pwc-archive/papers-with-abstracts huggingface dataset:
Weaving Multi-scale Context for Single Shot Detector
Authors: Yunpeng Chen, Jianshu Li, Bin Zhou, Jiashi Feng, Shuicheng Yan
Date: 2017-12-08 00:00:00
Tasks: object-detection, Object Detection

Aggregating context information from multiple scales has been proved to be
effective for improving accuracy of Single Shot Detectors (SSDs) on object
detection. However, existing multi-scale context fusion techniques are
computationally expensive, which unfavorably diminishes the advantageous speed
of SSD. In this work, we propose a novel network topology, called WeaveNet,
that can efficiently fuse multi-scale information and boost the detection
accuracy with negligible extra cost. The proposed WeaveNet iteratively weaves
context information from adjacent scales together to enable more sophisticated
context reasoning while maintaining fast speed. Built by stacking light-weight
blocks, WeaveNet is easy to train without requiring batch normalization and can
be further accelerated by our proposed architecture simplification.
Experimental results on PASCAL VOC 2007, PASCAL VOC 2012 benchmarks show
signification performance boost brought by WeaveNet. For 320x320 input of batch
size = 8, WeaveNet reaches 79.5% mAP on PASCAL VOC 2007 test in 101 fps with
only 4 fps extra cost, and further improves to 79.7% mAP with more iterations.
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
Authors: Battista Biggio, Fabio Roli
Date: 2017-12-08 00:00:00
Tasks: BIG-bench Machine Learning, Misconceptions

Learning-based pattern classifiers, including deep networks, have shown
impressive performance in several application domains, ranging from computer
vision to cybersecurity. However, it has also been shown that adversarial input
perturbations carefully crafted either at training or at test time can easily
subvert their predictions. The vulnerability of machine learning to such wild
patterns (also referred to as adversarial examples), along with the design of
suitable countermeasures, have been investigated in the research field of
adversarial machine learning. In this work, we provide a thorough overview of
the evolution of this research area over the last ten years and beyond,
starting from pioneering, earlier work on the security of non-deep learning
algorithms up to more recent work aimed to understand the security properties
of deep learning algorithms, in the context of computer vision and
cybersecurity tasks. We report interesting connections between these
apparently-different lines of work, highlighting common misconceptions related
to the security evaluation of machine-learning algorithms. We review the main
threat models and attacks defined to this end, and discuss the main limitations
of current work, along with the corresponding future challenges towards the
design of more secure learning algorithms.
Building competitive direct acoustics-to-word models for English conversational speech recognition
Authors: Kartik Audhkhasi, Brian Kingsbury, Bhuvana Ramabhadran, George Saon, Michael Picheny
Date: 2017-12-08 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, English Conversational Speech Recognition, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Direct acoustics-to-word (A2W) models in the end-to-end paradigm have
received increasing attention compared to conventional sub-word based automatic
speech recognition models using phones, characters, or context-dependent hidden
Markov model states. This is because A2W models recognize words from speech
without any decoder, pronunciation lexicon, or externally-trained language
model, making training and decoding with such models simple. Prior work has
shown that A2W models require orders of magnitude more training data in order
to perform comparably to conventional models. Our work also showed this
accuracy gap when using the English Switchboard-Fisher data set. This paper
describes a recipe to train an A2W model that closes this gap and is at-par
with state-of-the-art sub-word based models. We achieve a word error rate of
8.8%/13.9% on the Hub5-2000 Switchboard/CallHome test sets without any decoder
or language model. We find that model initialization, training data order, and
regularization have the most impact on the A2W model performance. Next, we
present a joint word-character A2W model that learns to first spell the word
and then recognize it. This model provides a rich output to the user instead of
simple word hypotheses, making it especially useful in the case of words unseen
or rarely-seen during training.
A Class of Logistic Functions for Approximating State-Inclusive Koopman Operators
Authors: Charles A. Johnson, Enoch Yeung
Date: 2017-12-08 00:00:00

