pwc-archive/papers-with-abstracts huggingface dataset:
A Framework for Real-Time Face and Facial Feature Tracking using Optical Flow Pre-estimation and Template Tracking
Authors: E. R. Gast, Michael S. Lew
Date: 2010-12-31 00:00:00
Tasks: Optical Flow Estimation

This work presents a framework for tracking head movements and capturing the
movements of the mouth and both the eyebrows in real-time. We present a head
tracker which is a combination of a optical flow and a template based tracker.
The estimation of the optical flow head tracker is used as starting point for
the template tracker which fine-tunes the head estimation. This approach
together with re-updating the optical flow points prevents the head tracker
from drifting. This combination together with our switching scheme, makes our
tracker very robust against fast movement and motion-blur. We also propose a
way to reduce the influence of partial occlusion of the head. In both the
optical flow and the template based tracker we identify and exclude occluded
points.
Faceted Ranking of Egos in Collaborative Tagging Systems
Authors: Jose Ignacio Orlicki, Pablo Ignacio Fierens, José Ignacio Alvarez-Hamelin
Date: 2008-09-26 00:00:00
Tasks: Retrieval, TAG

Multimedia uploaded content is tagged and recommended by users of
collaborative systems, resulting in informal classifications also known as
folksonomies. Faceted web ranking has been proved a reasonable alternative to a
single ranking which does not take into account a personalized context. In this
paper we analyze the online computation of rankings of users associated to
facets made up of multiple tags. Possible applications are user reputation
evaluation (ego-ranking) and improvement of content quality in case of
retrieval. We propose a solution based on PageRank as centrality measure: (i) a
ranking for each tag is computed offline on the basis of the corresponding
tag-dependent subgraph; (ii) a faceted order is generated by merging rankings
corresponding to all the tags in the facet. The fundamental assumption,
validated by empirical observations, is that step (i) is scalable. We also
present algorithms for part (ii) having time complexity O(k), where k is the
number of tags in the facet, well suited to online computation.
Multi-Faceted Ranking of News Articles using Post-Read Actions
Authors: Deepak Agarwal, Bee-Chung Chen, Xuanhui Wang
Date: 2012-05-03 00:00:00
Tasks: Articles

Personalized article recommendation is important to improve user engagement
on news sites. Existing work quantifies engagement primarily through click
rates. We argue that quality of recommendations can be improved by
incorporating different types of "post-read" engagement signals like sharing,
commenting, printing and e-mailing article links. More specifically, we propose
a multi-faceted ranking problem for recommending news articles where each facet
corresponds to a ranking problem to maximize actions of a post-read action
type. The key technical challenge is to estimate the rates of post-read action
types by mitigating the impact of enormous data sparsity, we do so through
several variations of factor models. To exploit correlations among post-read
action types we also introduce a novel variant called locally augmented tensor
(LAT) model. Through data obtained from a major news site in the US, we show
that factor models significantly outperform a few baseline IR models and the
LAT model significantly outperforms several other variations of factor models.
Our findings show that it is possible to incorporate post-read signals that are
commonly available on online news sites to improve quality of recommendations.
Real-time face swapping as a tool for understanding infant self-recognition
Authors: Sao Mai Nguyen, Masaki Ogino, Minoru Asada
Date: 2011-12-09 00:00:00
Tasks: Face Swapping, Visual Tracking

To study the preference of infants for contingency of movements and
familiarity of faces during self-recognition task, we built, as an accurate and
instantaneous imitator, a real-time face- swapper for videos. We present a
non-constraint face-swapper based on 3D visual tracking that achieves real-time
performance through parallel computing. Our imitator system is par- ticularly
suited for experiments involving children with Autistic Spectrum Disorder who
are often strongly disturbed by the constraints of other methods.
Thresholded ConvNet Ensembles: Neural Networks for Technical Forecasting
Authors: Sid Ghoshal, Stephen J. Roberts
Date: 2018-07-09 00:00:00
Tasks: Time Series, Time Series Analysis

