pwc-archive/papers-with-abstracts huggingface dataset:
Deep Reinforcement Learning for Robotic Manipulation-The state of the art
Authors: Smruti Amarjyoti
Date: 2017-01-31 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The focus of this work is to enumerate the various approaches and algorithms
that center around application of reinforcement learning in robotic ma-
]]nipulation tasks. Earlier methods utilized specialized policy representations
and human demonstrations to constrict the policy. Such methods worked well with
continuous state and policy space of robots but failed to come up with
generalized policies. Subsequently, high dimensional non-linear function
approximators like neural networks have been used to learn policies from
scratch. Several novel and recent approaches have also embedded control policy
with efficient perceptual representation using deep learning. This has led to
the emergence of a new branch of dynamic robot control system called deep r
inforcement learning(DRL). This work embodies a survey of the most recent
algorithms, architectures and their implementations in simulations and real
world robotic platforms. The gamut of DRL architectures are partitioned into
two different branches namely, discrete action space algorithms(DAS) and
continuous action space algorithms(CAS). Further, the CAS algorithms are
divided into stochastic continuous action space(SCAS) and deterministic
continuous action space(DCAS) algorithms. Along with elucidating an organ-
isation of the DRL algorithms this work also manifests some of the state of the
art applications of these approaches in robotic manipulation tasks.
3D Shape Retrieval via Irrelevance Filtering and Similarity Ranking (IF/SR)
Authors: Xiaqing Pan, Yueru Chen, C. -C. Jay Kuo
Date: 2017-01-30 00:00:00
Tasks: 3D Shape Classification, 3D Shape Retrieval, Clustering, Retrieval

A novel solution for the content-based 3D shape retrieval problem using an
unsupervised clustering approach, which does not need any label information of
3D shapes, is presented in this work. The proposed shape retrieval system
consists of two modules in cascade: the irrelevance filtering (IF) module and
the similarity ranking (SR) module. The IF module attempts to cluster gallery
shapes that are similar to each other by examining global and local features
simultaneously. However, shapes that are close in the local feature space can
be distant in the global feature space, and vice versa. To resolve this issue,
we propose a joint cost function that strikes a balance between two distances.
Irrelevant samples that are close in the local feature space but distant in the
global feature space can be removed in this stage. The remaining gallery
samples are ranked in the SR module using the local feature. The superior
performance of the proposed IF/SR method is demonstrated by extensive
experiments conducted on the popular SHREC12 dataset.
Interaction Information for Causal Inference: The Case of Directed Triangle
Authors: AmirEmad Ghassami, Negar Kiyavash
Date: 2017-01-30 00:00:00
Tasks: Causal Inference

Interaction information is one of the multivariate generalizations of mutual
information, which expresses the amount information shared among a set of
variables, beyond the information, which is shared in any proper subset of
those variables. Unlike (conditional) mutual information, which is always
non-negative, interaction information can be negative. We utilize this property
to find the direction of causal influences among variables in a triangle
topology under some mild assumptions.
Lip Reading Sentences in the Wild
Authors: Joon Son Chung, Andrew Senior, Oriol Vinyals, Andrew Zisserman
Date: 2016-11-16 00:00:00
Proceeding: CVPR 2017 7
Tasks: Lipreading, Lip Reading, speech-recognition, Speech Recognition, Visual Speech Recognition

The goal of this work is to recognise phrases and sentences being spoken by a
talking face, with or without the audio. Unlike previous works that have
focussed on recognising a limited number of words or phrases, we tackle lip
reading as an open-world problem - unconstrained natural language sentences,
and in the wild videos.
  Our key contributions are: (1) a 'Watch, Listen, Attend and Spell' (WLAS)
network that learns to transcribe videos of mouth motion to characters; (2) a
curriculum learning strategy to accelerate training and to reduce overfitting;
(3) a 'Lip Reading Sentences' (LRS) dataset for visual speech recognition,
consisting of over 100,000 natural sentences from British television.
  The WLAS model trained on the LRS dataset surpasses the performance of all
previous work on standard lip reading benchmark datasets, often by a
significant margin. This lip reading performance beats a professional lip
reader on videos from BBC television, and we also demonstrate that visual
information helps to improve speech recognition performance even when the audio
is available.
Spatial Projection of Multiple Climate Variables using Hierarchical Multitask Learning
Authors: André R. Gonçalves, Arindam Banerjee, Fernando J. Von Zuben
Date: 2017-01-30 00:00:00
Tasks: Climate Projection

