pwc-archive/papers-with-abstracts huggingface dataset:
Cooperative Multi-Agent Reinforcement Learning for Low-Level Wireless Communication
Authors: Colin de Vrieze, Shane Barratt, Daniel Tsai, Anant Sahai
Date: 2018-01-14 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Traditional radio systems are strictly co-designed on the lower levels of the
OSI stack for compatibility and efficiency. Although this has enabled the
success of radio communications, it has also introduced lengthy standardization
processes and imposed static allocation of the radio spectrum. Various
initiatives have been undertaken by the research community to tackle the
problem of artificial spectrum scarcity by both making frequency allocation
more dynamic and building flexible radios to replace the static ones. There is
reason to believe that just as computer vision and control have been overhauled
by the introduction of machine learning, wireless communication can also be
improved by utilizing similar techniques to increase the flexibility of
wireless networks. In this work, we pose the problem of discovering low-level
wireless communication schemes ex-nihilo between two agents in a fully
decentralized fashion as a reinforcement learning problem. Our proposed
approach uses policy gradients to learn an optimal bi-directional communication
scheme and shows surprisingly sophisticated and intelligent learning behavior.
We present the results of extensive experiments and an analysis of the fidelity
of our approach.
A Bio-inspired Collision Detecotr for Small Quadcopter
Authors: Jiannan Zhao, Cheng Hu, Chun Zhang, Zhihua Wang, Shigang Yue
Date: 2018-01-14 00:00:00
Tasks: Collision Avoidance

Sense and avoid capability enables insects to fly versatilely and robustly in
dynamic complex environment. Their biological principles are so practical and
efficient that inspired we human imitating them in our flying machines. In this
paper, we studied a novel bio-inspired collision detector and its application
on a quadcopter. The detector is inspired from LGMD neurons in the locusts, and
modeled into an STM32F407 MCU. Compared to other collision detecting methods
applied on quadcopters, we focused on enhancing the collision selectivity in a
bio-inspired way that can considerably increase the computing efficiency during
an obstacle detecting task even in complex dynamic environment. We designed the
quadcopter's responding operation imminent collisions and tested this
bio-inspired system in an indoor arena. The observed results from the
experiments demonstrated that the LGMD collision detector is feasible to work
as a vision module for the quadcopter's collision avoidance task.
Using accumulation to optimize deep residual neural nets
Authors: Yatin Saraiya
Date: 2018-01-14 00:00:00
Tasks: All

Residual Neural Networks [1] won first place in all five main tracks of the
ImageNet and COCO 2015 competitions. This kind of network involves the creation
of pluggable modules such that the output contains a residual from the input.
The residual in that paper is the identity function. We propose to include
residuals from all lower layers, suitably normalized, to create the residual.
This way, all previous layers contribute equally to the output of a layer. We
show that our approach is an improvement on [1] for the CIFAR-10 dataset.
Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep Neural Networks and Cascaded Regression
Authors: Guanjun Guo, Hanzi Wang, Chunhua Shen, Yan Yan, Hong-Yuan Mark Liao
Date: 2017-12-25 00:00:00
Tasks: Image Cropping, regression

Despite recent progress, computational visual aesthetic is still challenging.
Image cropping, which refers to the removal of unwanted scene areas, is an
important step to improve the aesthetic quality of an image. However, it is
challenging to evaluate whether cropping leads to aesthetically pleasing
results because the assessment is typically subjective. In this paper, we
propose a novel cascaded cropping regression (CCR) method to perform image
cropping by learning the knowledge from professional photographers. The
proposed CCR method improves the convergence speed of the cascaded method,
which directly uses random-ferns regressors. In addition, a two-step learning
strategy is proposed and used in the CCR method to address the problem of
lacking labelled cropping data. Specifically, a deep convolutional neural
network (CNN) classifier is first trained on large-scale visual aesthetic
datasets. The deep CNN model is then designed to extract features from several
image cropping datasets, upon which the cropping bounding boxes are predicted
by the proposed CCR method. Experimental results on public image cropping
datasets demonstrate that the proposed method significantly outperforms several
state-of-the-art image cropping methods.
A Gentle Tutorial of Recurrent Neural Network with Error Backpropagation
Authors: Gang Chen
Date: 2016-10-08 00:00:00
Tasks: Handwriting Recognition, Image to text, speech-recognition, Speech Recognition

