pwc-archive/papers-with-abstracts huggingface dataset:
ALE: Additive Latent Effect Models for Grade Prediction
Authors: Zhiyun Ren, Xia Ning, Huzefa Rangwala
Date: 2018-01-17 00:00:00
Tasks: Prediction

The past decade has seen a growth in the development and deployment of
educational technologies for assisting college-going students in choosing
majors, selecting courses and acquiring feedback based on past academic
performance. Grade prediction methods seek to estimate a grade that a student
may achieve in a course that she may take in the future (e.g., next term).
Accurate and timely prediction of students' academic grades is important for
developing effective degree planners and early warning systems, and ultimately
improving educational outcomes. Existing grade pre- diction methods mostly
focus on modeling the knowledge components associated with each course and
student, and often overlook other factors such as the difficulty of each
knowledge component, course instructors, student interest, capabilities and
effort. In this paper, we propose additive latent effect models that
incorporate these factors to predict the student next-term grades.
Specifically, the proposed models take into account four factors: (i) student's
academic level, (ii) course instructors, (iii) student global latent factor,
and (iv) latent knowledge factors. We compared the new models with several
state-of-the-art methods on students of various characteristics (e.g., whether
a student transferred in or not). The experimental results demonstrate that the
proposed methods significantly outperform the baselines on grade prediction
problem. Moreover, we perform a thorough analysis on the importance of
different factors and how these factors can practically assist students in
course selection, and finally improve their academic performance.
Reinforcement Learning based Recommender System using Biclustering Technique
Authors: Sungwoon Choi, Heonseok Ha, Uiwon Hwang, Chanju Kim, Jung-Woo Ha, Sungroh Yoon
Date: 2018-01-17 00:00:00
Tasks: Recommendation Systems, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A recommender system aims to recommend items that a user is interested in
among many items. The need for the recommender system has been expanded by the
information explosion. Various approaches have been suggested for providing
meaningful recommendations to users. One of the proposed approaches is to
consider a recommender system as a Markov decision process (MDP) problem and
try to solve it using reinforcement learning (RL). However, existing RL-based
methods have an obvious drawback. To solve an MDP in a recommender system, they
encountered a problem with the large number of discrete actions that bring RL
to a larger class of problems. In this paper, we propose a novel RL-based
recommender system. We formulate a recommender system as a gridworld game by
using a biclustering technique that can reduce the state and action space
significantly. Using biclustering not only reduces space but also improves the
recommendation quality effectively handling the cold-start problem. In
addition, our approach can provide users with some explanation why the system
recommends certain items. Lastly, we examine the proposed algorithm on a
real-world dataset and achieve a better performance than the widely used
recommendation algorithm.
Effective Sequential Classifier Training for SVM-based Multitemporal Remote Sensing Image Classification
Authors: Yiqing Guo, Xiuping Jia, David Paull
Date: 2017-06-15 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Remote Sensing Image Classification

The explosive availability of remote sensing images has challenged supervised
classification algorithms such as Support Vector Machines (SVM), as training
samples tend to be highly limited due to the expensive and laborious task of
ground truthing. The temporal correlation and spectral similarity between
multitemporal images have opened up an opportunity to alleviate this problem.
In this study, a SVM-based Sequential Classifier Training (SCT-SVM) approach is
proposed for multitemporal remote sensing image classification. The approach
leverages the classifiers of previous images to reduce the required number of
training samples for the classifier training of an incoming image. For each
incoming image, a rough classifier is firstly predicted based on the temporal
trend of a set of previous classifiers. The predicted classifier is then
fine-tuned into a more accurate position with current training samples. This
approach can be applied progressively to sequential image data, with only a
small number of training samples being required from each image. Experiments
were conducted with Sentinel-2A multitemporal data over an agricultural area in
Australia. Results showed that the proposed SCT-SVM achieved better
classification accuracies compared with two state-of-the-art model transfer
algorithms. When training data are insufficient, the overall classification
accuracy of the incoming image was improved from 76.18% to 94.02% with the
proposed SCT-SVM, compared with those obtained without the assistance from
previous images. These results demonstrate that the leverage of a priori
information from previous images can provide advantageous assistance for later
images in multitemporal image classification.
Cahn--Hilliard inpainting with the double obstacle potential
Authors: Harald Garcke, Kei Fong Lam, Vanessa Styles
Date: 2018-01-17 00:00:00