An outstanding challenge in nonlinear systems theory is identification or
learning of a given nonlinear system's Koopman operator directly from data or
models. Advances in extended dynamic mode decomposition approaches and machine
learning methods have enabled data-driven discovery of Koopman operators, for
both continuous and discrete-time systems. Since Koopman operators are often
infinite-dimensional, they are approximated in practice using
finite-dimensional systems. The fidelity and convergence of a given
finite-dimensional Koopman approximation is a subject of ongoing research. In
this paper we introduce a class of Koopman observable functions that confer an
approximate closure property on their corresponding finite-dimensional
approximations of the Koopman operator. We derive error bounds for the fidelity
of this class of observable functions, as well as identify two key learning
parameters which can be used to tune performance. We illustrate our approach on
two classical nonlinear system models: the Van Der Pol oscillator and the
bistable toggle switch.
Simultaneous Hand Pose and Skeleton Bone-Lengths Estimation from a Single Depth Image
Authors: Jameel Malik, Ahmed Elhayek, Didier Stricker
Date: 2017-12-08 00:00:00
Tasks: Hand Pose Estimation, Pose Estimation

Articulated hand pose estimation is a challenging task for human-computer
interaction. The state-of-the-art hand pose estimation algorithms work only
with one or a few subjects for which they have been calibrated or trained.
Particularly, the hybrid methods based on learning followed by model fitting or
model based deep learning do not explicitly consider varying hand shapes and
sizes. In this work, we introduce a novel hybrid algorithm for estimating the
3D hand pose as well as bone-lengths of the hand skeleton at the same time,
from a single depth image. The proposed CNN architecture learns hand pose
parameters and scale parameters associated with the bone-lengths
simultaneously. Subsequently, a new hybrid forward kinematics layer employs
both parameters to estimate 3D joint positions of the hand. For end-to-end
training, we combine three public datasets NYU, ICVL and MSRA-2015 in one
unified format to achieve large variation in hand shapes and sizes. Among
hybrid methods, our method shows improved accuracy over the state-of-the-art on
the combined dataset and the ICVL dataset that contain multiple subjects. Also,
our algorithm is demonstrated to work well with unseen images.
Empirical Evaluation of Abstract Argumentation: Supporting the Need for Bipolar and Probabilistic Approaches
Authors: Sylwia Polberg, Anthony Hunter
Date: 2017-07-28 00:00:00
Tasks: Abstract Argumentation

In dialogical argumentation it is often assumed that the involved parties
always correctly identify the intended statements posited by each other,
realize all of the associated relations, conform to the three acceptability
states (accepted, rejected, undecided), adjust their views when new and correct
information comes in, and that a framework handling only attack relations is
sufficient to represent their opinions. Although it is natural to make these
assumptions as a starting point for further research, removing them or even
acknowledging that such removal should happen is more challenging for some of
these concepts than for others. Probabilistic argumentation is one of the
approaches that can be harnessed for more accurate user modelling. The
epistemic approach allows us to represent how much a given argument is believed
by a given person, offering us the possibility to express more than just three
agreement states. It is equipped with a wide range of postulates, including
those that do not make any restrictions concerning how initial arguments should
be viewed, thus potentially being more adequate for handling beliefs of the
people that have not fully disclosed their opinions in comparison to Dung's
semantics. The constellation approach can be used to represent the views of
different people concerning the structure of the framework we are dealing with,
including cases in which not all relations are acknowledged or when they are
seen differently than intended. Finally, bipolar argumentation frameworks can
be used to express both positive and negative relations between arguments. In
this paper we describe the results of an experiment in which participants
judged dialogues in terms of agreement and structure. We compare our findings
with the aforementioned assumptions as well as with the constellation and
epistemic approaches to probabilistic argumentation and bipolar argumentation.
An Integrated Platform for Live 3D Human Reconstruction and Motion Capturing
Authors: Dimitrios S. Alexiadis, Anargyros Chatzitofis, Nikolaos Zioulis, Olga Zoidi, Georgios Louizis, Dimitrios Zarpalas, Petros Daras, Senior Member, IEEE
Date: 2017-12-08 00:00:00
Tasks: 3D Human Reconstruction, 3D Reconstruction

