pwc-archive/papers-with-abstracts huggingface dataset:
A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks
Authors: Cengiz Pehlevan, Dmitri B. Chklovskii
Date: 2015-11-30 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Dimensionality Reduction

To make sense of the world our brains must analyze high-dimensional datasets
streamed by our sensory organs. Because such analysis begins with
dimensionality reduction, modelling early sensory processing requires
biologically plausible online dimensionality reduction algorithms. Recently, we
derived such an algorithm, termed similarity matching, from a Multidimensional
Scaling (MDS) objective function. However, in the existing algorithm, the
number of output dimensions is set a priori by the number of output neurons and
cannot be changed. Because the number of informative dimensions in sensory
inputs is variable there is a need for adaptive dimensionality reduction. Here,
we derive biologically plausible dimensionality reduction algorithms which
adapt the number of output dimensions to the eigenspectrum of the input
covariance matrix. We formulate three objective functions which, in the offline
setting, are optimized by the projections of the input dataset onto its
principal subspace scaled by the eigenvalues of the output covariance matrix.
In turn, the output eigenvalues are computed as i) soft-thresholded, ii)
hard-thresholded, iii) equalized thresholded eigenvalues of the input
covariance matrix. In the online setting, we derive the three corresponding
adaptive algorithms and map them onto the dynamics of neuronal activity in
networks with biologically plausible local learning rules. Remarkably, in the
last two networks, neurons are divided into two classes which we identify with
principal neurons and interneurons in biological circuits.
LIA-RAG: a system based on graphs and divergence of probabilities applied to Speech-To-Text Summarization
Authors: Elvys Linhares Pontes, Juan-Manuel Torres-Moreno, Andréa Carneiro Linhares
Date: 2016-01-26 00:00:00
Tasks: RAG, Speech-to-Text, Text Summarization

This paper aims to introduces a new algorithm for automatic speech-to-text
summarization based on statistical divergences of probabilities and graphs. The
input is a text from speech conversations with noise, and the output a compact
text summary. Our results, on the pilot task CCCS Multiling 2015 French corpus
are very encouraging
Supersparse Linear Integer Models for Optimized Medical Scoring Systems
Authors: Berk Ustun, Cynthia Rudin
Date: 2015-02-15 00:00:00
Tasks: Interpretable Machine Learning

Scoring systems are linear classification models that only require users to
add, subtract and multiply a few small numbers in order to make a prediction.
These models are in widespread use by the medical community, but are difficult
to learn from data because they need to be accurate and sparse, have coprime
integer coefficients, and satisfy multiple operational constraints. We present
a new method for creating data-driven scoring systems called a Supersparse
Linear Integer Model (SLIM). SLIM scoring systems are built by solving an
integer program that directly encodes measures of accuracy (the 0-1 loss) and
sparsity (the $\ell_0$-seminorm) while restricting coefficients to coprime
integers. SLIM can seamlessly incorporate a wide range of operational
constraints related to accuracy and sparsity, and can produce highly tailored
models without parameter tuning. We provide bounds on the testing and training
accuracy of SLIM scoring systems, and present a new data reduction technique
that can improve scalability by eliminating a portion of the training data
beforehand. Our paper includes results from a collaboration with the
Massachusetts General Hospital Sleep Laboratory, where SLIM was used to create
a highly tailored scoring system for sleep apnea screening
What can we learn about CNNs from a large scale controlled object dataset?
Authors: Ali Borji, Saeed Izadi, Laurent Itti
Date: 2015-12-04 00:00:00
Tasks: Domain Adaptation, Object Recognition, Transfer Learning