The inpainting of damaged images has a wide range of applications, and many
different mathematical methods have been proposed to solve this problem.
Inpainting with the help of Cahn--Hilliard models has been particularly
successful, and it turns out that Cahn--Hilliard inpainting with the double
obstacle potential can lead to better results compared to inpainting with a
smooth double well potential. However, a mathematical analysis of this approach
is missing so far. In this paper we give first analytical results for a
Cahn--Hilliard double obstacle inpainting model regarding existence of global
solutions to the time-dependent problem and stationary solutions to the
time-independent problem without constraints on the parameters involved. With
the help of numerical results we show the effectiveness of the approach for
binary and grayscale images.
Identification of Seed Cells in Multispectral Images for GrowCut Segmentation
Authors: Wuilan Torres, Antonio Rueda-Toicen
Date: 2018-01-17 00:00:00
Tasks: General Classification, image-classification, Image Classification, Segmentation

The segmentation of satellite images is a necessary step to perform
object-oriented image classification, which has become relevant due to its
applicability on images with a high spatial resolution. To perform
object-oriented image classification, the studied image must first be segmented
in uniform regions. This segmentation requires manual work by an expert user,
who must exhaustively explore the image to establish thresholds that generate
useful and representative segments without oversegmenting and without
discarding representative segments. We propose a technique that automatically
segments the multispectral image while facing these issues. We identify in the
image homogenous zones according to their spectral signatures through the use
of morphological filters. These homogenous zones are representatives of
different types of land coverings in the image and are used as seeds for the
GrowCut multispectral segmentation algorithm. GrowCut is a cellular automaton
with competitive region growth, its cells are linked to every pixel in the
image through three parameters: the pixel's spectral signature, a label, and a
strength factor that represents the strength with which a cell defends its
label. The seed cells possess maximum strength and maintain their state
throughout the automaton's evolution. Starting from seed cells, each cell in
the image is iteratively attacked by its neighboring cells. When the automaton
stops updating its states, we obtain a segmented image where each pixel has
taken the label of one of its cells. In this paper the algorithm was applied in
an image acquired by Landsat8 on agricultural land of Calabozo, Guarico,
Venezuela where there are different types of land coverings: agriculture, urban
regions, water bodies, and savannas with different degrees of human
intervention. The segmentation obtained is presented as irregular polygons
enclosing geographical objects.
How to Train a CAT: Learning Canonical Appearance Transformations for Direct Visual Localization Under Illumination Change
Authors: Lee Clement, Jonathan Kelly
Date: 2017-09-09 00:00:00
Tasks: Decoder, Transfer Learning, Visual Localization, Visual Odometry

Direct visual localization has recently enjoyed a resurgence in popularity with the increasing availability of cheap mobile computing power. The competitive accuracy and robustness of these algorithms compared to state-of-the-art feature-based methods, as well as their natural ability to yield dense maps, makes them an appealing choice for a variety of mobile robotics applications. However, direct methods remain brittle in the face of appearance change due to their underlying assumption of photometric consistency, which is commonly violated in practice. In this paper, we propose to mitigate this problem by training deep convolutional encoder-decoder models to transform images of a scene such that they correspond to a previously-seen canonical appearance. We validate our method in multiple environments and illumination conditions using high-fidelity synthetic RGB-D datasets, and integrate the trained models into a direct visual localization pipeline, yielding improvements in visual odometry (VO) accuracy through time-varying illumination conditions, as well as improved metric relocalization performance under illumination change, where conventional methods normally fail. We further provide a preliminary investigation of transfer learning from synthetic to real environments in a localization context. An open-source implementation of our method using PyTorch is available at https://github.com/utiasSTARS/cat-net.
Deep Neural Networks for Survival Analysis Based on a Multi-Task Framework
Authors: Stephane Fotso
Date: 2018-01-17 00:00:00
Tasks: regression, Survival Analysis