Future projection of climate is typically obtained by combining outputs from
multiple Earth System Models (ESMs) for several climate variables such as
temperature and precipitation. While IPCC has traditionally used a simple model
output average, recent work has illustrated potential advantages of using a
multitask learning (MTL) framework for projections of individual climate
variables. In this paper we introduce a framework for hierarchical multitask
learning (HMTL) with two levels of tasks such that each super-task, i.e., task
at the top level, is itself a multitask learning problem over sub-tasks. For
climate projections, each super-task focuses on projections of specific climate
variables spatially using an MTL formulation. For the proposed HMTL approach, a
group lasso regularization is added to couple parameters across the
super-tasks, which in the climate context helps exploit relationships among the
behavior of different climate variables at a given spatial location. We show
that some recent works on MTL based on learning task dependency structures can
be viewed as special cases of HMTL. Experiments on synthetic and real climate
data show that HMTL produces better results than decoupled MTL methods applied
separately on the super-tasks and HMTL significantly outperforms baselines for
climate projection.
Emergence of Selective Invariance in Hierarchical Feed Forward Networks
Authors: Dipan K. Pal, Vishnu Boddeti, Marios Savvides
Date: 2017-01-30 00:00:00
Tasks: Object Categorization

Many theories have emerged which investigate how in- variance is generated in
hierarchical networks through sim- ple schemes such as max and mean pooling.
The restriction to max/mean pooling in theoretical and empirical studies has
diverted attention away from a more general way of generating invariance to
nuisance transformations. We con- jecture that hierarchically building
selective invariance (i.e. carefully choosing the range of the transformation
to be in- variant to at each layer of a hierarchical network) is im- portant
for pattern recognition. We utilize a novel pooling layer called adaptive
pooling to find linear pooling weights within networks. These networks with the
learnt pooling weights have performances on object categorization tasks that
are comparable to max/mean pooling networks. In- terestingly, adaptive pooling
can converge to mean pooling (when initialized with random pooling weights),
find more general linear pooling schemes or even decide not to pool at all. We
illustrate the general notion of selective invari- ance through object
categorization experiments on large- scale datasets such as SVHN and ILSVRC
2012.
Language Independent Single Document Image Super-Resolution using CNN for improved recognition
Authors: Ram Krishna Pandey, A. G. Ramakrishnan
Date: 2017-01-30 00:00:00
Tasks: Image Enhancement, Image Super-Resolution, Optical Character Recognition (OCR), Super-Resolution

Recognition of document images have important applications in restoring old
and classical texts. The problem involves quality improvement before passing it
to a properly trained OCR to get accurate recognition of the text. The image
enhancement and quality improvement constitute important steps as subsequent
recognition depends upon the quality of the input image. There are scenarios
when high resolution images are not available and our experiments show that the
OCR accuracy reduces significantly with decrease in the spatial resolution of
document images. Thus the only option is to improve the resolution of such
document images. The goal is to construct a high resolution image, given a
single low resolution binary image, which constitutes the problem of single
image super-resolution. Most of the previous work in super-resolution deal with
natural images which have more information-content than the document images.
Here, we use Convolution Neural Network to learn the mapping between low and
the corresponding high resolution images. We experiment with different number
of layers, parameter settings and non-linear functions to build a fast
end-to-end framework for document image super-resolution. Our proposed model
shows a very good PSNR improvement of about 4 dB on 75 dpi Tamil images,
resulting in a 3 % improvement of word level accuracy by the OCR. It takes less
time than the recent sparse based natural image super-resolution technique,
making it useful for real-time document recognition applications.
Visualization of Jacques Lacan's Registers of the Psychoanalytic Field, and Discovery of Metaphor and of Metonymy. Analytical Case Study of Edgar Allan Poe's "The Purloined Letter"
Authors: Fionn Murtagh, Giuseppe Iurato
Date: 2016-04-23 00:00:00