The latest developments in 3D capturing, processing, and rendering provide
means to unlock novel 3D application pathways. The main elements of an
integrated platform, which target tele-immersion and future 3D applications,
are described in this paper, addressing the tasks of real-time capturing,
robust 3D human shape/appearance reconstruction, and skeleton-based motion
tracking. More specifically, initially, the details of a multiple RGB-depth
(RGB-D) capturing system are given, along with a novel sensors' calibration
method. A robust, fast reconstruction method from multiple RGB-D streams is
then proposed, based on an enhanced variation of the volumetric Fourier
transform-based method, parallelized on the Graphics Processing Unit, and
accompanied with an appropriate texture-mapping algorithm. On top of that,
given the lack of relevant objective evaluation methods, a novel framework is
proposed for the quantitative evaluation of real-time 3D reconstruction
systems. Finally, a generic, multiple depth stream-based method for accurate
real-time human skeleton tracking is proposed. Detailed experimental results
with multi-Kinect2 data sets verify the validity of our arguments and the
effectiveness of the proposed system and methodologies.
Artificial Neural Networks that Learn to Satisfy Logic Constraints
Authors: Gadi Pinkas, Shimon Cohen
Date: 2017-12-08 00:00:00
Tasks: Automated Theorem Proving

Logic-based problems such as planning, theorem proving, or puzzles, typically
involve combinatoric search and structured knowledge representation. Artificial
neural networks are very successful statistical learners, however, for many
years, they have been criticized for their weaknesses in representing and in
processing complex structured knowledge which is crucial for combinatoric
search and symbol manipulation. Two neural architectures are presented, which
can encode structured relational knowledge in neural activation, and store
bounded First Order Logic constraints in connection weights. Both architectures
learn to search for a solution that satisfies the constraints. Learning is done
by unsupervised practicing on problem instances from the same domain, in a way
that improves the network-solving speed. No teacher exists to provide answers
for the problem instances of the training and test sets. However, the domain
constraints are provided as prior knowledge to a loss function that measures
the degree of constraint violations. Iterations of activation calculation and
learning are executed until a solution that maximally satisfies the constraints
emerges on the output units. As a test case, block-world planning problems are
used to train networks that learn to plan in that domain, but the techniques
proposed could be used more generally as in integrating prior symbolic
knowledge with statistical learning
Deep Video Generation, Prediction and Completion of Human Action Sequences
Authors: Haoye Cai, Chunyan Bai, Yu-Wing Tai, Chi-Keung Tang
Date: 2017-11-23 00:00:00
Proceeding: ECCV 2018 9
Tasks: Human action generation, Prediction, Video Generation, Video Prediction

Current deep learning results on video generation are limited while there are
only a few first results on video prediction and no relevant significant
results on video completion. This is due to the severe ill-posedness inherent
in these three problems. In this paper, we focus on human action videos, and
propose a general, two-stage deep framework to generate human action videos
with no constraints or arbitrary number of constraints, which uniformly address
the three problems: video generation given no input frames, video prediction
given the first few frames, and video completion given the first and last
frames. To make the problem tractable, in the first stage we train a deep
generative model that generates a human pose sequence from random noise. In the
second stage, a skeleton-to-image network is trained, which is used to generate
a human action video given the complete human pose sequence generated in the
first stage. By introducing the two-stage strategy, we sidestep the original
ill-posed problems while producing for the first time high-quality video
generation/prediction/completion results of much longer duration. We present
quantitative and qualitative evaluation to show that our two-stage approach
outperforms state-of-the-art methods in video generation, prediction and video
completion. Our video result demonstration can be viewed at
https://iamacewhite.github.io/supp/index.html
A Frequency Domain Neural Network for Fast Image Super-resolution
Authors: Junxuan Li, ShaoDi You, Antonio Robles-Kelly
Date: 2017-12-08 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