Survival analysis/time-to-event models are extremely useful as they can help
companies predict when a customer will buy a product, churn or default on a
loan, and therefore help them improve their ROI. In this paper, we introduce a
new method to calculate survival functions using the Multi-Task Logistic
Regression (MTLR) model as its base and a deep learning architecture as its
core. Based on the Concordance index (C-index) and Brier score, this method
outperforms the MTLR in all the experiments disclosed in this paper as well as
the Cox Proportional Hazard (CoxPH) model when nonlinear dependencies are
found.
Automatic Classification of Music Genre using Masked Conditional Neural Networks
Authors: Fady Medhat, David Chesmore, John Robinson
Date: 2018-01-16 00:00:00
Tasks: Classification, General Classification

Neural network based architectures used for sound recognition are usually
adapted from other application domains such as image recognition, which may not
harness the time-frequency representation of a signal. The ConditionaL Neural
Networks (CLNN) and its extension the Masked ConditionaL Neural Networks
(MCLNN) are designed for multidimensional temporal signal recognition. The CLNN
is trained over a window of frames to preserve the inter-frame relation, and
the MCLNN enforces a systematic sparseness over the network's links that mimics
a filterbank-like behavior. The masking operation induces the network to learn
in frequency bands, which decreases the network susceptibility to
frequency-shifts in time-frequency representations. Additionally, the mask
allows an exploration of a range of feature combinations concurrently analogous
to the manual handcrafting of the optimum collection of features for a
recognition task. MCLNN have achieved competitive performance on the Ballroom
music dataset compared to several hand-crafted attempts and outperformed models
based on state-of-the-art Convolutional Neural Networks.
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Authors: Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, Igor Mordatch
Date: 2017-06-07 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Deep Reinforcement Learning, Multi-agent Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), SMAC+

We explore deep reinforcement learning methods for multi-agent domains. We begin by analyzing the difficulty of traditional algorithms in the multi-agent case: Q-learning is challenged by an inherent non-stationarity of the environment, while policy gradient suffers from a variance that increases as the number of agents grows. We then present an adaptation of actor-critic methods that considers action policies of other agents and is able to successfully learn policies that require complex multi-agent coordination. Additionally, we introduce a training regimen utilizing an ensemble of policies for each agent that leads to more robust multi-agent policies. We show the strength of our approach compared to existing methods in cooperative as well as competitive scenarios, where agent populations are able to discover various physical and informational coordination strategies.
Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses
Authors: Ryan Lowe, Michael Noseworthy, Iulian V. Serban, Nicolas Angelard-Gontier, Yoshua Bengio, Joelle Pineau
Date: 2017-08-23 00:00:00
Proceeding: ACL 2017 7
Tasks: Dialogue Evaluation

Automatically evaluating the quality of dialogue responses for unstructured
domains is a challenging problem. Unfortunately, existing automatic evaluation
metrics are biased and correlate very poorly with human judgements of response
quality. Yet having an accurate automatic evaluation procedure is crucial for
dialogue research, as it allows rapid prototyping and testing of new models
with fewer expensive human evaluations. In response to this challenge, we
formulate automatic dialogue evaluation as a learning problem. We present an
evaluation model (ADEM) that learns to predict human-like scores to input
responses, using a new dataset of human response scores. We show that the ADEM
model's predictions correlate significantly, and at a level much higher than
word-overlap metrics such as BLEU, with human judgements at both the utterance
and system-level. We also show that ADEM can generalize to evaluating dialogue
models unseen during training, an important step for automatic dialogue
evaluation.
A deep generative model for gene expression profiles from single-cell RNA sequencing
Authors: Romain Lopez, Jeffrey Regier, Michael Cole, Michael Jordan, Nir Yosef
Date: 2017-09-07 00:00:00
Tasks: Stochastic Optimization, Variational Inference

