantly fewer images are needed than with a
typical saturating sensor. While the concept is appealing, we show that the
original reconstruction approach assumes noise-free measurements and quickly
breaks down otherwise. To address this, we propose a novel reconstruction
algorithm that is robust to image noise and produces significantly fewer
artifacts. We theoretically analyze correctness as well as limitations, and
show that our approach significantly outperforms the baseline on real data.
Benchmarking Denoising Algorithms with Real Photographs
Authors: Tobias Plötz, Stefan Roth
Date: 2017-07-05 00:00:00
Proceeding: CVPR 2017 7
Tasks: Benchmarking, Denoising, Image Denoising

Lacking realistic ground truth data, image denoising techniques are
traditionally evaluated on images corrupted by synthesized i.i.d. Gaussian
noise. We aim to obviate this unrealistic setting by developing a methodology
for benchmarking denoising techniques on real photographs. We capture pairs of
images with different ISO values and appropriately adjusted exposure times,
where the nearly noise-free low-ISO image serves as reference. To derive the
ground truth, careful post-processing is needed. We correct spatial
misalignment, cope with inaccuracies in the exposure parameters through a
linear intensity transform based on a novel heteroscedastic Tobit regression
model, and remove residual low-frequency bias that stems, e.g., from minor
illumination changes. We then capture a novel benchmark dataset, the Darmstadt
Noise Dataset (DND), with consumer cameras of differing sensor sizes. One
interesting finding is that various recent techniques that perform well on
synthetic noise are clearly outperformed by BM3D on photographs with real
noise. Our benchmark delineates realistic evaluation scenarios that deviate
strongly from those commonly used in the scientific literature.
Fast Multi-frame Stereo Scene Flow with Motion Segmentation
Authors: Tatsunori Taniai, Sudipta N. Sinha, Yoichi Sato
Date: 2017-07-05 00:00:00
Proceeding: CVPR 2017 7
Tasks: CPU, Motion Segmentation, Optical Flow Estimation, Stereo Matching, Stereo Matching Hand, Visual Odometry

We propose a new multi-frame method for efficiently computing scene flow
(dense depth and optical flow) and camera ego-motion for a dynamic scene
observed from a moving stereo camera rig. Our technique also segments out
moving objects from the rigid scene. In our method, we first estimate the
disparity map and the 6-DOF camera motion using stereo matching and visual
odometry. We then identify regions inconsistent with the estimated camera
motion and compute per-pixel optical flow only at these regions. This flow
proposal is fused with the camera motion-based flow proposal using fusion moves
to obtain the final optical flow and motion segmentation. This unified
framework benefits all four tasks - stereo, optical flow, visual odometry and
motion segmentation leading to overall higher accuracy and efficiency. Our
method is currently ranked third on the KITTI 2015 scene flow benchmark.
Furthermore, our CPU implementation runs in 2-3 seconds per frame which is 1-3
orders of magnitude faster than the top six methods. We also report a thorough
evaluation on challenging Sintel sequences with fast camera and object motion,
where our method consistently outperforms OSF [Menze and Geiger, 2015], which
is currently ranked second on the KITTI benchmark.
R-PHOC: Segmentation-Free Word Spotting using CNN
Authors: Suman Ghosh, Ernest Valveny
Date: 2017-07-05 00:00:00
Tasks: Segmentation

This paper proposes a region based convolutional neural network for
segmentation-free word spotting. Our net- work takes as input an image and a
set of word candidate bound- ing boxes and embeds all bounding boxes into an
embedding space, where word spotting can be casted as a simple nearest
neighbour search between the query representation and each of the candidate
bounding boxes. We make use of PHOC embedding as it has previously achieved
significant success in segmentation- based word spotting. Word candidates are
generated using a simple procedure based on grouping connected components using
some spatial constraints. Experiments show that R-PHOC which operates on images
directly can improve the current state-of- the-art in the standard GW dataset
and performs as good as PHOCNET in some cases designed for segmentation based
word spotting.
Computer methods for 3D motion tracking in real-time
Authors: Bogusław Rymut
Date: 2017-07-05 00:00:00
Tasks: GPU, Pose Estimation

This thesis is devoted to marker-less 3D human motion tracking in calibrated
and synchronized multicamera systems. Pose estimation is based on a 3D model,
which is transformed into the image plane and then rendered. Owing to
elaborated techniques the tracking of the full body has been achieved in
real-time via dynamic optimization or dynamic Bayesian filtering. The objective
function of a particle swarm optimization algorithm and the observation model
of a particle filter are based on matching between the rendered 3D models in
the required poses and image features representing the extracted person. In
such an approach the main part of the computational overload is associated with
the rendering of 3D models in hypothetical poses as well as determination of
value of objective function. Effective methods for rendering of 3D models in
real-time with support of OpenGL as well as parallel methods for determining
the objective function on the GPU were developed. The elaborated solutions
permit 3D tracking of full body motion in real-time.
SADA: A General Framework to Support Robust Causation Discovery with Theoretical Guarantee
Authors: Ruichu Cai, Zhenjie Zhang, Zhifeng Hao
Date: 2017-07-05 00:00:00

