pwc-archive/papers-with-abstracts huggingface dataset:
How to avoid ethically relevant Machine Consciousness
Authors: Aleksander Lodwich
Date: 2016-05-31 00:00:00

This paper discusses the root cause of systems perceiving the self experience
and how to exploit adaptive and learning features without introducing ethically
problematic system properties.
Pushing the Limits of Deep CNNs for Pedestrian Detection
Authors: Qichang Hu, Peng Wang, Chunhua Shen, Anton Van Den Hengel, Fatih Porikli
Date: 2016-03-15 00:00:00
Tasks: Occlusion Handling, Optical Flow Estimation, Pedestrian Detection

Compared to other applications in computer vision, convolutional neural
networks have under-performed on pedestrian detection. A breakthrough was made
very recently by using sophisticated deep CNN models, with a number of
hand-crafted features, or explicit occlusion handling mechanism. In this work,
we show that by re-using the convolutional feature maps (CFMs) of a deep
convolutional neural network (DCNN) model as image features to train an
ensemble of boosted decision models, we are able to achieve the best reported
accuracy without using specially designed learning algorithms. We empirically
identify and disclose important implementation details. We also show that pixel
labelling may be simply combined with a detector to boost the detection
performance. By adding complementary hand-crafted features such as optical
flow, the DCNN based detector can be further improved. We set a new record on
the Caltech pedestrian dataset, lowering the log-average miss rate from
$11.7\%$ to $8.9\%$, a relative improvement of $24\%$. We also achieve a
comparable result to the state-of-the-art approaches on the KITTI dataset.
shapeDTW: shape Dynamic Time Warping
Authors: Jiaping Zhao, Laurent Itti
Date: 2016-06-06 00:00:00
Tasks: Dynamic Time Warping, Temporal Sequences, Time Series, Time Series Analysis

Dynamic Time Warping (DTW) is an algorithm to align temporal sequences with
possible local non-linear distortions, and has been widely applied to audio,
video and graphics data alignments. DTW is essentially a point-to-point
matching method under some boundary and temporal consistency constraints.
Although DTW obtains a global optimal solution, it does not necessarily achieve
locally sensible matchings. Concretely, two temporal points with entirely
dissimilar local structures may be matched by DTW. To address this problem, we
propose an improved alignment algorithm, named shape Dynamic Time Warping
(shapeDTW), which enhances DTW by taking point-wise local structural
information into consideration. shapeDTW is inherently a DTW algorithm, but
additionally attempts to pair locally similar structures and to avoid matching
points with distinct neighborhood structures. We apply shapeDTW to align audio
signal pairs having ground-truth alignments, as well as artificially simulated
pairs of aligned sequences, and obtain quantitatively much lower alignment
errors than DTW and its two variants. When shapeDTW is used as a distance
measure in a nearest neighbor classifier (NN-shapeDTW) to classify time series,
it beats DTW on 64 out of 84 UCR time series datasets, with significantly
improved classification accuracies. By using a properly designed local
structure descriptor, shapeDTW improves accuracies by more than 10% on 18
datasets. To the best of our knowledge, shapeDTW is the first distance measure
under the nearest neighbor classifier scheme to significantly outperform DTW,
which had been widely recognized as the best distance measure to date. Our code
is publicly accessible at: https://github.com/jiapingz/shapeDTW.
Deep Linear Discriminant Analysis on Fisher Networks: A Hybrid Architecture for Person Re-identification
Authors: Lin Wu, Chunhua Shen, Anton Van Den Hengel
Date: 2016-06-06 00:00:00
Tasks: Person Re-Identification