We describe recurrent neural networks (RNNs), which have attracted great
attention on sequential tasks, such as handwriting recognition, speech
recognition and image to text. However, compared to general feedforward neural
networks, RNNs have feedback loops, which makes it a little hard to understand
the backpropagation step. Thus, we focus on basics, especially the error
backpropagation to compute gradients with respect to model parameters. Further,
we go into detail on how error backpropagation algorithm is applied on long
short-term memory (LSTM) by unfolding the memory unit.
Accelerated Alternating Projections for Robust Principal Component Analysis
Authors: HanQin Cai, Jian-Feng Cai, Ke Wei
Date: 2017-11-15 00:00:00
Tasks: Computational Efficiency

We study robust PCA for the fully observed setting, which is about separating
a low rank matrix $\boldsymbol{L}$ and a sparse matrix $\boldsymbol{S}$ from
their sum $\boldsymbol{D}=\boldsymbol{L}+\boldsymbol{S}$. In this paper, a new
algorithm, dubbed accelerated alternating projections, is introduced for robust
PCA which significantly improves the computational efficiency of the existing
alternating projections proposed in [Netrapalli, Praneeth, et al., 2014] when
updating the low rank factor. The acceleration is achieved by first projecting
a matrix onto some low dimensional subspace before obtaining a new estimate of
the low rank matrix via truncated SVD. Exact recovery guarantee has been
established which shows linear convergence of the proposed algorithm. Empirical
performance evaluations establish the advantage of our algorithm over other
state-of-the-art algorithms for robust PCA.
Multivariate LSTM-FCNs for Time Series Classification
Authors: Fazle Karim, Somshubra Majumdar, Houshang Darabi, Samuel Harford
Date: 2018-01-14 00:00:00
Tasks: Action Recognition, Activity Recognition, General Classification, Temporal Action Localization, Time Series, Time Series Analysis, Time Series Classification

Over the past decade, multivariate time series classification has received great attention. We propose transforming the existing univariate time series classification models, the Long Short Term Memory Fully Convolutional Network (LSTM-FCN) and Attention LSTM-FCN (ALSTM-FCN), into a multivariate time series classification model by augmenting the fully convolutional block with a squeeze-and-excitation block to further improve accuracy. Our proposed models outperform most state-of-the-art models while requiring minimum preprocessing. The proposed models work efficiently on various complex multivariate time series classification tasks such as activity recognition or action recognition. Furthermore, the proposed models are highly efficient at test time and small enough to deploy on memory constrained systems.
An Explicit Convergence Rate for Nesterov's Method from SDP
Authors: Sam Safavi, Bikash Joshi, Guilherme França, José Bento
Date: 2018-01-13 00:00:00

The framework of Integral Quadratic Constraints (IQC) introduced by Lessard
et al. (2014) reduces the computation of upper bounds on the convergence rate
of several optimization algorithms to semi-definite programming (SDP). In
particular, this technique was applied to Nesterov's accelerated method (NAM).
For quadratic functions, this SDP was explicitly solved leading to a new bound
on the convergence rate of NAM, and for arbitrary strongly convex functions it
was shown numerically that IQC can improve bounds from Nesterov (2004).
Unfortunately, an explicit analytic solution to the SDP was not provided. In
this paper, we provide such an analytical solution, obtaining a new general and
explicit upper bound on the convergence rate of NAM, which we further optimize
over its parameters. To the best of our knowledge, this is the best, and
explicit, upper bound on the convergence rate of NAM for strongly convex
functions.
A Review of 40 Years of Cognitive Architecture Research: Core Cognitive Abilities and Practical Applications
Authors: Iuliia Kotseruba, John K. Tsotsos
Date: 2016-10-27 00:00:00

In this paper we present a broad overview of the last 40 years of research on
cognitive architectures. Although the number of existing architectures is
nearing several hundred, most of the existing surveys do not reflect this
growth and focus on a handful of well-established architectures. Thus, in this
survey we wanted to shift the focus towards a more inclusive and high-level
overview of the research on cognitive architectures. Our final set of 84
architectures includes 49 that are still actively developed, and borrow from a
diverse set of disciplines, spanning areas from psychoanalysis to neuroscience.
To keep the length of this paper within reasonable limits we discuss only the
core cognitive abilities, such as perception, attention mechanisms, action
selection, memory, learning and reasoning. In order to assess the breadth of
practical applications of cognitive architectures we gathered information on
over 900 practical projects implemented using the cognitive architectures in
our list. We use various visualization techniques to highlight overall trends
in the development of the field. In addition to summarizing the current
state-of-the-art in the cognitive architecture research, this survey describes
a variety of methods and ideas that have been tried and their relative success
in modeling human cognitive abilities, as well as which aspects of cognitive
behavior need more research with respect to their mechanistic counterparts and
thus can further inform how cognitive science might progress.
Phase Retrieval via Randomized Kaczmarz: Theoretical Guarantees
Authors: Yan Shuo Tan, Roman Vershynin
Date: 2017-06-30 00:00:00
Tasks: Retrieval