In this paper, we present a frequency domain neural network for image
super-resolution. The network employs the convolution theorem so as to cast
convolutions in the spatial domain as products in the frequency domain.
Moreover, the non-linearity in deep nets, often achieved by a rectifier unit,
is here cast as a convolution in the frequency domain. This not only yields a
network which is very computationally efficient at testing but also one whose
parameters can all be learnt accordingly. The network can be trained using back
propagation and is devoid of complex numbers due to the use of the Hartley
transform as an alternative to the Fourier transform. Moreover, the network is
potentially applicable to other problems elsewhere in computer vision and image
processing which are often cast in the frequency domain. We show results on
super-resolution and compare against alternatives elsewhere in the literature.
In our experiments, our network is one to two orders of magnitude faster than
the alternatives with an imperceptible loss of performance.
Music Transcription by Deep Learning with Data and "Artificial Semantic" Augmentation
Authors: Vladyslav Sarnatskyi, Vadym Ovcharenko, Mariia Tkachenko, Sergii Stirenko, Yuri Gordienko, Anis Rojbi
Date: 2017-12-08 00:00:00
Tasks: Data Augmentation, Deep Learning, Music Transcription

In this progress paper the previous results of the single note recognition by
deep learning are presented. The several ways for data augmentation and
"artificial semantic" augmentation are proposed to enhance efficiency of deep
learning approaches for monophonic and polyphonic note recognition by increase
of dimensions of training data, their lossless and lossy transformations.
Dynamic Time-Aware Attention to Speaker Roles and Contexts for Spoken Language Understanding
Authors: Po-Chun Chen, Ta-Chung Chi, Shang-Yu Su, Yun-Nung Chen
Date: 2017-09-30 00:00:00
Tasks: Dialogue State Tracking, Spoken Language Understanding

Spoken language understanding (SLU) is an essential component in
conversational systems. Most SLU component treats each utterance independently,
and then the following components aggregate the multi-turn information in the
separate phases. In order to avoid error propagation and effectively utilize
contexts, prior work leveraged history for contextual SLU. However, the
previous model only paid attention to the content in history utterances without
considering their temporal information and speaker roles. In the dialogues, the
most recent utterances should be more important than the least recent ones.
Furthermore, users usually pay attention to 1) self history for reasoning and
2) others' utterances for listening, the speaker of the utterances may provides
informative cues to help understanding. Therefore, this paper proposes an
attention-based network that additionally leverages temporal information and
speaker role for better SLU, where the attention to contexts and speaker roles
can be automatically learned in an end-to-end manner. The experiments on the
benchmark Dialogue State Tracking Challenge 4 (DSTC4) dataset show that the
time-aware dynamic role attention networks significantly improve the
understanding performance.
Coordinate Descent with Bandit Sampling
Authors: Farnood Salehi, Patrick Thiran, L. Elisa Celis
Date: 2017-12-08 00:00:00
Proceeding: NeurIPS 2018 12

Coordinate descent methods usually minimize a cost function by updating a
random decision variable (corresponding to one coordinate) at a time. Ideally,
we would update the decision variable that yields the largest decrease in the
cost function. However, finding this coordinate would require checking all of
them, which would effectively negate the improvement in computational
tractability that coordinate descent is intended to afford. To address this, we
propose a new adaptive method for selecting a coordinate. First, we find a
lower bound on the amount the cost function decreases when a coordinate is
updated. We then use a multi-armed bandit algorithm to learn which coordinates
result in the largest lower bound by interleaving this learning with
conventional coordinate descent updates except that the coordinate is selected
proportionately to the expected decrease. We show that our approach improves
the convergence of coordinate descent methods both theoretically and
experimentally.
Variational models for joint subsampling and reconstruction of turbulence-degraded images
Authors: Chun Pong Lau, Yu Hin Lai, Lok Ming Lui
Date: 2017-12-08 00:00:00