Person re-identification is to seek a correct match for a person of interest
across views among a large number of imposters. It typically involves two
procedures of non-linear feature extractions against dramatic appearance
changes, and subsequent discriminative analysis in order to reduce intra-
personal variations while enlarging inter-personal differences. In this paper,
we introduce a hybrid architecture which combines Fisher vectors and deep
neural networks to learn non-linear representations of person images to a space
where data can be linearly separable. We reinforce a Linear Discriminant
Analysis (LDA) on top of the deep neural network such that linearly separable
latent representations can be learnt in an end-to-end fashion. By optimizing an
objective function modified from LDA, the network is enforced to produce
feature distributions which have a low variance within the same class and high
variance between classes. The objective is essentially derived from the general
LDA eigenvalue problem and allows to train the network with stochastic gradient
descent and back-propagate LDA gradients to compute the gradients involved in
Fisher vector encoding. For evaluation we test our approach on four benchmark
data sets in person re-identification (VIPeR [1], CUHK03 [2], CUHK01 [3], and
Market1501 [4]). Extensive experiments on these benchmarks show that our model
can achieve state-of-the-art results.
Efficient piecewise training of deep structured models for semantic segmentation
Authors: Guosheng Lin, Chunhua Shen, Anton van dan Hengel, Ian Reid
Date: 2015-04-04 00:00:00
Proceeding: CVPR 2016 6
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Recent advances in semantic image segmentation have mostly been achieved by
training deep convolutional neural networks (CNNs). We show how to improve
semantic segmentation through the use of contextual information; specifically,
we explore `patch-patch' context between image regions, and `patch-background'
context. For learning from the patch-patch context, we formulate Conditional
Random Fields (CRFs) with CNN-based pairwise potential functions to capture
semantic correlations between neighboring patches. Efficient piecewise training
of the proposed deep structured model is then applied to avoid repeated
expensive CRF inference for back propagation. For capturing the
patch-background context, we show that a network design with traditional
multi-scale image input and sliding pyramid pooling is effective for improving
performance. Our experimental results set new state-of-the-art performance on a
number of popular semantic segmentation datasets, including NYUDv2, PASCAL VOC
2012, PASCAL-Context, and SIFT-flow. In particular, we achieve an
intersection-over-union score of 78.0 on the challenging PASCAL VOC 2012
dataset.
A Deep-Learning Approach for Operation of an Automated Realtime Flare Forecast
Authors: Yuko Hada-Muranushi, Takayuki Muranushi, Ayumi Asai, Daisuke Okanohara, Rudy Raymond, Gentaro Watanabe, Shigeru Nemoto, Kazunari Shibata
Date: 2016-06-06 00:00:00

Automated forecasts serve important role in space weather science, by
providing statistical insights to flare-trigger mechanisms, and by enabling
tailor-made forecasts and high-frequency forecasts. Only by realtime forecast
we can experimentally measure the performance of flare-forecasting methods
while confidently avoiding overlearning.
  We have been operating unmanned flare forecast service since August, 2015
that provides 24-hour-ahead forecast of solar flares, every 12 minutes. We
report the method and prediction results of the system.
DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks
Authors: Martin Rajchl, Matthew C. H. Lee, Ozan Oktay, Konstantinos Kamnitsas, Jonathan Passerat-Palmbach, Wenjia Bai, Mellisa Damodaram, Mary A. Rutherford, Joseph V. Hajnal, Bernhard Kainz, Daniel Rueckert
Date: 2016-05-25 00:00:00
Tasks: Object, Semantic Segmentation

In this paper, we propose DeepCut, a method to obtain pixelwise object
segmentations given an image dataset labelled with bounding box annotations. It
extends the approach of the well-known GrabCut method to include machine
learning by training a neural network classifier from bounding box annotations.
We formulate the problem as an energy minimisation problem over a
densely-connected conditional random field and iteratively update the training
targets to obtain pixelwise object segmentations. Additionally, we propose
variants of the DeepCut method and compare those to a naive approach to CNN
training under weak supervision. We test its applicability to solve brain and
lung segmentation problems on a challenging fetal magnetic resonance dataset
and obtain encouraging results in terms of accuracy.
Shallow Networks for High-Accuracy Road Object-Detection
Authors: Khalid Ashraf, Bichen Wu, Forrest N. Iandola, Mattthew W. Moskewicz, Kurt Keutzer
Date: 2016-06-05 00:00:00
Tasks: Autonomous Vehicles, Object, object-detection, Object Detection, Vocal Bursts Intensity Prediction