Tolerance to image variations (e.g. translation, scale, pose, illumination)
is an important desired property of any object recognition system, be it human
or machine. Moving towards increasingly bigger datasets has been trending in
computer vision specially with the emergence of highly popular deep learning
models. While being very useful for learning invariance to object inter- and
intra-class shape variability, these large-scale wild datasets are not very
useful for learning invariance to other parameters forcing researchers to
resort to other tricks for training a model. In this work, we introduce a
large-scale synthetic dataset, which is freely and publicly available, and use
it to answer several fundamental questions regarding invariance and selectivity
properties of convolutional neural networks. Our dataset contains two parts: a)
objects shot on a turntable: 16 categories, 8 rotation angles, 11 cameras on a
semicircular arch, 5 lighting conditions, 3 focus levels, variety of
backgrounds (23.4 per instance) generating 1320 images per instance (over 20
million images in total), and b) scenes: in which a robot arm takes pictures of
objects on a 1:160 scale scene. We study: 1) invariance and selectivity of
different CNN layers, 2) knowledge transfer from one object category to
another, 3) systematic or random sampling of images to build a train set, 4)
domain adaptation from synthetic to natural scenes, and 5) order of knowledge
delivery to CNNs. We also explore how our analyses can lead the field to
develop more efficient CNNs.
Intelligent Conversational Bot for Massive Online Open Courses (MOOCs)
Authors: Ser Ling Lim, Ong Sing Goh
Date: 2016-01-26 00:00:00
Tasks: General Knowledge, speech-recognition, Speech Recognition, Speech Synthesis

Massive Online Open Courses (MOOCs) which were introduced in 2008 has since
drawn attention around the world for both its advantages as well as criticism
on its drawbacks. One of the issues in MOOCs which is the lack of interactivity
with the instructor has brought conversational bot into the picture to fill in
this gap. In this study, a prototype of MOOCs conversational bot, MOOC-bot is
being developed and integrated into MOOCs website to respond to the learner
inquiries using text or speech input. MOOC-bot is using the popular Artificial
Intelligence Markup Language (AIML) to develop its knowledge base, leverage
from AIML capability to deliver appropriate responses and can be quickly
adapted to new knowledge domains. The system architecture of MOOC-bot consists
of knowledge base along with AIML interpreter, chat interface, MOOCs website
and Web Speech API to provide speech recognition and speech synthesis
capability. The initial MOOC-bot prototype has the general knowledge from the
past Loebner Prize winner - ALICE, frequent asked questions, and a content
offered by Universiti Teknikal Malaysia Melaka (UTeM). The evaluation of
MOOC-bot based on the past competition questions from Chatterbox Challenge
(CBC) and Loebner Prize has shown that it was able to provide correct answers
most of the time during the test and demonstrated the capability to prolong the
conversation. The advantages of MOOC-bot such as able to provide 24-hour
service that can serve different time zones, able to have knowledge in multiple
domains, and can be shared by multiple sites simultaneously have outweighed its
existing limitations.
Polyhedron Volume-Ratio-based Classification for Image Recognition
Authors: Qingxiang Feng, Jeng-Shyang Pan, Jar-Ferr Yang, Yang-Ting Chou
Date: 2016-01-26 00:00:00
Tasks: Classification, General Classification

In this paper, a novel method, called polyhedron volume ratio classification
(PVRC) is proposed for image recognition
Diffusion tensor imaging with deterministic error bounds
Authors: Artur Gorokh, Yury Korolev, Tuomo Valkonen
Date: 2015-09-07 00:00:00

Errors in the data and the forward operator of an inverse problem can be
handily modelled using partial order in Banach lattices. We present some
existing results of the theory of regularisation in this novel framework, where
errors are represented as bounds by means of the appropriate partial order.
  We apply the theory to Diffusion Tensor Imaging, where correct noise
modelling is challenging: it involves the Rician distribution and the nonlinear
Stejskal-Tanner equation. Linearisation of the latter in the statistical
framework would complicate the noise model even further. We avoid this using
the error bounds approach, which preserves simple error structure under
monotone transformations.
Virtual Rephotography: Novel View Prediction Error for 3D Reconstruction
Authors: Michael Waechter, Mate Beljan, Simon Fuhrmann, Nils Moehrle, Johannes Kopf, Michael Goesele
Date: 2016-01-26 00:00:00
Tasks: 3D Reconstruction