We start with a description of Lacan's work that we then take into our
analytics methodology. In a first investigation, a Lacan-motivated template of
the Poe story is fitted to the data. A segmentation of the storyline is used in
order to map out the diachrony. Based on this, it will be shown how synchronous
aspects, potentially related to Lacanian registers, can be sought. This
demonstrates the effectiveness of an approach based on a model template of the
storyline narrative. In a second and more comprehensive investigation, we
develop an approach for revealing, that is, uncovering, Lacanian register
relationships. Objectives of this work include the wide and general application
of our methodology. This methodology is strongly based on the "letting the data
speak" Correspondence Analysis analytics platform of Jean-Paul Benz\'ecri, that
is also the geometric data analysis, both qualitative and quantitative
analytics, developed by Pierre Bourdieu.
Expert Level control of Ramp Metering based on Multi-task Deep Reinforcement Learning
Authors: Francois Belletti, Daniel Haziza, Gabriel Gomes, Alexandre M. Bayen
Date: 2017-01-30 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This article shows how the recent breakthroughs in Reinforcement Learning
(RL) that have enabled robots to learn to play arcade video games, walk or
assemble colored bricks, can be used to perform other tasks that are currently
at the core of engineering cyberphysical systems. We present the first use of
RL for the control of systems modeled by discretized non-linear Partial
Differential Equations (PDEs) and devise a novel algorithm to use
non-parametric control techniques for large multi-agent systems. We show how
neural network based RL enables the control of discretized PDEs whose
parameters are unknown, random, and time-varying. We introduce an algorithm of
Mutual Weight Regularization (MWR) which alleviates the curse of dimensionality
of multi-agent control schemes by sharing experience between agents while
giving each agent the opportunity to specialize its action policy so as to
tailor it to the local parameters of the part of the system it is located in.
Improving Variational Inference with Inverse Autoregressive Flow
Authors: Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling
Date: 2016-06-15 00:00:00
Tasks: Variational Inference

The framework of normalizing flows provides a general strategy for flexible
variational inference of posteriors over latent variables. We propose a new
type of normalizing flow, inverse autoregressive flow (IAF), that, in contrast
to earlier published flows, scales well to high-dimensional latent spaces. The
proposed flow consists of a chain of invertible transformations, where each
transformation is based on an autoregressive neural network. In experiments, we
show that IAF significantly improves upon diagonal Gaussian approximate
posteriors. In addition, we demonstrate that a novel type of variational
autoencoder, coupled with IAF, is competitive with neural autoregressive models
in terms of attained log-likelihood on natural images, while allowing
significantly faster synthesis.
Structural Connectome Validation Using Pairwise Classification
Authors: Dmitry Petrov, Boris Gutman, Alexander Ivanov, Joshua Faskowitz, Neda Jahanshad, Mikhail Belyaev, Paul Thompson
Date: 2017-01-26 00:00:00
Tasks: Classification, General Classification

In this work, we study the extent to which structural connectomes and
topological derivative measures are unique to individual changes within human
brains. To do so, we classify structural connectome pairs from two large
longitudinal datasets as either belonging to the same individual or not. Our
data is comprised of 227 individuals from the Alzheimer's Disease Neuroimaging
Initiative (ADNI) and 226 from the Parkinson's Progression Markers Initiative
(PPMI). We achieve 0.99 area under the ROC curve score for features which
represent either weights or network structure of the connectomes (node degrees,
PageRank and local efficiency). Our approach may be useful for eliminating
noisy features as a preprocessing step in brain aging studies and early
diagnosis classification problems.
Learning from various labeling strategies for suicide-related messages on social media: An experimental study
Authors: Tong Liu, Qijin Cheng, Christopher M. Homan, Vincent M. B. Silenzio
Date: 2017-01-30 00:00:00