The ability to automatically detect other vehicles on the road is vital to
the safety of partially-autonomous and fully-autonomous vehicles. Most of the
high-accuracy techniques for this task are based on R-CNN or one of its faster
variants. In the research community, much emphasis has been applied to using 3D
vision or complex R-CNN variants to achieve higher accuracy. However, are there
more straightforward modifications that could deliver higher accuracy? Yes. We
show that increasing input image resolution (i.e. upsampling) offers up to 12
percentage-points higher accuracy compared to an off-the-shelf baseline. We
also find situations where earlier/shallower layers of CNN provide higher
accuracy than later/deeper layers. We further show that shallow models and
upsampled images yield competitive accuracy. Our findings contrast with the
current trend towards deeper and larger models to achieve high accuracy in
domain specific detection tasks.
Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators
Authors: Shashank Singh, Barnabás Póczos
Date: 2016-06-05 00:00:00
Proceeding: NeurIPS 2016 12

We provide finite-sample analysis of a general framework for using k-nearest
neighbor statistics to estimate functionals of a nonparametric continuous
probability density, including entropies and divergences. Rather than plugging
a consistent density estimate (which requires $k \to \infty$ as the sample size
$n \to \infty$) into the functional of interest, the estimators we consider fix
k and perform a bias correction. This is more efficient computationally, and,
as we show in certain cases, statistically, leading to faster convergence
rates. Our framework unifies several previous estimators, for most of which
ours are the first finite sample guarantees.
View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation
Authors: Joel Z. Leibo, Qianli Liao, Winrich Freiwald, Fabio Anselmi, Tomaso Poggio
Date: 2016-06-05 00:00:00
Tasks: Face Recognition, Object, Object Recognition

The primate brain contains a hierarchy of visual areas, dubbed the ventral
stream, which rapidly computes object representations that are both specific
for object identity and relatively robust against identity-preserving
transformations like depth-rotations. Current computational models of object
recognition, including recent deep learning networks, generate these properties
through a hierarchy of alternating selectivity-increasing filtering and
tolerance-increasing pooling operations, similar to simple-complex cells
operations. While simulations of these models recapitulate the ventral stream's
progression from early view-specific to late view-tolerant representations,
they fail to generate the most salient property of the intermediate
representation for faces found in the brain: mirror-symmetric tuning of the
neural population to head orientation. Here we prove that a class of
hierarchical architectures and a broad set of biologically plausible learning
rules can provide approximate invariance at the top level of the network. While
most of the learning rules do not yield mirror-symmetry in the mid-level
representations, we characterize a specific biologically-plausible Hebb-type
learning rule that is guaranteed to generate mirror-symmetric tuning to faces
tuning at intermediate levels of the architecture.
Pairwise Quantization
Authors: Artem Babenko, Relja Arandjelović, Victor Lempitsky
Date: 2016-06-05 00:00:00
Tasks: Quantization

We consider the task of lossy compression of high-dimensional vectors through
quantization. We propose the approach that learns quantization parameters by
minimizing the distortion of scalar products and squared distances between
pairs of points. This is in contrast to previous works that obtain these
parameters through the minimization of the reconstruction error of individual
points. The proposed approach proceeds by finding a linear transformation of
the data that effectively reduces the minimization of the pairwise distortions
to the minimization of individual reconstruction errors. After such
transformation, any of the previously-proposed quantization approaches can be
used. Despite the simplicity of this transformation, the experiments
demonstrate that it achieves considerable reduction of the pairwise distortions
compared to applying quantization directly to the untransformed data.
OpenAI Gym
Authors: Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, Wojciech Zaremba
Date: 2016-06-05 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

OpenAI Gym is a toolkit for reinforcement learning research. It includes a
growing collection of benchmark problems that expose a common interface, and a
website where people can share their results and compare the performance of
algorithms. This whitepaper discusses the components of OpenAI Gym and the
design decisions that went into the software.
Total Variation Regularized Tensor RPCA for Background Subtraction from Compressive Measurements
Authors: Wenfei Cao, Yao Wang, Jian Sun, Deyu Meng, Can Yang, Andrzej Cichocki, Zongben Xu
Date: 2015-03-06 00:00:00

