pwc-archive/papers-with-abstracts huggingface dataset:
When confidence and competence collide: Effects on online decision-making discussions
Authors: Liye Fu, Lillian Lee, Cristian Danescu-Niculescu-Mizil
Date: 2017-02-24 00:00:00
Tasks: Decision Making

Group discussions are a way for individuals to exchange ideas and arguments
in order to reach better decisions than they could on their own. One of the
premises of productive discussions is that better solutions will prevail, and
that the idea selection process is mediated by the (relative) competence of the
individuals involved. However, since people may not know their actual
competence on a new task, their behavior is influenced by their self-estimated
competence --- that is, their confidence --- which can be misaligned with their
actual competence.
  Our goal in this work is to understand the effects of confidence-competence
misalignment on the dynamics and outcomes of discussions. To this end, we
design a large-scale natural setting, in the form of an online team-based
geography game, that allows us to disentangle confidence from competence and
thus separate their effects.
  We find that in task-oriented discussions, the more-confident individuals
have a larger impact on the group's decisions even when these individuals are
at the same level of competence as their teammates. Furthermore, this
unjustified role of confidence in the decision-making process often leads teams
to under-perform. We explore this phenomenon by investigating the effects of
confidence on conversational dynamics.
Residual Networks of Residual Networks: Multilevel Residual Networks
Authors: Ke Zhang, Miao Sun, Tony X. Han, Xingfang Yuan, Liru Guo, Tao Liu
Date: 2016-08-09 00:00:00
Tasks: Image Classification

A residual-networks family with hundreds or even thousands of layers
dominates major image recognition tasks, but building a network by simply
stacking residual blocks inevitably limits its optimization ability. This paper
proposes a novel residual-network architecture, Residual networks of Residual
networks (RoR), to dig the optimization ability of residual networks. RoR
substitutes optimizing residual mapping of residual mapping for optimizing
original residual mapping. In particular, RoR adds level-wise shortcut
connections upon original residual networks to promote the learning capability
of residual networks. More importantly, RoR can be applied to various kinds of
residual networks (ResNets, Pre-ResNets and WRN) and significantly boost their
performance. Our experiments demonstrate the effectiveness and versatility of
RoR, where it achieves the best performance in all residual-network-like
structures. Our RoR-3-WRN58-4+SD models achieve new state-of-the-art results on
CIFAR-10, CIFAR-100 and SVHN, with test errors 3.77%, 19.73% and 1.59%,
respectively. RoR-3 models also achieve state-of-the-art results compared to
ResNets on ImageNet data set.
A Statistical Machine Learning Approach to Yield Curve Forecasting
Authors: Rajiv Sambasivan, Sourish Das
Date: 2017-03-04 00:00:00
Tasks: BIG-bench Machine Learning, Gaussian Processes, Time Series, Time Series Analysis

Yield curve forecasting is an important problem in finance. In this work we
explore the use of Gaussian Processes in conjunction with a dynamic modeling
strategy, much like the Kalman Filter, to model the yield curve. Gaussian
Processes have been successfully applied to model functional data in a variety
of applications. A Gaussian Process is used to model the yield curve. The
hyper-parameters of the Gaussian Process model are updated as the algorithm
receives yield curve data. Yield curve data is typically available as a time
series with a frequency of one day. We compare existing methods to forecast the
yield curve with the proposed method. The results of this study showed that
while a competing method (a multivariate time series method) performed well in
forecasting the yields at the short term structure region of the yield curve,
Gaussian Processes perform well in the medium and long term structure regions
of the yield curve. Accuracy in the long term structure region of the yield
curve has important practical implications. The Gaussian Process framework
yields uncertainty and probability estimates directly in contrast to other
competing methods. Analysts are frequently interested in this information. In
this study the proposed method has been applied to yield curve forecasting,
however it can be applied to model high frequency time series data or data
streams in other domains.
Evolving Deep Neural Networks
Authors: Risto Miikkulainen, Jason Liang, Elliot Meyerson, Aditya Rawal, Dan Fink, Olivier Francon, Bala Raju, Hormoz Shahrzad, Arshak Navruzyan, Nigel Duffy, Babak Hodjat
Date: 2017-03-01 00:00:00
Tasks: Deep Learning, Image Captioning, Language Modeling, Language Modelling, Object Recognition