Suicide is an important but often misunderstood problem, one that researchers
are now seeking to better understand through social media. Due in large part to
the fuzzy nature of what constitutes suicidal risks, most supervised approaches
for learning to automatically detect suicide-related activity in social media
require a great deal of human labor to train. However, humans themselves have
diverse or conflicting views on what constitutes suicidal thoughts. So how to
obtain reliable gold standard labels is fundamentally challenging and, we
hypothesize, depends largely on what is asked of the annotators and what slice
of the data they label. We conducted multiple rounds of data labeling and
collected annotations from crowdsourcing workers and domain experts. We
aggregated the resulting labels in various ways to train a series of supervised
models. Our preliminary evaluations show that using unanimously agreed labels
from multiple annotators is helpful to achieve robust machine models.
Understanding food inflation in India: A Machine Learning approach
Authors: Akash Malhotra, Mayank Maloo
Date: 2017-01-30 00:00:00
Tasks: BIG-bench Machine Learning

Over the past decade, the stellar growth of Indian economy has been
challenged by persistently high levels of inflation, particularly in food
prices. The primary reason behind this stubborn food inflation is mismatch in
supply-demand, as domestic agricultural production has failed to keep up with
rising demand owing to a number of proximate factors. The relative significance
of these factors in determining the change in food prices have been analysed
using gradient boosted regression trees (BRT), a machine learning technique.
The results from BRT indicates all predictor variables to be fairly significant
in explaining the change in food prices, with MSP and farm wages being
relatively more important than others. International food prices were found to
have limited relevance in explaining the variation in domestic food prices. The
challenge of ensuring food and nutritional security for growing Indian
population with rising incomes needs to be addressed through resolute policy
reforms.
Click Through Rate Prediction for Contextual Advertisment Using Linear Regression
Authors: Muhammad Junaid Effendi, Syed Abbas Ali
Date: 2017-01-30 00:00:00
Tasks: Click-Through Rate Prediction, feature selection, regression

This research presents an innovative and unique way of solving the
advertisement prediction problem which is considered as a learning problem over
the past several years. Online advertising is a multi-billion-dollar industry
and is growing every year with a rapid pace. The goal of this research is to
enhance click through rate of the contextual advertisements using Linear
Regression. In order to address this problem, a new technique propose in this
paper to predict the CTR which will increase the overall revenue of the system
by serving the advertisements more suitable to the viewers with the help of
feature extraction and displaying the advertisements based on context of the
publishers. The important steps include the data collection, feature
extraction, CTR prediction and advertisement serving. The statistical results
obtained from the dynamically used technique show an efficient outcome by
fitting the data close to perfection for the LR technique using optimized
feature selection.
PathNet: Evolution Channels Gradient Descent in Super Neural Networks
Authors: Chrisantha Fernando, Dylan Banarse, Charles Blundell, Yori Zwols, David Ha, Andrei A. Rusu, Alexander Pritzel, Daan Wierstra
Date: 2017-01-30 00:00:00
Tasks: Continual Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