Much of modern practice in financial forecasting relies on technicals, an
umbrella term for several heuristics applying visual pattern recognition to
price charts. Despite its ubiquity in financial media, the reliability of its
signals remains a contentious and highly subjective form of 'domain knowledge'.
We investigate the predictive value of patterns in financial time series,
applying machine learning and signal processing techniques to 22 years of US
equity data. By reframing technical analysis as a poorly specified, arbitrarily
preset feature-extractive layer in a deep neural network, we show that better
convolutional filters can be learned directly from the data, and provide visual
representations of the features being identified. We find that an ensemble of
shallow, thresholded CNNs optimised over different resolutions achieves
state-of-the-art performance on this domain, outperforming technical methods
while retaining some of their interpretability.
Block Belief Propagation for Parameter Learning in Markov Random Fields
Authors: You Lu, Zhiyuan Liu, Bert Huang
Date: 2018-11-09 00:00:00

Traditional learning methods for training Markov random fields require doing
inference over all variables to compute the likelihood gradient. The iteration
complexity for those methods therefore scales with the size of the graphical
models. In this paper, we propose \emph{block belief propagation learning}
(BBPL), which uses block-coordinate updates of approximate marginals to compute
approximate gradients, removing the need to compute inference on the entire
graphical model. Thus, the iteration complexity of BBPL does not scale with the
size of the graphs. We prove that the method converges to the same solution as
that obtained by using full inference per iteration, despite these
approximations, and we empirically demonstrate its scalability improvements
over standard training methods.
Automated Multi-Label Classification based on ML-Plan
Authors: Marcel Wever, Felix Mohr, Eyke Hüllermeier
Date: 2018-11-09 00:00:00
Tasks: AutoML, BIG-bench Machine Learning, Classification, General Classification, Multi-class Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Automated machine learning (AutoML) has received increasing attention in the
recent past. While the main tools for AutoML, such as Auto-WEKA, TPOT, and
auto-sklearn, mainly deal with single-label classification and regression,
there is very little work on other types of machine learning tasks. In
particular, there is almost no work on automating the engineering of machine
learning applications for multi-label classification. This paper makes two
contributions. First, it discusses the usefulness and feasibility of an AutoML
approach for multi-label classification. Second, we show how the scope of
ML-Plan, an AutoML-tool for multi-class classification, can be extended towards
multi-label classification using MEKA, which is a multi-label extension of the
well-known Java library WEKA. The resulting approach recursively refines MEKA's
multi-label classifiers, which sometimes nest another multi-label classifier,
up to the selection of a single-label base learner provided by WEKA. In our
evaluation, we find that the proposed approach yields superb results and
performs significantly better than a set of baselines.
A Microprocessor implemented in 65nm CMOS with Configurable and Bit-scalable Accelerator for Programmable In-memory Computing
Authors: Hongyang Jia, Yinqi Tang, Hossein Valavi, Jintao Zhang, Naveen Verma
Date: 2018-11-09 00:00:00
Tasks: CPU

This paper presents a programmable in-memory-computing processor,
demonstrated in a 65nm CMOS technology. For data-centric workloads, such as
deep neural networks, data movement often dominates when implemented with
today's computing architectures. This has motivated spatial architectures,
where the arrangement of data-storage and compute hardware is distributed and
explicitly aligned to the computation dataflow, most notably for matrix-vector
multiplication. In-memory computing is a spatial architecture where processing
elements correspond to dense bit cells, providing local storage and compute,
typically employing analog operation. Though this raises the potential for high
energy efficiency and throughput, analog operation has significantly limited
robustness, scale, and programmability. This paper describes a 590kb
in-memory-computing accelerator integrated in a programmable processor
architecture, by exploiting recent approaches to charge-domain in-memory
computing. The architecture takes the approach of tight coupling with an
embedded CPU, through accelerator interfaces enabling integration in the
standard processor memory space. Additionally, a near-memory-computing datapath
both enables diverse computations locally, to address operations required
across applications, and enables bit-precision scalability for
matrix/input-vector elements, through a bit-parallel/bit-serial (BP/BS) scheme.
Chip measurements show an energy efficiency of 152/297 1b-TOPS/W and throughput
of 4.7/1.9 1b-TOPS (scaling linearly with the matrix/input-vector element
precisions) at VDD of 1.2/0.85V. Neural network demonstrations with 1-b/4-b
weights and activations for CIFAR-10 classification consume 5.3/105.2
$\mu$J/image at 176/23 fps, with accuracy at the level of digital/software
implementation (89.3/92.4 $\%$ accuracy).
Splenomegaly Segmentation on Multi-modal MRI using Deep Convolutional Networks
Authors: Yuankai Huo, Zhoubing Xu, Shunxing Bao, Camilo Bermudez, Hyeonsoo Moon, Prasanna Parvathaneni, Tamara K. Moyo, Michael R. Savona, Albert Assad, Richard G. Abramson, Bennett A. Landman
Date: 2018-11-09 00:00:00
Tasks: Segmentation, Splenomegaly Segmentation On Multi-Modal Mri