The success of deep learning depends on finding an architecture to fit the
task. As deep learning has scaled up to more challenging tasks, the
architectures have become difficult to design by hand. This paper proposes an
automated method, CoDeepNEAT, for optimizing deep learning architectures
through evolution. By extending existing neuroevolution methods to topology,
components, and hyperparameters, this method achieves results comparable to
best human designs in standard benchmarks in object recognition and language
modeling. It also supports building a real-world application of automated image
captioning on a magazine website. Given the anticipated increases in available
computing power, evolution of deep networks is promising approach to
constructing deep learning applications in the future.
High Accuracy Classification of Parkinson's Disease through Shape Analysis and Surface Fitting in $^{123}$I-Ioflupane SPECT Imaging
Authors: R. Prashanth, Sumantra Dutta Roy, Pravat K. Mandal, Shantanu Ghosh
Date: 2017-03-04 00:00:00
Tasks: Diagnostic, Feature Importance, General Classification, Management, Prognosis

Early and accurate identification of parkinsonian syndromes (PS) involving
presynaptic degeneration from non-degenerative variants such as Scans Without
Evidence of Dopaminergic Deficit (SWEDD) and tremor disorders, is important for
effective patient management as the course, therapy and prognosis differ
substantially between the two groups. In this study, we use Single Photon
Emission Computed Tomography (SPECT) images from healthy normal, early PD and
SWEDD subjects, as obtained from the Parkinson's Progression Markers Initiative
(PPMI) database, and process them to compute shape- and surface fitting-based
features for the three groups. We use these features to develop and compare
various classification models that can discriminate between scans showing
dopaminergic deficit, as in PD, from scans without the deficit, as in healthy
normal or SWEDD. Along with it, we also compare these features with Striatal
Binding Ratio (SBR)-based features, which are well-established and clinically
used, by computing a feature importance score using Random forests technique.
We observe that the Support Vector Machine (SVM) classifier gave the best
performance with an accuracy of 97.29%. These features also showed higher
importance than the SBR-based features. We infer from the study that shape
analysis and surface fitting are useful and promising methods for extracting
discriminatory features that can be used to develop diagnostic models that
might have the potential to help clinicians in the diagnostic process.
Don't Fear the Reaper: Refuting Bostrom's Superintelligence Argument
Authors: Sebastian Benthall
Date: 2017-02-27 00:00:00

In recent years prominent intellectuals have raised ethical concerns about
the consequences of artificial intelligence. One concern is that an autonomous
agent might modify itself to become "superintelligent" and, in supremely
effective pursuit of poorly specified goals, destroy all of humanity. This
paper considers and rejects the possibility of this outcome. We argue that this
scenario depends on an agent's ability to rapidly improve its ability to
predict its environment through self-modification. Using a Bayesian model of a
reasoning agent, we show that there are important limitations to how an agent
may improve its predictive ability through self-modification alone. We conclude
that concern about this artificial intelligence outcome is misplaced and better
directed at policy questions around data access and storage.
Genetic CNN
Authors: Lingxi Xie, Alan Yuille
Date: 2017-03-04 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object Recognition