Background subtraction has been a fundamental and widely studied task in
video analysis, with a wide range of applications in video surveillance,
teleconferencing and 3D modeling. Recently, motivated by compressive imaging,
background subtraction from compressive measurements (BSCM) is becoming an
active research task in video surveillance. In this paper, we propose a novel
tensor-based robust PCA (TenRPCA) approach for BSCM by decomposing video frames
into backgrounds with spatial-temporal correlations and foregrounds with
spatio-temporal continuity in a tensor framework. In this approach, we use 3D
total variation (TV) to enhance the spatio-temporal continuity of foregrounds,
and Tucker decomposition to model the spatio-temporal correlations of video
background. Based on this idea, we design a basic tensor RPCA model over the
video frames, dubbed as the holistic TenRPCA model (H-TenRPCA). To characterize
the correlations among the groups of similar 3D patches of video background, we
further design a patch-group-based tensor RPCA model (PG-TenRPCA) by joint
tensor Tucker decompositions of 3D patch groups for modeling the video
background. Efficient algorithms using alternating direction method of
multipliers (ADMM) are developed to solve the proposed models. Extensive
experiments on simulated and real-world videos demonstrate the superiority of
the proposed approaches over the existing state-of-the-art approaches.
What is the Best Feature Learning Procedure in Hierarchical Recognition Architectures?
Authors: Kevin Jarrett, Koray Kvukcuoglu, Karol Gregor, Yann Lecun
Date: 2016-06-05 00:00:00
Tasks: Object Recognition, Unsupervised Pre-training

(This paper was written in November 2011 and never published. It is posted on
arXiv.org in its original form in June 2016). Many recent object recognition
systems have proposed using a two phase training procedure to learn sparse
convolutional feature hierarchies: unsupervised pre-training followed by
supervised fine-tuning. Recent results suggest that these methods provide
little improvement over purely supervised systems when the appropriate
nonlinearities are included. This paper presents an empirical exploration of
the space of learning procedures for sparse convolutional networks to assess
which method produces the best performance. In our study, we introduce an
augmentation of the Predictive Sparse Decomposition method that includes a
discriminative term (DPSD). We also introduce a new single phase supervised
learning procedure that places an L1 penalty on the output state of each layer
of the network. This forces the network to produce sparse codes without the
expensive pre-training phase. Using DPSD with a new, complex predictor that
incorporates lateral inhibition, combined with multi-scale feature pooling, and
supervised refinement, the system achieves a 70.6\% recognition rate on
Caltech-101. With the addition of convolutional training, a 77\% recognition
was obtained on the CIfAR-10 dataset.
Gaussian Process Regression for Out-of-Sample Extension
Authors: Oren Barkan, Jonathan Weill, Amir Averbuch
Date: 2016-03-07 00:00:00
Tasks: regression

Manifold learning methods are useful for high dimensional data analysis. Many
of the existing methods produce a low dimensional representation that attempts
to describe the intrinsic geometric structure of the original data. Typically,
this process is computationally expensive and the produced embedding is limited
to the training data. In many real life scenarios, the ability to produce
embedding of unseen samples is essential. In this paper we propose a Bayesian
non-parametric approach for out-of-sample extension. The method is based on
Gaussian Process Regression and independent of the manifold learning algorithm.
Additionally, the method naturally provides a measure for the degree of
abnormality for a newly arrived data point that did not participate in the
training process. We derive the mathematical connection between the proposed
method and the Nystrom extension and show that the latter is a special case of
the former. We present extensive experimental results that demonstrate the
performance of the proposed method and compare it to other existing
out-of-sample extension methods.
Adaptive Submodular Ranking and Routing
Authors: Fatemeh Navidi, Prabhanjan Kambadur, Viswanath Nagarajan
Date: 2016-06-05 00:00:00
Tasks: Active Learning