The ultimate goal of many image-based modeling systems is to render
photo-realistic novel views of a scene without visible artifacts. Existing
evaluation metrics and benchmarks focus mainly on the geometric accuracy of the
reconstructed model, which is, however, a poor predictor of visual accuracy.
Furthermore, using only geometric accuracy by itself does not allow evaluating
systems that either lack a geometric scene representation or utilize coarse
proxy geometry. Examples include light field or image-based rendering systems.
We propose a unified evaluation approach based on novel view prediction error
that is able to analyze the visual quality of any method that can render novel
views from input images. One of the key advantages of this approach is that it
does not require ground truth geometry. This dramatically simplifies the
creation of test datasets and benchmarks. It also allows us to evaluate the
quality of an unknown scene during the acquisition and reconstruction process,
which is useful for acquisition planning. We evaluate our approach on a range
of methods including standard geometry-plus-texture pipelines as well as
image-based rendering techniques, compare it to existing geometry-based
benchmarks, and demonstrate its utility for a range of use cases.
DeePM: A Deep Part-Based Model for Object Detection and Semantic Part Localization
Authors: Jun Zhu, Xianjie Chen, Alan L. Yuille
Date: 2015-11-23 00:00:00
Tasks: Object, object-detection, Object Detection

In this paper, we propose a deep part-based model (DeePM) for symbiotic
object detection and semantic part localization. For this purpose, we annotate
semantic parts for all 20 object categories on the PASCAL VOC 2012 dataset,
which provides information on object pose, occlusion, viewpoint and
functionality. DeePM is a latent graphical model based on the state-of-the-art
R-CNN framework, which learns an explicit representation of the object-part
configuration with flexible type sharing (e.g., a sideview horse head can be
shared by a fully-visible sideview horse and a highly truncated sideview horse
with head and neck only). For comparison, we also present an end-to-end
Object-Part (OP) R-CNN which learns an implicit feature representation for
jointly mapping an image ROI to the object and part bounding boxes. We evaluate
the proposed methods for both the object and part detection performance on
PASCAL VOC 2012, and show that DeePM consistently outperforms OP R-CNN in
detecting objects and parts. In addition, it obtains superior performance to
Fast and Faster R-CNNs in object detection.
A Novel Memetic Feature Selection Algorithm
Authors: Mohadeseh Montazeri, Hamid Reza Naji, Mitra Montazeri, Ahmad Faraahi
Date: 2016-01-26 00:00:00
Tasks: feature selection, General Classification

Feature selection is a problem of finding efficient features among all
features in which the final feature set can improve accuracy and reduce
complexity. In feature selection algorithms search strategies are key aspects.
Since feature selection is an NP-Hard problem; therefore heuristic algorithms
have been studied to solve this problem. In this paper, we have proposed a
method based on memetic algorithm to find an efficient feature subset for a
classification problem. It incorporates a filter method in the genetic
algorithm to improve classification performance and accelerates the search in
identifying core feature subsets. Particularly, the method adds or deletes a
feature from a candidate feature subset based on the multivariate feature
information. Empirical study on commonly data sets of the university of
California, Irvine shows that the proposed method outperforms existing methods.
Fisher Motion Descriptor for Multiview Gait Recognition
Authors: F. M. Castro, M. J. Marín-Jiménez, N. Guil, R. Muñoz-Salinas
Date: 2016-01-26 00:00:00
Tasks: Gait Recognition, Multiview Gait Recognition

The goal of this paper is to identify individuals by analyzing their gait.
Instead of using binary silhouettes as input data (as done in many previous
works) we propose and evaluate the use of motion descriptors based on densely
sampled short-term trajectories. We take advantage of state-of-the-art people
detectors to define custom spatial configurations of the descriptors around the
target person, obtaining a rich representation of the gait motion. The local
motion features (described by the Divergence-Curl-Shear descriptor) extracted
on the different spatial areas of the person are combined into a single
high-level gait descriptor by using the Fisher Vector encoding. The proposed
approach, coined Pyramidal Fisher Motion, is experimentally validated on
`CASIA' dataset (parts B and C), `TUM GAID' dataset, `CMU MoBo' dataset and the
recent `AVA Multiview Gait' dataset. The results show that this new approach
achieves state-of-the-art results in the problem of gait recognition, allowing
to recognize walking people from diverse viewpoints on single and multiple
camera setups, wearing different clothes, carrying bags, walking at diverse
speeds and not limited to straight walking paths.
Classification and Verification of Online Handwritten Signatures with Time Causal Information Theory Quantifiers
Authors: Osvaldo A. Rosso, Raydonal Ospina, Alejandro C. Frery
Date: 2016-01-26 00:00:00
Tasks: General Classification