The deep Convolutional Neural Network (CNN) is the state-of-the-art solution
for large-scale visual recognition. Following basic principles such as
increasing the depth and constructing highway connections, researchers have
manually designed a lot of fixed network structures and verified their
effectiveness.
  In this paper, we discuss the possibility of learning deep network structures
automatically. Note that the number of possible network structures increases
exponentially with the number of layers in the network, which inspires us to
adopt the genetic algorithm to efficiently traverse this large search space. We
first propose an encoding method to represent each network structure in a
fixed-length binary string, and initialize the genetic algorithm by generating
a set of randomized individuals. In each generation, we define standard genetic
operations, e.g., selection, mutation and crossover, to eliminate weak
individuals and then generate more competitive ones. The competitiveness of
each individual is defined as its recognition accuracy, which is obtained via
training the network from scratch and evaluating it on a validation set. We run
the genetic process on two small datasets, i.e., MNIST and CIFAR10,
demonstrating its ability to evolve and find high-quality structures which are
little studied before. These structures are also transferrable to the
large-scale ILSVRC2012 dataset.
Morphognosis: the shape of knowledge in space and time
Authors: Thomas E. Portegys
Date: 2017-01-05 00:00:00
Tasks: Navigate

Artificial intelligence research to a great degree focuses on the brain and
behaviors that the brain generates. But the brain, an extremely complex
structure resulting from millions of years of evolution, can be viewed as a
solution to problems posed by an environment existing in space and time. The
environment generates signals that produce sensory events within an organism.
Building an internal spatial and temporal model of the environment allows an
organism to navigate and manipulate the environment. Higher intelligence might
be the ability to process information coming from a larger extent of
space-time. In keeping with nature's penchant for extending rather than
replacing, the purpose of the mammalian neocortex might then be to record
events from distant reaches of space and time and render them, as though yet
near and present, to the older, deeper brain whose instinctual roles have
changed little over eons. Here this notion is embodied in a model called
morphognosis (morpho = shape and gnosis = knowledge). Its basic structure is a
pyramid of event recordings called a morphognostic. At the apex of the pyramid
are the most recent and nearby events. Receding from the apex are less recent
and possibly more distant events. A morphognostic can thus be viewed as a
structure of progressively larger chunks of space-time knowledge. A set of
morphognostics forms long-term memories that are learned by exposure to the
environment. A cellular automaton is used as the platform to investigate the
morphognosis model, using a simulated organism that learns to forage in its
world for food, build a nest, and play the game of Pong.
A Compositional Object-Based Approach to Learning Physical Dynamics
Authors: Michael B. Chang, Tomer Ullman, Antonio Torralba, Joshua B. Tenenbaum
Date: 2016-12-01 00:00:00
Tasks: Object

We present the Neural Physics Engine (NPE), a framework for learning
simulators of intuitive physics that naturally generalize across variable
object count and different scene configurations. We propose a factorization of
a physical scene into composable object-based representations and a neural
network architecture whose compositional structure factorizes object dynamics
into pairwise interactions. Like a symbolic physics engine, the NPE is endowed
with generic notions of objects and their interactions; realized as a neural
network, it can be trained via stochastic gradient descent to adapt to specific
object properties and dynamics of different worlds. We evaluate the efficacy of
our approach on simple rigid body dynamics in two-dimensional worlds. By
comparing to less structured architectures, we show that the NPE's
compositional representation of the structure in physical interactions improves
its ability to predict movement, generalize across variable object count and
different scene configurations, and infer latent properties of objects such as
mass.
Autoencoding Variational Inference For Topic Models
Authors: Akash Srivastava, Charles Sutton
Date: 2017-03-04 00:00:00
Tasks: Topic Models, Variational Inference

Topic models are one of the most popular methods for learning representations
of text, but a major challenge is that any change to the topic model requires
mathematically deriving a new inference algorithm. A promising approach to
address this problem is autoencoding variational Bayes (AEVB), but it has
proven diffi- cult to apply to topic models in practice. We present what is to
our knowledge the first effective AEVB based inference method for latent
Dirichlet allocation (LDA), which we call Autoencoded Variational Inference For
Topic Model (AVITM). This model tackles the problems caused for AEVB by the
Dirichlet prior and by component collapsing. We find that AVITM matches
traditional methods in accuracy with much better inference time. Indeed,
because of the inference network, we find that it is unnecessary to pay the
computational cost of running variational optimization on test data. Because
AVITM is black box, it is readily applied to new topic models. As a dramatic
illustration of this, we present a new topic model called ProdLDA, that
replaces the mixture model in LDA with a product of experts. By changing only
one line of code from LDA, we find that ProdLDA yields much more interpretable
topics, even if LDA is trained via collapsed Gibbs sampling.
Lexical Resources for Hindi Marathi MT
Authors: Sreelekha. S, Pushpak Bhattacharyya
Date: 2017-03-04 00:00:00
Tasks: Machine Translation, Translation