The findings of splenomegaly, abnormal enlargement of the spleen, is a
non-invasive clinical biomarker for liver and spleen disease. Automated
segmentation methods are essential to efficiently quantify splenomegaly from
clinically acquired abdominal magnetic resonance imaging (MRI) scans. However,
the task is challenging due to (1) large anatomical and spatial variations of
splenomegaly, (2) large inter- and intra-scan intensity variations on
multi-modal MRI, and (3) limited numbers of labeled splenomegaly scans. In this
paper, we propose the Splenomegaly Segmentation Network (SS-Net) to introduce
the deep convolutional neural network (DCNN) approaches in multi-modal MRI
splenomegaly segmentation. Large convolutional kernel layers were used to
address the spatial and anatomical variations, while the conditional generative
adversarial networks (GAN) were employed to leverage the segmentation
performance of SS-Net in an end-to-end manner. A clinically acquired cohort
containing both T1-weighted (T1w) and T2-weighted (T2w) MRI splenomegaly scans
was used to train and evaluate the performance of multi-atlas segmentation
(MAS), 2D DCNN networks, and a 3D DCNN network. From the experimental results,
the DCNN methods achieved superior performance to the state-of-the-art MAS
method. The proposed SS-Net method achieved the highest median and mean Dice
scores among investigated baseline DCNN methods.
An Overview of Computational Approaches for Interpretation Analysis
Authors: Philipp Blandfort, Jörn Hees, Desmond U. Patton
Date: 2018-11-09 00:00:00

It is said that beauty is in the eye of the beholder. But how exactly can we characterize such discrepancies in interpretation? For example, are there any specific features of an image that makes person A regard an image as beautiful while person B finds the same image displeasing? Such questions ultimately aim at explaining our individual ways of interpretation, an intention that has been of fundamental importance to the social sciences from the beginning. More recently, advances in computer science brought up two related questions: First, can computational tools be adopted for analyzing ways of interpretation? Second, what if the "beholder" is a computer model, i.e., how can we explain a computer model's point of view? Numerous efforts have been made regarding both of these points, while many existing approaches focus on particular aspects and are still rather separate. With this paper, in order to connect these approaches we introduce a theoretical framework for analyzing interpretation, which is applicable to interpretation of both human beings and computer models. We give an overview of relevant computational approaches from various fields, and discuss the most common and promising application areas. The focus of this paper lies on interpretation of text and image data, while many of the presented approaches are applicable to other types of data as well.
Adversarial Uncertainty Quantification in Physics-Informed Neural Networks
Authors: Yibo Yang, Paris Perdikaris
Date: 2018-11-09 00:00:00
Tasks: Uncertainty Quantification

We present a deep learning framework for quantifying and propagating
uncertainty in systems governed by non-linear differential equations using
physics-informed neural networks. Specifically, we employ latent variable
models to construct probabilistic representations for the system states, and
put forth an adversarial inference procedure for training them on data, while
constraining their predictions to satisfy given physical laws expressed by
partial differential equations. Such physics-informed constraints provide a
regularization mechanism for effectively training deep generative models as
surrogates of physical systems in which the cost of data acquisition is high,
and training data-sets are typically small. This provides a flexible framework
for characterizing uncertainty in the outputs of physical systems due to
randomness in their inputs or noise in their observations that entirely
bypasses the need for repeatedly sampling expensive experiments or numerical
simulators. We demonstrate the effectiveness of our approach through a series
of examples involving uncertainty propagation in non-linear conservation laws,
and the discovery of constitutive laws for flow through porous media directly
from noisy data.
Convolutional neural networks in phase space and inverse problems
Authors: Gunther Uhlmann, Yiran Wang
Date: 2018-11-09 00:00:00
Tasks: BIG-bench Machine Learning

