pwc-archive/papers-with-abstracts huggingface dataset:
A Simple Exponential Family Framework for Zero-Shot Learning
Authors: Vinay Kumar Verma, Piyush Rai
Date: 2017-07-25 00:00:00
Tasks: Attribute, Few-Shot Learning, Zero-Shot Learning

We present a simple generative framework for learning to predict previously
unseen classes, based on estimating class-attribute-gated class-conditional
distributions. We model each class-conditional distribution as an exponential
family distribution and the parameters of the distribution of each seen/unseen
class are defined as functions of the respective observed class attributes.
These functions can be learned using only the seen class data and can be used
to predict the parameters of the class-conditional distribution of each unseen
class. Unlike most existing methods for zero-shot learning that represent
classes as fixed embeddings in some vector space, our generative model
naturally represents each class as a probability distribution. It is simple to
implement and also allows leveraging additional unlabeled data from unseen
classes to improve the estimates of their class-conditional distributions using
transductive/semi-supervised learning. Moreover, it extends seamlessly to
few-shot learning by easily updating these distributions when provided with a
small number of additional labelled examples from unseen classes. Through a
comprehensive set of experiments on several benchmark data sets, we demonstrate
the efficacy of our framework.
A New Backpropagation Algorithm without Gradient Descent
Authors: Varun Ranganathan, S. Natarajan
Date: 2018-01-25 00:00:00

The backpropagation algorithm, which had been originally introduced in the
1970s, is the workhorse of learning in neural networks. This backpropagation
algorithm makes use of the famous machine learning algorithm known as Gradient
Descent, which is a first-order iterative optimization algorithm for finding
the minimum of a function. To find a local minimum of a function using gradient
descent, one takes steps proportional to the negative of the gradient (or of
the approximate gradient) of the function at the current point. In this paper,
we develop an alternative to the backpropagation without the use of the
Gradient Descent Algorithm, but instead we are going to devise a new algorithm
to find the error in the weights and biases of an artificial neuron using
Moore-Penrose Pseudo Inverse. The numerical studies and the experiments
performed on various datasets are used to verify the working of this
alternative algorithm.
Nonparametric Hawkes Processes: Online Estimation and Generalization Bounds
Authors: Yingxiang Yang, Jalal Etesami, Niao He, Negar Kiyavash
Date: 2018-01-25 00:00:00
Tasks: Generalization Bounds

In this paper, we design a nonparametric online algorithm for estimating the
triggering functions of multivariate Hawkes processes. Unlike parametric
estimation, where evolutionary dynamics can be exploited for fast computation
of the gradient, and unlike typical function learning, where representer
theorem is readily applicable upon proper regularization of the objective
function, nonparametric estimation faces the challenges of (i) inefficient
evaluation of the gradient, (ii) lack of representer theorem, and (iii)
computationally expensive projection necessary to guarantee positivity of the
triggering functions. In this paper, we offer solutions to the above
challenges, and design an online estimation algorithm named NPOLE-MHP that
outputs estimations with a $\mathcal{O}(1/T)$ regret, and a $\mathcal{O}(1/T)$
stability. Furthermore, we design an algorithm, NPOLE-MMHP, for estimation of
multivariate marked Hawkes processes. We test the performance of NPOLE-MHP on
various synthetic and real datasets, and demonstrate, under different
evaluation metrics, that NPOLE-MHP performs as good as the optimal maximum
likelihood estimation (MLE), while having a run time as little as parametric
online algorithms.
A Tutorial on Modeling and Inference in Undirected Graphical Models for Hyperspectral Image Analysis
Authors: Utsav B. Gewali, Sildomar T. Monteiro
Date: 2018-01-25 00:00:00
Tasks: Hyperspectral image analysis