In this paper we describe some ways to utilize various lexical resources to
improve the quality of statistical machine translation system. We have
augmented the training corpus with various lexical resources such as
IndoWordnet semantic relation set, function words, kridanta pairs and verb
phrases etc. Our research on the usage of lexical resources mainly focused on
two ways such as augmenting parallel corpus with more vocabulary and augmenting
with various word forms. We have described case studies, evaluations and
detailed error analysis for both Marathi to Hindi and Hindi to Marathi machine
translation systems. From the evaluations we observed that, there is an
incremental growth in the quality of machine translation as the usage of
various lexical resources increases. Moreover usage of various lexical
resources helps to improve the coverage and quality of machine translation
where limited parallel corpus is available.
Sharp bounds for population recovery
Authors: Anindya De, Ryan O'Donnell, Rocco Servedio
Date: 2017-03-04 00:00:00

The population recovery problem is a basic problem in noisy unsupervised
learning that has attracted significant research attention in recent years
[WY12,DRWY12, MS13, BIMP13, LZ15,DST16]. A number of different variants of this
problem have been studied, often under assumptions on the unknown distribution
(such as that it has restricted support size). In this work we study the sample
complexity and algorithmic complexity of the most general version of the
problem, under both bit-flip noise and erasure noise model. We give essentially
matching upper and lower sample complexity bounds for both noise models, and
efficient algorithms matching these sample complexity bounds up to polynomial
factors.
Chain-NN: An Energy-Efficient 1D Chain Architecture for Accelerating Deep Convolutional Neural Networks
Authors: Shihao Wang, Dajiang Zhou, Xushen Han, Takeshi Yoshimura
Date: 2017-03-04 00:00:00

Deep convolutional neural networks (CNN) have shown their good performances
in many computer vision tasks. However, the high computational complexity of
CNN involves a huge amount of data movements between the computational
processor core and memory hierarchy which occupies the major of the power
consumption. This paper presents Chain-NN, a novel energy-efficient 1D chain
architecture for accelerating deep CNNs. Chain-NN consists of the dedicated
dual-channel process engines (PE). In Chain-NN, convolutions are done by the 1D
systolic primitives composed of a group of adjacent PEs. These systolic
primitives, together with the proposed column-wise scan input pattern, can
fully reuse input operand to reduce the memory bandwidth requirement for energy
saving. Moreover, the 1D chain architecture allows the systolic primitives to
be easily reconfigured according to specific CNN parameters with fewer design
complexity. The synthesis and layout of Chain-NN is under TSMC 28nm process. It
costs 3751k logic gates and 352KB on-chip memory. The results show a 576-PE
Chain-NN can be scaled up to 700MHz. This achieves a peak throughput of
806.4GOPS with 567.5mW and is able to accelerate the five convolutional layers
in AlexNet at a frame rate of 326.2fps. 1421.0GOPS/W power efficiency is at
least 2.5 to 4.1x times better than the state-of-the-art works.
Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion
Authors: Benjamin Hou, Amir Alansary, Steven McDonagh, Alice Davidson, Mary Rutherford, Jo V. Hajnal, Daniel Rueckert, Ben Glocker, Bernhard Kainz
Date: 2017-02-28 00:00:00
Tasks: Image Registration, Motion Compensation, regression