We study a general stochastic ranking problem where an algorithm needs to
adaptively select a sequence of elements so as to "cover" a random scenario
(drawn from a known distribution) at minimum expected cost. The coverage of
each scenario is captured by an individual submodular function, where the
scenario is said to be covered when its function value goes above a given
threshold. We obtain a logarithmic factor approximation algorithm for this
adaptive ranking problem, which is the best possible (unless P=NP). This
problem unifies and generalizes many previously studied problems with
applications in search ranking and active learning. The approximation ratio of
our algorithm either matches or improves the best result known in each of these
special cases. Furthermore, we extend our results to an adaptive vehicle
routing problem, where costs are determined by an underlying metric. This
routing problem is a significant generalization of the previously-studied
adaptive traveling salesman and traveling repairman problems. Our approximation
ratio nearly matches the best bound known for these special cases. Finally, we
present experimental results for some applications of adaptive ranking.
A Deep Learning Approach to Block-based Compressed Sensing of Images
Authors: Amir Adler, David Boublil, Michael Elad, Michael Zibulevsky
Date: 2016-06-05 00:00:00
Tasks: compressed sensing

Compressed sensing (CS) is a signal processing framework for efficiently
reconstructing a signal from a small number of measurements, obtained by linear
projections of the signal. Block-based CS is a lightweight CS approach that is
mostly suitable for processing very high-dimensional images and videos: it
operates on local patches, employs a low-complexity reconstruction operator and
requires significantly less memory to store the sensing matrix. In this paper
we present a deep learning approach for block-based CS, in which a
fully-connected network performs both the block-based linear sensing and
non-linear reconstruction stages. During the training phase, the sensing matrix
and the non-linear reconstruction operator are \emph{jointly} optimized, and
the proposed approach outperforms state-of-the-art both in terms of
reconstruction quality and computation time. For example, at a 25% sensing rate
the average PSNR advantage is 0.77dB and computation time is over 200-times
faster.
Mental State Recognition via Wearable EEG
Authors: Pouya Bashivan, Irina Rish, Steve Heisig
Date: 2016-02-02 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

The increasing quality and affordability of consumer electroencephalogram
(EEG) headsets make them attractive for situations where medical grade devices
are impractical. Predicting and tracking cognitive states is possible for tasks
that were previously not conducive to EEG monitoring. For instance, monitoring
operators for states inappropriate to the task (e.g. drowsy drivers), tracking
mental health (e.g. anxiety) and productivity (e.g. tiredness) are among
possible applications for the technology. Consumer grade EEG headsets are
affordable and relatively easy to use, but they lack the resolution and quality
of signal that can be achieved using medical grade EEG devices. Thus, the key
questions remain: to what extent are wearable EEG devices capable of mental
state recognition, and what kind of mental states can be accurately recognized
with these devices? In this work, we examined responses to two different types
of input: instructional (logical) versus recreational (emotional) videos, using
a range of machine-learning methods. We tried SVMs, sparse logistic regression,
and Deep Belief Networks, to discriminate between the states of mind induced by
different types of video input, that can be roughly labeled as logical vs.
emotional. Our results demonstrate a significant potential of wearable EEG
devices in differentiating cognitive states between situations with large
contextual but subtle apparent differences.
Generative Adversarial Text to Image Synthesis
Authors: Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, Honglak Lee
Date: 2016-05-17 00:00:00
Tasks: Adversarial Text, Image Generation, Text-to-Image Generation

Automatic synthesis of realistic images from text would be interesting and
useful, but current AI systems are still far from this goal. However, in recent
years generic and powerful recurrent neural network architectures have been
developed to learn discriminative text feature representations. Meanwhile, deep
convolutional generative adversarial networks (GANs) have begun to generate
highly compelling images of specific categories, such as faces, album covers,
and room interiors. In this work, we develop a novel deep architecture and GAN
formulation to effectively bridge these advances in text and image model- ing,
translating visual concepts from characters to pixels. We demonstrate the
capability of our model to generate plausible images of birds and flowers from
detailed text descriptions.
Bounds for Vector-Valued Function Estimation
Authors: Andreas Maurer, Massimiliano Pontil
Date: 2016-06-05 00:00:00
Tasks: Multi-Task Learning

We present a framework to derive risk bounds for vector-valued learning with
a broad class of feature maps and loss functions. Multi-task learning and
one-vs-all multi-category learning are treated as examples. We discuss in
detail vector-valued functions with one hidden layer, and demonstrate that the
conditions under which shared representations are beneficial for multi- task
learning are equally applicable to multi-category learning.