We propose a probabilistic model for interpreting gene expression levels that
are observed through single-cell RNA sequencing. In the model, each cell has a
low-dimensional latent representation. Additional latent variables account for
technical effects that may erroneously set some observations of gene expression
levels to zero. Conditional distributions are specified by neural networks,
giving the proposed model enough flexibility to fit the data well. We use
variational inference and stochastic optimization to approximate the posterior
distribution. The inference procedure scales to over one million cells, whereas
competing algorithms do not. Even for smaller datasets, for several tasks, the
proposed procedure outperforms state-of-the-art methods like ZIFA and
ZINB-WaVE. We also extend our framework to account for batch effects and other
confounding factors, and propose a Bayesian hypothesis test for differential
expression that outperforms DESeq2.
Cellular-Connected UAVs over 5G: Deep Reinforcement Learning for Interference Management
Authors: Ursula Challita, Walid Saad, Christian Bettstetter
Date: 2018-01-16 00:00:00
Tasks: Deep Reinforcement Learning, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, an interference-aware path planning scheme for a network of
cellular-connected unmanned aerial vehicles (UAVs) is proposed. In particular,
each UAV aims at achieving a tradeoff between maximizing energy efficiency and
minimizing both wireless latency and the interference level caused on the
ground network along its path. The problem is cast as a dynamic game among
UAVs. To solve this game, a deep reinforcement learning algorithm, based on
echo state network (ESN) cells, is proposed. The introduced deep ESN
architecture is trained to allow each UAV to map each observation of the
network state to an action, with the goal of minimizing a sequence of
time-dependent utility functions. Each UAV uses ESN to learn its optimal path,
transmission power level, and cell association vector at different locations
along its path. The proposed algorithm is shown to reach a subgame perfect Nash
equilibrium (SPNE) upon convergence. Moreover, an upper and lower bound for the
altitude of the UAVs is derived thus reducing the computational complexity of
the proposed algorithm. Simulation results show that the proposed scheme
achieves better wireless latency per UAV and rate per ground user (UE) while
requiring a number of steps that is comparable to a heuristic baseline that
considers moving via the shortest distance towards the corresponding
destinations. The results also show that the optimal altitude of the UAVs
varies based on the ground network density and the UE data rate requirements
and plays a vital role in minimizing the interference level on the ground UEs
as well as the wireless transmission delay of the UAV.
Sparse quadratic classification rules via linear dimension reduction
Authors: Irina Gaynanova, Tianying Wang
Date: 2017-11-13 00:00:00
Tasks: Classification, Dimensionality Reduction, General Classification, Variable Selection

We consider the problem of high-dimensional classification between the two
groups with unequal covariance matrices. Rather than estimating the full
quadratic discriminant rule, we propose to perform simultaneous variable
selection and linear dimension reduction on original data, with the subsequent
application of quadratic discriminant analysis on the reduced space. In
contrast to quadratic discriminant analysis, the proposed framework doesn't
require estimation of precision matrices and scales linearly with the number of
measurements, making it especially attractive for the use on high-dimensional
datasets. We support the methodology with theoretical guarantees on variable
selection consistency, and empirical comparison with competing approaches. We
apply the method to gene expression data of breast cancer patients, and confirm
the crucial importance of ESR1 gene in differentiating estrogen receptor
status.
StackSeq2Seq: Dual Encoder Seq2Seq Recurrent Networks
Authors: Alessandro Bay, Biswa Sengupta
Date: 2017-10-11 00:00:00
Tasks: Translation