This paper aims to solve a fundamental problem in intensity-based 2D/3D
registration, which concerns the limited capture range and need for very good
initialization of state-of-the-art image registration methods. We propose a
regression approach that learns to predict rotation and translations of
arbitrary 2D image slices from 3D volumes, with respect to a learned canonical
atlas co-ordinate system. To this end, we utilize Convolutional Neural Networks
(CNNs) to learn the highly complex regression function that maps 2D image
slices into their correct position and orientation in 3D space. Our approach is
attractive in challenging imaging scenarios, where significant subject motion
complicates reconstruction performance of 3D volumes from 2D slice data. We
extensively evaluate the effectiveness of our approach quantitatively on
simulated MRI brain data with extreme random motion. We further demonstrate
qualitative results on fetal MRI where our method is integrated into a full
reconstruction and motion compensation pipeline. With our CNN regression
approach we obtain an average prediction error of 7mm on simulated data, and
convincing reconstruction quality of images of very young fetuses where
previous methods fail. We further discuss applications to Computed Tomography
and X-ray projections. Our approach is a general solution to the 2D/3D
initialization problem. It is computationally efficient, with prediction times
per slice of a few milliseconds, making it suitable for real-time scenarios.
An unsupervised bayesian approach for the joint reconstruction and classification of cutaneous reflectance confocal microscopy images
Authors: Abdelghafour Halimi, Hadj Batatia, Jimmy Le Digabel, Gwendal Josse, Jean-Yves Tourneret
Date: 2017-03-04 00:00:00
Tasks: General Classification

This paper studies a new Bayesian algorithm for the joint reconstruction and
classification of reflectance confocal microscopy (RCM) images, with
application to the identification of human skin lentigo. The proposed Bayesian
approach takes advantage of the distribution of the multiplicative speckle
noise affecting the true reflectivity of these images and of appropriate priors
for the unknown model parameters. A Markov chain Monte Carlo (MCMC) algorithm
is proposed to jointly estimate the model parameters and the image of true
reflectivity while classifying images according to the distribution of their
reflectivity. Precisely, a Metropolis-whitin-Gibbs sampler is investigated to
sample the posterior distribution of the Bayesian model associated with RCM
images and to build estimators of its parameters, including labels indicating
the class of each RCM image. The resulting algorithm is applied to synthetic
data and to real images from a clinical study containing healthy and lentigo
patients.
Sequential Quantiles via Hermite Series Density Estimation
Authors: Michael Stephanou, Melvin Varughese, Iain Macdonald
Date: 2015-07-17 00:00:00
Tasks: Data Summarization, Sequential Distribution Function Estimation, Sequential Quantile Estimation

Sequential quantile estimation refers to incorporating observations into
quantile estimates in an incremental fashion thus furnishing an online estimate
of one or more quantiles at any given point in time. Sequential quantile
estimation is also known as online quantile estimation. This area is relevant
to the analysis of data streams and to the one-pass analysis of massive data
sets. Applications include network traffic and latency analysis, real time
fraud detection and high frequency trading. We introduce new techniques for
online quantile estimation based on Hermite series estimators in the settings
of static quantile estimation and dynamic quantile estimation. In the static
quantile estimation setting we apply the existing Gauss-Hermite expansion in a
novel manner. In particular, we exploit the fact that Gauss-Hermite
coefficients can be updated in a sequential manner. To treat dynamic quantile
estimation we introduce a novel expansion with an exponentially weighted
estimator for the Gauss-Hermite coefficients which we term the Exponentially
Weighted Gauss-Hermite (EWGH) expansion. These algorithms go beyond existing
sequential quantile estimation algorithms in that they allow arbitrary
quantiles (as opposed to pre-specified quantiles) to be estimated at any point
in time. In doing so we provide a solution to online distribution function and
online quantile function estimation on data streams. In particular we derive an
analytical expression for the CDF and prove consistency results for the CDF
under certain conditions. In addition we analyse the associated quantile
estimator. Simulation studies and tests on real data reveal the Gauss-Hermite
based algorithms to be competitive with a leading existing algorithm.