We study inverse problems consisting on determining medium properties using
the responses to probing waves from the machine learning point of view. Based
on the understanding of propagation of waves and their nonlinear interactions,
we construct a deep convolutional neural network in which the parameters are
used to classify and reconstruct the coefficients of nonlinear wave equations
that model the medium properties. Furthermore, for given approximation
accuracy, we obtain the depth and number of units of the network and their
quantitative dependence on the complexity of the medium.
A generic framework for privacy preserving deep learning
Authors: Theo Ryffel, Andrew Trask, Morten Dahl, Bobby Wagner, Jason Mancuso, Daniel Rueckert, Jonathan Passerat-Palmbach
Date: 2018-11-09 00:00:00
Tasks: Deep Learning, Federated Learning, Privacy Preserving, Privacy Preserving Deep Learning

We detail a new framework for privacy preserving deep learning and discuss
its assets. The framework puts a premium on ownership and secure processing of
data and introduces a valuable representation based on chains of commands and
tensors. This abstraction allows one to implement complex privacy preserving
constructs such as Federated Learning, Secure Multiparty Computation, and
Differential Privacy while still exposing a familiar deep learning API to the
end-user. We report early results on the Boston Housing and Pima Indian
Diabetes datasets. While the privacy features apart from Differential Privacy
do not impact the prediction accuracy, the current implementation of the
framework introduces a significant overhead in performance, which will be
addressed at a later stage of the development. We believe this work is an
important milestone introducing the first reliable, general framework for
privacy preserving deep learning.
Reachability-based safe learning for optimal control problem
Authors: Stanislav Fedorov, Antonio Candelieri
Date: 2018-11-09 00:00:00
Tasks: Friction

In this work we seek for an approach to integrate safety in the learning
process that relies on a partly known state-space model of the system and
regards the unknown dynamics as an additive bounded disturbance. We introduce a
framework for safely learning a control strategy for a given system with an
additive disturbance. On the basis of the known part of the model, a safe set
in which the system can learn safely, the algorithm can choose optimal actions
for pursuing the target set as long as the safety-preserving condition is
satisfied. After some learning episodes, the disturbance can be updated based
on real-world data. To this end, Gaussian Process regression is conducted on
the collected disturbance samples. Since the unstable nature of the law of the
real world, for example, change of friction or conductivity with the
temperature, we expect to have the more robust solution of optimal control
problem.
  For evaluation of approach described above we choose an inverted pendulum as
a benchmark model. The proposed algorithm manages to learn a policy that does
not violate the pre-specified safety constraints. Observed performance is
improved when it was incorporated exploration set up to make sure that an
optimal policy is learned everywhere in the safe set. Finally, we outline some
promising directions for future research beyond the scope of this paper.
Identify, locate and separate: Audio-visual object extraction in large video collections using weak supervision
Authors: Sanjeel Parekh, Alexey Ozerov, Slim Essid, Ngoc Duong, Patrick Pérez, Gaël Richard
Date: 2018-11-09 00:00:00
Tasks: General Classification, Multiple Instance Learning, Object, Object Localization, Representation Learning

We tackle the problem of audiovisual scene analysis for weakly-labeled data.
To this end, we build upon our previous audiovisual representation learning
framework to perform object classification in noisy acoustic environments and
integrate audio source enhancement capability. This is made possible by a novel
use of non-negative matrix factorization for the audio modality. Our approach
is founded on the multiple instance learning paradigm. Its effectiveness is
established through experiments over a challenging dataset of music instrument
performance videos. We also show encouraging visual object localization
results.
Deep Compression of Sum-Product Networks on Tensor Networks
Authors: Ching-Yun Ko, Cong Chen, Yuke Zhang, Kim Batselier, Ngai Wong
Date: 2018-11-09 00:00:00
Tasks: Tensor Networks