Turbulence-degraded image frames are distorted by both turbulent deformations
and space-time-varying blurs. To suppress these effects, we propose a
multi-frame reconstruction scheme to recover a latent image from the observed
image sequence. Recent approaches are commonly based on registering each frame
to a reference image, by which geometric turbulent deformations can be
estimated and a sharp image can be restored. A major challenge is that a fine
reference image is usually unavailable, as every turbulence-degraded frame is
distorted. A high-quality reference image is crucial for the accurate
estimation of geometric deformations and fusion of frames. Besides, it is
unlikely that all frames from the image sequence are useful, and thus frame
selection is necessary and highly beneficial. In this work, we propose a
variational model for joint subsampling of frames and extraction of a clear
image. A fine image and a suitable choice of subsample are simultaneously
obtained by iteratively reducing an energy functional. The energy consists of a
fidelity term measuring the discrepancy between the extracted image and the
subsampled frames, as well as regularization terms on the extracted image and
the subsample. Different choices of fidelity and regularization terms are
explored. By carefully selecting suitable frames and extracting the image, the
quality of the reconstructed image can be significantly improved. Extensive
experiments have been carried out, which demonstrate the efficacy of our
proposed model. In addition, the extracted subsamples and images can be put in
existing algorithms to produce improved results.
Direct and Real-Time Cardiovascular Risk Prediction
Authors: Bob D. de Vos, Nikolas Lessmann, Pim A. de Jong, Max A. Viergever, Ivana Isgum
Date: 2017-12-08 00:00:00
Tasks: Prediction, Segmentation

Coronary artery calcium (CAC) burden quantified in low-dose chest CT is a
predictor of cardiovascular events. We propose an automatic method for CAC
quantification, circumventing intermediate segmentation of CAC. The method
determines a bounding box around the heart using a ConvNet for localization.
Subsequently, a dedicated ConvNet analyzes axial slices within the bounding
boxes to determine CAC quantity by regression. A dataset of 1,546 baseline CT
scans was used from the National Lung Screening Trial with manually identified
CAC. The method achieved an ICC of 0.98 between manual reference and
automatically obtained Agatston scores. Stratification of subjects into five
cardiovascular risk categories resulted in an accuracy of 85\% and Cohen's
linearly weighted $\kappa$ of 0.90. The results demonstrate that real-time
quantification of CAC burden in chest CT without the need for segmentation of
CAC is possible.
Nonconvex Sparse Spectral Clustering by Alternating Direction Method of Multipliers and Its Convergence Analysis
Authors: Canyi Lu, Jiashi Feng, Zhouchen Lin, Shuicheng Yan
Date: 2017-12-08 00:00:00
Tasks: Clustering

Spectral Clustering (SC) is a widely used data clustering method which first
learns a low-dimensional embedding $U$ of data by computing the eigenvectors of
the normalized Laplacian matrix, and then performs k-means on $U^\top$ to get
the final clustering result. The Sparse Spectral Clustering (SSC) method
extends SC with a sparse regularization on $UU^\top$ by using the block
diagonal structure prior of $UU^\top$ in the ideal case. However, encouraging
$UU^\top$ to be sparse leads to a heavily nonconvex problem which is
challenging to solve and the work (Lu, Yan, and Lin 2016) proposes a convex
relaxation in the pursuit of this aim indirectly. However, the convex
relaxation generally leads to a loose approximation and the quality of the
solution is not clear. This work instead considers to solve the nonconvex
formulation of SSC which directly encourages $UU^\top$ to be sparse. We propose
an efficient Alternating Direction Method of Multipliers (ADMM) to solve the
nonconvex SSC and provide the convergence guarantee. In particular, we prove
that the sequences generated by ADMM always exist a limit point and any limit
point is a stationary point. Our analysis does not impose any assumptions on
the iterates and thus is practical. Our proposed ADMM for nonconvex problems
allows the stepsize to be increasing but upper bounded, and this makes it very
efficient in practice. Experimental analysis on several real data sets verifies
the effectiveness of our method.
Exponential Machines
Authors: Alexander Novikov, Mikhail Trofimov, Ivan Oseledets
Date: 2016-05-12 00:00:00
Tasks: Recommendation Systems, Riemannian optimization, Sentiment Analysis

Modeling interactions between features improves the performance of machine
learning solutions in many domains (e.g. recommender systems or sentiment
analysis). In this paper, we introduce Exponential Machines (ExM), a predictor
that models all interactions of every order. The key idea is to represent an
exponentially large tensor of parameters in a factorized format called Tensor
Train (TT). The Tensor Train format regularizes the model and lets you control
the number of underlying parameters. To train the model, we develop a
stochastic Riemannian optimization procedure, which allows us to fit tensors
with 2^160 entries. We show that the model achieves state-of-the-art
performance on synthetic data with high-order interactions and that it works on
par with high-order factorization machines on a recommender system dataset
MovieLens 100K.