We present a new approach for online handwritten signature classification and
verification based on descriptors stemming from Information Theory. The
proposal uses the Shannon Entropy, the Statistical Complexity, and the Fisher
Information evaluated over the Bandt and Pompe symbolization of the horizontal
and vertical coordinates of signatures. These six features are easy and fast to
compute, and they are the input to an One-Class Support Vector Machine
classifier. The results produced surpass state-of-the-art techniques that
employ higher-dimensional feature spaces which often require specialized
software and hardware. We assess the consistency of our proposal with respect
to the size of the training sample, and we also use it to classify the
signatures into meaningful groups.
Minimax Structured Normal Means Inference
Authors: Akshay Krishnamurthy
Date: 2015-06-25 00:00:00
Tasks: Experimental Design

We provide a unified treatment of a broad class of noisy structure recovery
problems, known as structured normal means problems. In this setting, the goal
is to identify, from a finite collection of Gaussian distributions with
different means, the distribution that produced some observed data. Recent work
has studied several special cases including sparse vectors, biclusters, and
graph-based structures. We establish nearly matching upper and lower bounds on
the minimax probability of error for any structured normal means problem, and
we derive an optimality certificate for the maximum likelihood estimator, which
can be applied to many instantiations. We also consider an experimental design
setting, where we generalize our minimax bounds and derive an algorithm for
computing a design strategy with a certain optimality property. We show that
our results give tight minimax bounds for many structure recovery problems and
consider some consequences for interactive sampling.
A Survey on Artificial Intelligence and Data Mining for MOOCs
Authors: Simon Fauvel, Han Yu
Date: 2016-01-26 00:00:00
Tasks: Survey

Massive Open Online Courses (MOOCs) have gained tremendous popularity in the
last few years. Thanks to MOOCs, millions of learners from all over the world
have taken thousands of high-quality courses for free. Putting together an
excellent MOOC ecosystem is a multidisciplinary endeavour that requires
contributions from many different fields. Artificial intelligence (AI) and data
mining (DM) are two such fields that have played a significant role in making
MOOCs what they are today. By exploiting the vast amount of data generated by
learners engaging in MOOCs, DM improves our understanding of the MOOC ecosystem
and enables MOOC practitioners to deliver better courses. Similarly, AI,
supported by DM, can greatly improve student experience and learning outcomes.
In this survey paper, we first review the state-of-the-art artificial
intelligence and data mining research applied to MOOCs, emphasising the use of
AI and DM tools and techniques to improve student engagement, learning
outcomes, and our understanding of the MOOC ecosystem. We then offer an
overview of key trends and important research to carry out in the fields of AI
and DM so that MOOCs can reach their full potential.
Survey on the attention based RNN model and its applications in computer vision
Authors: Feng Wang, David M. J. Tax
Date: 2016-01-25 00:00:00
Tasks: Implicit Relations

The recurrent neural networks (RNN) can be used to solve the sequence to
sequence problem, where both the input and the output have sequential
structures. Usually there are some implicit relations between the structures.
However, it is hard for the common RNN model to fully explore the relations
between the sequences. In this survey, we introduce some attention based RNN
models which can focus on different parts of the input for each output item, in
order to explore and take advantage of the implicit relations between the input
and the output items. The different attention mechanisms are described in
detail. We then introduce some applications in computer vision which apply the
attention based RNN models. The superiority of the attention based RNN model is
shown by the experimental results. At last some future research directions are
given.
Very Efficient Training of Convolutional Neural Networks using Fast Fourier Transform and Overlap-and-Add
Authors: Tyler Highlander, Andres Rodriguez
Date: 2016-01-25 00:00:00