Sum-product networks (SPNs) represent an emerging class of neural networks
with clear probabilistic semantics and superior inference speed over graphical
models. This work reveals a strikingly intimate connection between SPNs and
tensor networks, thus leading to a highly efficient representation that we call
tensor SPNs (tSPNs). For the first time, through mapping an SPN onto a tSPN and
employing novel optimization techniques, we demonstrate remarkable parameter
compression with negligible loss in accuracy.
A Convergence Theory for Deep Learning via Over-Parameterization
Authors: Zeyuan Allen-Zhu, Yuanzhi Li, Zhao Song
Date: 2018-11-09 00:00:00
Tasks: Deep Learning

Deep neural networks (DNNs) have demonstrated dominating performance in many fields; since AlexNet, networks used in practice are going wider and deeper. On the theoretical side, a long line of works has been focusing on training neural networks with one hidden layer. The theory of multi-layer networks remains largely unsettled. In this work, we prove why stochastic gradient descent (SGD) can find $\textit{global minima}$ on the training objective of DNNs in $\textit{polynomial time}$. We only make two assumptions: the inputs are non-degenerate and the network is over-parameterized. The latter means the network width is sufficiently large: $\textit{polynomial}$ in $L$, the number of layers and in $n$, the number of samples. Our key technique is to derive that, in a sufficiently large neighborhood of the random initialization, the optimization landscape is almost-convex and semi-smooth even with ReLU activations. This implies an equivalence between over-parameterized neural networks and neural tangent kernel (NTK) in the finite (and polynomial) width setting. As concrete examples, starting from randomly initialized weights, we prove that SGD can attain 100% training accuracy in classification tasks, or minimize regression loss in linear convergence speed, with running time polynomial in $n,L$. Our theory applies to the widely-used but non-smooth ReLU activation, and to any smooth and possibly non-convex loss functions. In terms of network architectures, our theory at least applies to fully-connected neural networks, convolutional neural networks (CNN), and residual neural networks (ResNet).
Matrix Recovery with Implicitly Low-Rank Data
Authors: Xingyu Xie, Jianlong Wu, Guangcan Liu, Jun Wang
Date: 2018-11-09 00:00:00

In this paper, we study the problem of matrix recovery, which aims to restore
a target matrix of authentic samples from grossly corrupted observations. Most
of the existing methods, such as the well-known Robust Principal Component
Analysis (RPCA), assume that the target matrix we wish to recover is low-rank.
However, the underlying data structure is often non-linear in practice,
therefore the low-rankness assumption could be violated. To tackle this issue,
we propose a novel method for matrix recovery in this paper, which could well
handle the case where the target matrix is low-rank in an implicit feature
space but high-rank or even full-rank in its original form. Namely, our method
pursues the low-rank structure of the target matrix in an implicit feature
space. By making use of the specifics of an accelerated proximal gradient based
optimization algorithm, the proposed method could recover the target matrix
with non-linear structures from its corrupted version. Comprehensive
experiments on both synthetic and real datasets demonstrate the superiority of
our method.
A Hierarchical Framework for Relation Extraction with Reinforcement Learning
Authors: Ryuichi Takanobu, Tianyang Zhang, Jiexi Liu, Minlie Huang
Date: 2018-11-09 00:00:00
Tasks: Entity Extraction using GAN, Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Relation, Relation Extraction

Most existing methods determine relation types only after all the entities
have been recognized, thus the interaction between relation types and entity
mentions is not fully modeled. This paper presents a novel paradigm to deal
with relation extraction by regarding the related entities as the arguments of
a relation. We apply a hierarchical reinforcement learning (HRL) framework in
this paradigm to enhance the interaction between entity mentions and relation
types. The whole extraction process is decomposed into a hierarchy of two-level
RL policies for relation detection and entity extraction respectively, so that
it is more feasible and natural to deal with overlapping relations. Our model
was evaluated on public datasets collected via distant supervision, and results
show that it gains better performance than existing methods and is more
powerful for extracting overlapping relations.