Undirected graphical models have been successfully used to jointly model the
spatial and the spectral dependencies in earth observing hyperspectral images.
They produce less noisy, smooth, and spatially coherent land cover maps and
give top accuracies on many datasets. Moreover, they can easily be combined
with other state-of-the-art approaches, such as deep learning. This has made
them an essential tool for remote sensing researchers and practitioners.
However, graphical models have not been easily accessible to the larger remote
sensing community as they are not discussed in standard remote sensing
textbooks and not included in the popular remote sensing software and
toolboxes. In this tutorial, we provide a theoretical introduction to Markov
random fields and conditional random fields based spatial-spectral
classification for land cover mapping along with a detailed step-by-step
practical guide on applying these methods using freely available software.
Furthermore, the discussed methods are benchmarked on four public hyperspectral
datasets for a fair comparison among themselves and easy comparison with the
vast number of methods in literature which use the same datasets. The source
code necessary to reproduce all the results in the paper is published on-line
to make it easier for the readers to apply these techniques to different remote
sensing problems.
Visual Weather Temperature Prediction
Authors: Wei-Ta Chu, Kai-Chia Ho, Ali Borji
Date: 2018-01-25 00:00:00
Tasks: Prediction

In this paper, we attempt to employ convolutional recurrent neural networks
for weather temperature estimation using only image data. We study ambient
temperature estimation based on deep neural networks in two scenarios a)
estimating temperature of a single outdoor image, and b) predicting temperature
of the last image in an image sequence. In the first scenario, visual features
are extracted by a convolutional neural network trained on a large-scale image
dataset. We demonstrate that promising performance can be obtained, and analyze
how volume of training data influences performance. In the second scenario, we
consider the temporal evolution of visual appearance, and construct a recurrent
neural network to predict the temperature of the last image in a given image
sequence. We obtain better prediction accuracy compared to the state-of-the-art
models. Further, we investigate how performance varies when information is
extracted from different scene regions, and when images are captured in
different daytime hours. Our approach further reinforces the idea of using only
visual information for cost efficient weather prediction in the future.
A Hilbert Space of Stationary Ergodic Processes
Authors: Ishanu Chattopadhyay
Date: 2018-01-25 00:00:00
Tasks: Clustering, Robust classification, valid

Identifying meaningful signal buried in noise is a problem of interest
arising in diverse scenarios of data-driven modeling. We present here a
theoretical framework for exploiting intrinsic geometry in data that resists
noise corruption, and might be identifiable under severe obfuscation. Our
approach is based on uncovering a valid complete inner product on the space of
ergodic stationary finite valued processes, providing the latter with the
structure of a Hilbert space on the real field. This rigorous construction,
based on non-standard generalizations of the notions of sum and scalar
multiplication of finite dimensional probability vectors, allows us to
meaningfully talk about "angles" between data streams and data sources, and,
make precise the notion of orthogonal stochastic processes. In particular, the
relative angles appear to be preserved, and identifiable, under severe noise,
and will be developed in future as the underlying principle for robust
classification, clustering and unsupervised featurization algorithms.
Personalized Human Activity Recognition Using Convolutional Neural Networks
Authors: Seyed Ali Rokni, Marjan Nourollahi, Hassan Ghasemzadeh
Date: 2018-01-25 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Transfer Learning

A major barrier to the personalized Human Activity Recognition using wearable
sensors is that the performance of the recognition model drops significantly
upon adoption of the system by new users or changes in physical/ behavioral
status of users. Therefore, the model needs to be retrained by collecting new
labeled data in the new context. In this study, we develop a transfer learning
framework using convolutional neural networks to build a personalized activity
recognition model with minimal user supervision.
A mullti- or many- objective evolutionary algorithm with global loop update
Authors: Yingyu Zhang, Bing Zeng, Yuanzhen Li, Junqing Li
Date: 2018-01-25 00:00:00
Tasks: Diversity, Evolutionary Algorithms