A widely studied non-deterministic polynomial time (NP) hard problem lies in
finding a route between the two nodes of a graph. Often meta-heuristics
algorithms such as $A^{*}$ are employed on graphs with a large number of nodes.
Here, we propose a deep recurrent neural network architecture based on the
Sequence-2-Sequence (Seq2Seq) model, widely used, for instance in text
translation. Particularly, we illustrate that utilising a context vector that
has been learned from two different recurrent networks enables increased
accuracies in learning the shortest route of a graph. Additionally, we show
that one can boost the performance of the Seq2Seq network by smoothing the loss
function using a homotopy continuation of the decoder's loss function.
Convolutional Networks in Visual Environments
Authors: Alessandro Betti, Marco Gori
Date: 2018-01-16 00:00:00
Tasks: Rhythm

The puzzle of computer vision might find new challenging solutions when we
realize that most successful methods are working at image level, which is
remarkably more difficult than processing directly visual streams. In this
paper, we claim that their processing naturally leads to formulate the motion
invariance principle, which enables the construction of a new theory of
learning with convolutional networks. The theory addresses a number of
intriguing questions that arise in natural vision, and offers a well-posed
computational scheme for the discovery of convolutional filters over the
retina. They are driven by differential equations derived from the principle of
least cognitive action. Unlike traditional convolutional networks, which need
massive supervision, the proposed theory offers a truly new scenario in which
feature learning takes place by unsupervised processing of video signals. It is
pointed out that an opportune blurring of the video, along the interleaving of
segments of null signal, make it possible to conceive a novel learning
mechanism that yields the minimum of the cognitive action. Basically, while the
theory enables the implementation of novel computer vision systems, it is also
provides an intriguing explanation of the solution that evolution has
discovered for humans, where it looks like that the video blurring in newborns
and the day-night rhythm seem to emerge in a general computational framework,
regardless of biology.
Application of Convolutional Neural Network to Predict Airfoil Lift Coefficient
Authors: Yao Zhang, Woong-Je Sung, Dimitri Mavris
Date: 2017-12-29 00:00:00

The adaptability of the convolutional neural network (CNN) technique for
aerodynamic meta-modeling tasks is probed in this work. The primary objective
is to develop suitable CNN architecture for variable flow conditions and object
geometry, in addition to identifying a sufficient data preparation process.
Multiple CNN structures were trained to learn the lift coefficients of the
airfoils with a variety of shapes in multiple flow Mach numbers, Reynolds
numbers, and diverse angles of attack. This is conducted to illustrate the
concept of the technique. A multi-layered perceptron (MLP) is also used for the
training sets. The MLP results are compared with that of the CNN results. The
newly proposed meta-modeling concept has been found to be comparable with the
MLP in learning capability; and more importantly, our CNN model exhibits a
competitive prediction accuracy with minimal constraints in a geometric
representation.
The Role of Conditional Independence in the Evolution of Intelligent Systems
Authors: Jory Schossau, Larissa Albantakis, Arend Hintze
Date: 2018-01-16 00:00:00

Systems are typically made from simple components regardless of their
complexity. While the function of each part is easily understood, higher order
functions are emergent properties and are notoriously difficult to explain. In
networked systems, both digital and biological, each component receives inputs,
performs a simple computation, and creates an output. When these components
have multiple outputs, we intuitively assume that the outputs are causally
dependent on the inputs but are themselves independent of each other given the
state of their shared input. However, this intuition can be violated for
components with probabilistic logic, as these typically cannot be decomposed
into separate logic gates with one output each. This violation of conditional
independence on the past system state is equivalent to instantaneous
interaction --- the idea is that some information between the outputs is not
coming from the inputs and thus must have been created instantaneously. Here we
compare evolved artificial neural systems with and without instantaneous
interaction across several task environments. We show that systems without
instantaneous interactions evolve faster, to higher final levels of
performance, and require fewer logic components to create a densely connected
cognitive machinery.