For artificial general intelligence (AGI) it would be efficient if multiple
users trained the same giant neural network, permitting parameter reuse,
without catastrophic forgetting. PathNet is a first step in this direction. It
is a neural network algorithm that uses agents embedded in the neural network
whose task is to discover which parts of the network to re-use for new tasks.
Agents are pathways (views) through the network which determine the subset of
parameters that are used and updated by the forwards and backwards passes of
the backpropogation algorithm. During learning, a tournament selection genetic
algorithm is used to select pathways through the neural network for replication
and mutation. Pathway fitness is the performance of that pathway measured
according to a cost function. We demonstrate successful transfer learning;
fixing the parameters along a path learned on task A and re-evolving a new
population of paths for task B, allows task B to be learned faster than it
could be learned from scratch or after fine-tuning. Paths evolved on task B
re-use parts of the optimal path evolved on task A. Positive transfer was
demonstrated for binary MNIST, CIFAR, and SVHN supervised learning
classification tasks, and a set of Atari and Labyrinth reinforcement learning
tasks, suggesting PathNets have general applicability for neural network
training. Finally, PathNet also significantly improves the robustness to
hyperparameter choices of a parallel asynchronous reinforcement learning
algorithm (A3C).
Memory Augmented Neural Networks with Wormhole Connections
Authors: Caglar Gulcehre, Sarath Chandar, Yoshua Bengio
Date: 2017-01-30 00:00:00

Recent empirical results on long-term dependency tasks have shown that neural
networks augmented with an external memory can learn the long-term dependency
tasks more easily and achieve better generalization than vanilla recurrent
neural networks (RNN). We suggest that memory augmented neural networks can
reduce the effects of vanishing gradients by creating shortcut (or wormhole)
connections. Based on this observation, we propose a novel memory augmented
neural network model called TARDIS (Temporal Automatic Relation Discovery in
Sequences). The controller of TARDIS can store a selective set of embeddings of
its own previous hidden states into an external memory and revisit them as and
when needed. For TARDIS, memory acts as a storage for wormhole connections to
the past to propagate the gradients more effectively and it helps to learn the
temporal dependencies. The memory structure of TARDIS has similarities to both
Neural Turing Machines (NTM) and Dynamic Neural Turing Machines (D-NTM), but
both read and write operations of TARDIS are simpler and more efficient. We use
discrete addressing for read/write operations which helps to substantially to
reduce the vanishing gradient problem with very long sequences. Read and write
operations in TARDIS are tied with a heuristic once the memory becomes full,
and this makes the learning problem simpler when compared to NTM or D-NTM type
of architectures. We provide a detailed analysis on the gradient propagation in
general for MANNs. We evaluate our models on different long-term dependency
tasks and report competitive results in all of them.
Margins of discrete Bayesian networks
Authors: Robin J. Evans
Date: 2015-01-09 00:00:00

Bayesian network models with latent variables are widely used in statistics
and machine learning. In this paper we provide a complete algebraic
characterization of Bayesian network models with latent variables when the
observed variables are discrete and no assumption is made about the state-space
of the latent variables. We show that it is algebraically equivalent to the
so-called nested Markov model, meaning that the two are the same up to
inequality constraints on the joint probabilities. In particular these two
models have the same dimension. The nested Markov model is therefore the best
possible description of the latent variable model that avoids consideration of
inequalities, which are extremely complicated in general. A consequence of this
is that the constraint finding algorithm of Tian and Pearl (UAI 2002,
pp519-527) is complete for finding equality constraints.
  Latent variable models suffer from difficulties of unidentifiable parameters
and non-regular asymptotics; in contrast the nested Markov model is fully
identifiable, represents a curved exponential family of known dimension, and
can easily be fitted using an explicit parameterization.
Double/Debiased/Neyman Machine Learning of Treatment Effects
Authors: Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey
Date: 2017-01-30 00:00:00
Tasks: BIG-bench Machine Learning, valid

Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, and Newey (2016) provide a
generic double/de-biased machine learning (DML) approach for obtaining valid
inferential statements about focal parameters, using Neyman-orthogonal scores
and cross-fitting, in settings where nuisance parameters are estimated using a
new generation of nonparametric fitting methods for high-dimensional data,
called machine learning methods. In this note, we illustrate the application of
this method in the context of estimating average treatment effects (ATE) and
average treatment effects on the treated (ATTE) using observational data. A
more general discussion and references to the existing literature are available
in Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, and Newey (2016).