Multi- or many-objective evolutionary algorithm- s(MOEAs), especially the
decomposition-based MOEAs have been widely concerned in recent years. The
decomposition-based MOEAs emphasize convergence and diversity in a simple model
and have made a great success in dealing with theoretical and practical multi-
or many-objective optimization problems. In this paper, we focus on update
strategies of the decomposition- based MOEAs, and their criteria for comparing
solutions. Three disadvantages of the decomposition-based MOEAs with local
update strategies and several existing criteria for comparing solutions are
analyzed and discussed. And a global loop update strategy and two hybrid
criteria are suggested. Subsequently, an evolutionary algorithm with the global
loop update is implement- ed and compared to several of the best multi- or
many-objective optimization algorithms on two famous unconstraint test suites
with up to 15 objectives. Experimental results demonstrate that unlike
evolutionary algorithms with local update strategies, the population of our
algorithm does not degenerate at any generation of its evolution, which
guarantees the diversity of the resulting population. In addition, our
algorithm wins in most instances of the two test suites, indicating that it is
very compet- itive in terms of convergence and diversity. Running results of
our algorithm with different criteria for comparing solutions are also
compared. Their differences are very significant, indicating that the
performance of our algorithm is affected by the criterion it adopts.
Provably Correct Algorithms for Matrix Column Subset Selection with Selectively Sampled Data
Authors: Yining Wang, Aarti Singh
Date: 2015-05-17 00:00:00
Tasks: Computational Efficiency, Recommendation Systems

We consider the problem of matrix column subset selection, which selects a
subset of columns from an input matrix such that the input can be well
approximated by the span of the selected columns. Column subset selection has
been applied to numerous real-world data applications such as population
genetics summarization, electronic circuits testing and recommendation systems.
In many applications the complete data matrix is unavailable and one needs to
select representative columns by inspecting only a small portion of the input
matrix. In this paper we propose the first provably correct column subset
selection algorithms for partially observed data matrices. Our proposed
algorithms exhibit different merits and limitations in terms of statistical
accuracy, computational efficiency, sample complexity and sampling schemes,
which provides a nice exploration of the tradeoff between these desired
properties for column subset selection. The proposed methods employ the idea of
feedback driven sampling and are inspired by several sampling schemes
previously introduced for low-rank matrix approximation tasks (Drineas et al.,
2008; Frieze et al., 2004; Deshpande and Vempala, 2006; Krishnamurthy and
Singh, 2014). Our analysis shows that, under the assumption that the input data
matrix has incoherent rows but possibly coherent columns, all algorithms
provably converge to the best low-rank approximation of the original data as
number of selected columns increases. Furthermore, two of the proposed
algorithms enjoy a relative error bound, which is preferred for column subset
selection and matrix approximation purposes. We also demonstrate through both
theoretical and empirical analysis the power of feedback driven sampling
compared to uniform random sampling on input matrices with highly correlated
columns.
When Vehicles See Pedestrians with Phones:A Multi-Cue Framework for Recognizing Phone-based Activities of Pedestrians
Authors: Akshay Rangesh, Mohan M. Trivedi
Date: 2018-01-24 00:00:00
Tasks: Activity Recognition, Decision Making, Pose Estimation

The intelligent vehicle community has devoted considerable efforts to model
driver behavior, and in particular to detect and overcome driver distraction in
an effort to reduce accidents caused by driver negligence. However, as the
domain increasingly shifts towards autonomous and semi-autonomous solutions,
the driver is no longer integral to the decision making process, indicating a
need to refocus efforts elsewhere. To this end, we propose to study pedestrian
distraction instead. In particular, we focus on detecting pedestrians who are
engaged in secondary activities involving their cellphones and similar handheld
multimedia devices from a purely vision-based standpoint. To achieve this
objective, we propose a pipeline incorporating articulated human pose
estimation, followed by a soft object label transfer from an ensemble of
exemplar SVMs trained on the nearest neighbors in pose feature space. We
additionally incorporate head gaze features and prior pose information to carry
out cellphone related pedestrian activity recognition. Finally, we offer a
method to reliably track the articulated pose of a pedestrian through a
sequence of images using a particle filter with a Gaussian Process Dynamical
Model (GPDM), which can then be used to estimate sequentially varying activity
scores at a very low computational cost. The entire framework is fast
(especially for sequential data) and accurate, and easily extensible to include
other secondary activities and sources of distraction.
Deep Interactive Evolution
Authors: Philip Bontrager, Wending Lin, Julian Togelius, Sebastian Risi
Date: 2018-01-24 00:00:00
Tasks: Image Generation, valid