Causation discovery without manipulation is considered a crucial problem to a
variety of applications. The state-of-the-art solutions are applicable only
when large numbers of samples are available or the problem domain is
sufficiently small. Motivated by the observations of the local sparsity
properties on causal structures, we propose a general Split-and-Merge
framework, named SADA, to enhance the scalability of a wide class of causation
discovery algorithms. In SADA, the variables are partitioned into subsets, by
finding causal cut on the sparse causal structure over the variables. By
running mainstream causation discovery algorithms as basic causal solvers on
the subproblems, complete causal structure can be reconstructed by combining
the partial results. SADA benefits from the recursive division technique, since
each small subproblem generates more accurate result under the same number of
samples. We theoretically prove that SADA always reduces the scales of problems
without sacrifice on accuracy, under the condition of local causal sparsity and
reliable conditional independence tests. We also present sufficient condition
to accuracy enhancement by SADA, even when the conditional independence tests
are vulnerable. Extensive experiments on both simulated and real-world datasets
verify the improvements on scalability and accuracy by applying SADA together
with existing causation discovery algorithms.
Échantillonnage de signaux sur graphes via des processus déterminantaux
Authors: Nicolas Tremblay, Simon Barthelme, Pierre-Olivier Amblard
Date: 2017-04-07 00:00:00
Tasks: Point Processes

We consider the problem of sampling k-bandlimited graph signals, ie, linear
combinations of the first k graph Fourier modes. We know that a set of k nodes
embedding all k-bandlimited signals always exists, thereby enabling their
perfect reconstruction after sampling. Unfortunately, to exhibit such a set,
one needs to partially diagonalize the graph Laplacian, which becomes
prohibitive at large scale. We propose a novel strategy based on determinantal
point processes that side-steps partial diagonalisation and enables
reconstruction with only O(k) samples. While doing so, we exhibit a new general
algorithm to sample determinantal process, faster than the state-of-the-art
algorithm by an order k.
Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management
Authors: Pei-Hao Su, Pawel Budzianowski, Stefan Ultes, Milica Gasic, Steve Young
Date: 2017-07-01 00:00:00
Proceeding: WS 2017 8
Tasks: Deep Reinforcement Learning, Dialogue Management, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning (RL) methods have significant potential for
dialogue policy optimisation. However, they suffer from a poor performance in
the early stages of learning. This is especially problematic for on-line
learning with real users. Two approaches are introduced to tackle this problem.
Firstly, to speed up the learning process, two sample-efficient neural networks
algorithms: trust region actor-critic with experience replay (TRACER) and
episodic natural actor-critic with experience replay (eNACER) are presented.
For TRACER, the trust region helps to control the learning step size and avoid
catastrophic model changes. For eNACER, the natural gradient identifies the
steepest ascent direction in policy space to speed up the convergence. Both
models employ off-policy learning with experience replay to improve
sample-efficiency. Secondly, to mitigate the cold start issue, a corpus of
demonstration data is utilised to pre-train the models prior to on-line
reinforcement learning. Combining these two approaches, we demonstrate a
practical approach to learn deep RL-based dialogue policies and demonstrate
their effectiveness in a task-oriented information seeking domain.
Graph Based Recommendations: From Data Representation to Feature Extraction and Application
Authors: Amit Tiroshi, Tsvi Kuflik, Shlomo Berkovsky, Mohamed Ali Kaafar
Date: 2017-07-05 00:00:00
Tasks: Recommendation Systems

Modeling users for the purpose of identifying their preferences and then
personalizing services on the basis of these models is a complex task,
primarily due to the need to take into consideration various explicit and
implicit signals, missing or uncertain information, contextual aspects, and
more. In this study, a novel generic approach for uncovering latent preference
patterns from user data is proposed and evaluated. The approach relies on
representing the data using graphs, and then systematically extracting
graph-based features and using them to enrich the original user models. The
extracted features encapsulate complex relationships between users, items, and
metadata. The enhanced user models can then serve as an input to any
recommendation algorithm. The proposed approach is domain-independent
(demonstrated on data from movies, music, and business recommender systems),
and is evaluated using several state-of-the-art machine learning methods, on
different recommendation tasks, and using different evaluation metrics. The
results show a unanimous improvement in the recommendation accuracy across
tasks and domains. In addition, the evaluation provides a deeper analysis
regarding the performance of the approach in special scenarios, including high
sparsity and variability of ratings.