Convolutional neural networks (CNNs) are currently state-of-the-art for
various classification tasks, but are computationally expensive. Propagating
through the convolutional layers is very slow, as each kernel in each layer
must sequentially calculate many dot products for a single forward and backward
propagation which equates to $\mathcal{O}(N^{2}n^{2})$ per kernel per layer
where the inputs are $N \times N$ arrays and the kernels are $n \times n$
arrays. Convolution can be efficiently performed as a Hadamard product in the
frequency domain. The bottleneck is the transformation which has a cost of
$\mathcal{O}(N^{2}\log_2 N)$ using the fast Fourier transform (FFT). However,
the increase in efficiency is less significant when $N\gg n$ as is the case in
CNNs. We mitigate this by using the "overlap-and-add" technique reducing the
computational complexity to $\mathcal{O}(N^2\log_2 n)$ per kernel. This method
increases the algorithm's efficiency in both the forward and backward
propagation, reducing the training and testing time for CNNs. Our empirical
results show our method reduces computational time by a factor of up to 16.3
times the traditional convolution implementation for a 8 $\times$ 8 kernel and
a 224 $\times$ 224 image.
Emerging Dimension Weights in a Conceptual Spaces Model of Concept Combination
Authors: Martha Lewis, Jonathan Lawry
Date: 2016-01-25 00:00:00

We investigate the generation of new concepts from combinations of properties
as an artificial language develops. To do so, we have developed a new framework
for conjunctive concept combination. This framework gives a semantic grounding
to the weighted sum approach to concept combination seen in the literature. We
implement the framework in a multi-agent simulation of language evolution and
show that shared combination weights emerge. The expected value and the
variance of these weights across agents may be predicted from the distribution
of elements in the conceptual space, as determined by the underlying
environment, together with the rate at which agents adopt others' concepts.
When this rate is smaller, the agents are able to converge to weights with
lower variance. However, the time taken to converge to a steady state
distribution of weights is longer.
Fast Robust PCA on Graphs
Authors: Nauman Shahid, Nathanael Perraudin, Vassilis Kalofolias, Gilles Puy, Pierre Vandergheynst
Date: 2015-07-29 00:00:00
Tasks: Clustering, Dimensionality Reduction

Mining useful clusters from high dimensional data has received significant
attention of the computer vision and pattern recognition community in the
recent years. Linear and non-linear dimensionality reduction has played an
important role to overcome the curse of dimensionality. However, often such
methods are accompanied with three different problems: high computational
complexity (usually associated with the nuclear norm minimization),
non-convexity (for matrix factorization methods) and susceptibility to gross
corruptions in the data. In this paper we propose a principal component
analysis (PCA) based solution that overcomes these three issues and
approximates a low-rank recovery method for high dimensional datasets. We
target the low-rank recovery by enforcing two types of graph smoothness
assumptions, one on the data samples and the other on the features by designing
a convex optimization problem. The resulting algorithm is fast, efficient and
scalable for huge datasets with O(nlog(n)) computational complexity in the
number of data samples. It is also robust to gross corruptions in the dataset
as well as to the model parameters. Clustering experiments on 7 benchmark
datasets with different types of corruptions and background separation
experiments on 3 video datasets show that our proposed model outperforms 10
state-of-the-art dimensionality reduction models. Our theoretical analysis
proves that the proposed model is able to recover approximate low-rank
representations with a bounded error for clusterable data.
The Utility of Hedged Assertions in the Emergence of Shared Categorical Labels
Authors: Martha Lewis, Jonathan Lawry
Date: 2016-01-25 00:00:00

We investigate the emergence of shared concepts in a community of language
users using a multi-agent simulation. We extend results showing that negated
assertions are of use in developing shared categories, to include assertions
modified by linguistic hedges. Results show that using hedged assertions
positively affects the emergence of shared categories in two distinct ways.
Firstly, using contraction hedges like `very' gives better convergence over
time. Secondly, using expansion hedges such as `quite' reduces concept overlap.
However, both these improvements come at a cost of slower speed of development.