This paper describes an approach that combines generative adversarial
networks (GANs) with interactive evolutionary computation (IEC). While GANs can
be trained to produce lifelike images, they are normally sampled randomly from
the learned distribution, providing limited control over the resulting output.
On the other hand, interactive evolution has shown promise in creating various
artifacts such as images, music and 3D objects, but traditionally relies on a
hand-designed evolvable representation of the target domain. The main insight
in this paper is that a GAN trained on a specific target domain can act as a
compact and robust genotype-to-phenotype mapping (i.e. most produced phenotypes
do resemble valid domain artifacts). Once such a GAN is trained, the latent
vector given as input to the GAN's generator network can be put under
evolutionary control, allowing controllable and high-quality image generation.
In this paper, we demonstrate the advantage of this novel approach through a
user study in which participants were able to evolve images that strongly
resemble specific target images.
Matrix Completion with Nonconvex Regularization: Spectral Operators and Scalable Algorithms
Authors: Rahul Mazumder, Diego F. Saldana, Haolei Weng
Date: 2018-01-24 00:00:00
Tasks: Matrix Completion

In this paper, we study the popularly dubbed matrix completion problem, where the task is to "fill in" the unobserved entries of a matrix from a small subset of observed entries, under the assumption that the underlying matrix is of low-rank. Our contributions herein, enhance our prior work on nuclear norm regularized problems for matrix completion (Mazumder et al., 2010) by incorporating a continuum of nonconvex penalty functions between the convex nuclear norm and nonconvex rank functions. Inspired by SOFT-IMPUTE (Mazumder et al., 2010; Hastie et al., 2016), we propose NC-IMPUTE- an EM-flavored algorithmic framework for computing a family of nonconvex penalized matrix completion problems with warm-starts. We present a systematic study of the associated spectral thresholding operators, which play an important role in the overall algorithm. We study convergence properties of the algorithm. Using structured low-rank SVD computations, we demonstrate the computational scalability of our proposal for problems up to the Netflix size (approximately, a $500,000 \times 20, 000$ matrix with $10^8$ observed entries). We demonstrate that on a wide range of synthetic and real data instances, our proposed nonconvex regularization framework leads to low-rank solutions with better predictive performance when compared to those obtained from nuclear norm problems. Implementations of algorithms proposed herein, written in the R programming language, are made available on github.
Active Neural Localization
Authors: Devendra Singh Chaplot, Emilio Parisotto, Ruslan Salakhutdinov
Date: 2018-01-24 00:00:00
Proceeding: ICLR 2018 1
Tasks: FPS Games, Game of Doom, Reinforcement Learning

Localization is the problem of estimating the location of an autonomous agent
from an observation and a map of the environment. Traditional methods of
localization, which filter the belief based on the observations, are
sub-optimal in the number of steps required, as they do not decide the actions
taken by the agent. We propose "Active Neural Localizer", a fully
differentiable neural network that learns to localize accurately and
efficiently. The proposed model incorporates ideas of traditional
filtering-based localization methods, by using a structured belief of the state
with multiplicative interactions to propagate belief, and combines it with a
policy model to localize accurately while minimizing the number of steps
required for localization. Active Neural Localizer is trained end-to-end with
reinforcement learning. We use a variety of simulation environments for our
experiments which include random 2D mazes, random mazes in the Doom game engine
and a photo-realistic environment in the Unreal game engine. The results on the
2D environments show the effectiveness of the learned policy in an idealistic
setting while results on the 3D environments demonstrate the model's capability
of learning the policy and perceptual model jointly from raw-pixel based RGB
observations. We also show that a model trained on random textures in the Doom
environment generalizes well to a photo-realistic office space environment in
the Unreal engine.
Multi-optional Many-sorted Past Present Future structures and its description
Authors: Sergio Miguel Tomé
Date: 2018-01-24 00:00:00

The cognitive theory of true conditions (CTTC) is a proposal to describe the
model-theoretic semantics of symbolic cognitive architectures and design the
implementation of cognitive abilities. The CTTC is formulated mathematically
using the multi-optional many-sorted past present future(MMPPF) structures.
This article defines mathematically the MMPPF structures and the formal
languages proposed to describe them by the CTTC.
Optimal transport maps for distribution preserving operations on latent spaces of Generative Models
Authors: Eirikur Agustsson, Alexander Sage, Radu Timofte, Luc van Gool
Date: 2017-11-06 00:00:00
Proceeding: ICLR 2018 1

Generative models such as Variational Auto Encoders (VAEs) and Generative
Adversarial Networks (GANs) are typically trained for a fixed prior
distribution in the latent space, such as uniform or Gaussian. After a trained
model is obtained, one can sample the Generator in various forms for
exploration and understanding, such as interpolating between two samples,
sampling in the vicinity of a sample or exploring differences between a pair of
samples applied to a third sample. In this paper, we show that the latent space
operations used in the literature so far induce a distribution mismatch between
the resulting outputs and the prior distribution the model was trained on. To
address this, we propose to use distribution matching transport maps to ensure
that such latent space operations preserve the prior distribution, while
minimally modifying the original operation. Our experimental results validate
that the proposed operations give higher quality samples compared to the
original operations.
Development and application of a machine learning supported methodology for measurement and verification (M&V) 2.0
Authors: Colm V. Gallagher, Kevin Leahy, Peter O'Donovan, Ken Bruton, Dominic T. J. O'Sullivan
Date: 2018-01-24 00:00:00
Tasks: BIG-bench Machine Learning, feature selection, regression

The foundations of all methodologies for the measurement and verification
(M&V) of energy savings are based on the same five key principles: accuracy,
completeness, conservatism, consistency and transparency. The most widely
accepted methodologies tend to generalise M&V so as to ensure applicability
across the spectrum of energy conservation measures (ECM's). These do not
provide a rigid calculation procedure to follow. This paper aims to bridge the
gap between high-level methodologies and the practical application of modelling
algorithms, with a focus on the industrial buildings sector. This is achieved
with the development of a novel, machine learning supported methodology for M&V
2.0 which enables accurate quantification of savings.
  A novel and computationally efficient feature selection algorithm and
powerful machine learning regression algorithms are employed to maximise the
effectiveness of available data. The baseline period energy consumption is
modelled using artificial neural networks, support vector machines, k-nearest
neighbours and multiple ordinary least squares regression. Improved knowledge
discovery and an expanded boundary of analysis allow more complex energy
systems be analysed, thus increasing the applicability of M&V. A case study in
a large biomedical manufacturing facility is used to demonstrate the
methodology's ability to accurately quantify the savings under real-world
conditions. The ECM was found to result in 604,527 kWh of energy savings with
57% uncertainty at a confidence interval of 68%. 20 baseline energy models are
developed using an exhaustive approach with the optimal model being used to
quantify savings. The range of savings estimated with each model are presented
and the acceptability of uncertainty is reviewed. The case study demonstrates
the ability of the methodology to perform M&V to an acceptable standard in
challenging circumstances.
Non-parametric Sparse Additive Auto-regressive Network Models
Authors: Hao Henry Zhou, Garvesh Raskutti
Date: 2018-01-23 00:00:00
Tasks: Time Series, Time Series Analysis

Consider a multi-variate time series $(X_t)_{t=0}^{T}$ where $X_t \in
\mathbb{R}^d$ which may represent spike train responses for multiple neurons in
a brain, crime event data across multiple regions, and many others. An
important challenge associated with these time series models is to estimate an
influence network between the $d$ variables, especially when the number of
variables $d$ is large meaning we are in the high-dimensional setting. Prior
work has focused on parametric vector auto-regressive models. However,
parametric approaches are somewhat restrictive in practice. In this paper, we
use the non-parametric sparse additive model (SpAM) framework to address this
challenge. Using a combination of $\beta$ and $\phi$-mixing properties of
Markov chains and empirical process techniques for reproducing kernel Hilbert
spaces (RKHSs), we provide upper bounds on mean-squared error in terms of the
sparsity $s$, logarithm of the dimension $\log d$, number of time points $T$,
and the smoothness of the RKHSs. Our rates are sharp up to logarithm factors in
many cases. We also provide numerical experiments that support our theoretical
results and display potential advantages of using our non-parametric SpAM
framework for a Chicago crime dataset.