We consider the problem of phase retrieval, i.e. that of solving systems of
quadratic equations. A simple variant of the randomized Kaczmarz method was
recently proposed for phase retrieval, and it was shown numerically to have a
computational edge over state-of-the-art Wirtinger flow methods. In this paper,
we provide the first theoretical guarantee for the convergence of the
randomized Kaczmarz method for phase retrieval. We show that it is sufficient
to have as many Gaussian measurements as the dimension, up to a constant
factor. Along the way, we introduce a sufficient condition on measurement sets
for which the randomized Kaczmarz method is guaranteed to work. We show that
Gaussian sampling vectors satisfy this property with high probability; this is
proved using a chaining argument coupled with bounds on VC dimension and metric
entropy.
Using probabilistic programs as proposals
Authors: Marco F. Cusumano-Towner, Vikash K. Mansinghka
Date: 2018-01-11 00:00:00
Tasks: Probabilistic Programming

Monte Carlo inference has asymptotic guarantees, but can be slow when using
generic proposals. Handcrafted proposals that rely on user knowledge about the
posterior distribution can be efficient, but are difficult to derive and
implement. This paper proposes to let users express their posterior knowledge
in the form of proposal programs, which are samplers written in probabilistic
programming languages. One strategy for writing good proposal programs is to
combine domain-specific heuristic algorithms with neural network models. The
heuristics identify high probability regions, and the neural networks model the
posterior uncertainty around the outputs of the algorithm. Proposal programs
can be used as proposal distributions in importance sampling and
Metropolis-Hastings samplers without sacrificing asymptotic consistency, and
can be optimized offline using inference compilation. Support for optimizing
and using proposal programs is easily implemented in a sampling-based
probabilistic programming runtime. The paper illustrates the proposed technique
with a proposal program that combines RANSAC and neural networks to accelerate
inference in a Bayesian linear regression with outliers model.
Characterizing Types of Convolution in Deep Convolutional Recurrent Neural Networks for Robust Speech Emotion Recognition
Authors: Che-Wei Huang, Shrikanth. S. Narayanan
Date: 2017-06-07 00:00:00
Tasks: Emotion Recognition, Event Detection, Speech Emotion Recognition, speech-recognition, Speech Recognition

Deep convolutional neural networks are being actively investigated in a wide
range of speech and audio processing applications including speech recognition,
audio event detection and computational paralinguistics, owing to their ability
to reduce factors of variations, for learning from speech. However, studies
have suggested to favor a certain type of convolutional operations when
building a deep convolutional neural network for speech applications although
there has been promising results using different types of convolutional
operations. In this work, we study four types of convolutional operations on
different input features for speech emotion recognition under noisy and clean
conditions in order to derive a comprehensive understanding. Since affective
behavioral information has been shown to reflect temporally varying of mental
state and convolutional operation are applied locally in time, all deep neural
networks share a deep recurrent sub-network architecture for further temporal
modeling. We present detailed quantitative module-wise performance analysis to
gain insights into information flows within the proposed architectures. In
particular, we demonstrate the interplay of affective information and the other
irrelevant information during the progression from one module to another.
Finally we show that all of our deep neural networks provide state-of-the-art
performance on the eNTERFACE'05 corpus.
MEBoost: Mixing Estimators with Boosting for Imbalanced Data Classification
Authors: Farshid Rayhan, Sajid Ahmed, Asif Mahbub, Md. Rafsan Jani, Swakkhar Shatabda, Dewan Md. Farid, Chowdhury Mofizur Rahman
Date: 2017-12-18 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification

Class imbalance problem has been a challenging research problem in the fields
of machine learning and data mining as most real life datasets are imbalanced.
Several existing machine learning algorithms try to maximize the accuracy
classification by correctly identifying majority class samples while ignoring
the minority class. However, the concept of the minority class instances
usually represents a higher interest than the majority class. Recently, several
cost sensitive methods, ensemble models and sampling techniques have been used
in literature in order to classify imbalance datasets. In this paper, we
propose MEBoost, a new boosting algorithm for imbalanced datasets. MEBoost
mixes two different weak learners with boosting to improve the performance on
imbalanced datasets. MEBoost is an alternative to the existing techniques such
as SMOTEBoost, RUSBoost, Adaboost, etc. The performance of MEBoost has been
evaluated on 12 benchmark imbalanced datasets with state of the art ensemble
methods like SMOTEBoost, RUSBoost, Easy Ensemble, EUSBoost, DataBoost.
Experimental results show significant improvement over the other methods and it
can be concluded that MEBoost is an effective and promising algorithm to deal
with imbalance datasets. The python version of the code is available here:
https://github.com/farshidrayhanuiu/
Gradient descent GAN optimization is locally stable
Authors: Vaishnavh Nagarajan, J. Zico Kolter
Date: 2017-06-13 00:00:00
Proceeding: NeurIPS 2017 12

Despite the growing prominence of generative adversarial networks (GANs),
optimization in GANs is still a poorly understood topic. In this paper, we
analyze the "gradient descent" form of GAN optimization i.e., the natural
setting where we simultaneously take small gradient steps in both generator and
discriminator parameters. We show that even though GAN optimization does not
correspond to a convex-concave game (even for simple parameterizations), under
proper conditions, equilibrium points of this optimization procedure are still
\emph{locally asymptotically stable} for the traditional GAN formulation. On
the other hand, we show that the recently proposed Wasserstein GAN can have
non-convergent limit cycles near equilibrium. Motivated by this stability
analysis, we propose an additional regularization term for gradient descent GAN
updates, which \emph{is} able to guarantee local stability for both the WGAN
and the traditional GAN, and also shows practical promise in speeding up
convergence and addressing mode collapse.
Sparse principal component analysis via axis-aligned random projections
Authors: Milana Gataric, Tengyao Wang, Richard J. Samworth
Date: 2017-12-15 00:00:00

We introduce a new method for sparse principal component analysis, based on the aggregation of eigenvector information from carefully-selected axis-aligned random projections of the sample covariance matrix. Unlike most alternative approaches, our algorithm is non-iterative, so is not vulnerable to a bad choice of initialisation. We provide theoretical guarantees under which our principal subspace estimator can attain the minimax optimal rate of convergence in polynomial time. In addition, our theory provides a more refined understanding of the statistical and computational trade-off in the problem of sparse principal component estimation, revealing a subtle interplay between the effective sample size and the number of random projections that are required to achieve the minimax optimal rate. Numerical studies provide further insight into the procedure and confirm its highly competitive finite-sample performance.
Size-to-depth: A New Perspective for Single Image Depth Estimation
Authors: Yiran Wu, Sihao Ying, Lianmin Zheng
Date: 2018-01-13 00:00:00
Tasks: Depth Estimation

In this paper we consider the problem of single monocular image depth
estimation. It is a challenging problem due to its ill-posedness nature and has
found wide application in industry. Previous efforts belongs roughly to two
families: learning-based method and interactive method. Learning-based method,
in which deep convolutional neural network (CNN) is widely used, can achieve
good result. But they suffer low generalization ability and typically perform
poorly for unfamiliar scenes. Besides, data-hungry nature for such method makes
data aquisition expensive and time-consuming. Interactive method requires human
annotation of depth which, however, is errorneous and of large variance.
  To overcome these problems, we propose a new perspective for single monocular
image depth estimation problem: size to depth. Our method require sparse label
for real-world size of object rather than raw depth. A Coarse depth map is then
inferred following geometric relationships according to size labels. Then we
refine the depth map by doing energy function optimization on conditional
random field(CRF). We experimentally demonstrate that our method outperforms
traditional depth-labeling methods and can produce satisfactory depth maps.
Semi-supervised Fisher vector network
Authors: Petar Palasek, Ioannis Patras
Date: 2018-01-13 00:00:00
Tasks: Action Recognition, Classification, Dimensionality Reduction, General Classification, image-classification, Image Classification, Temporal Action Localization

In this work we explore how the architecture proposed in [8], which expresses
the processing steps of the classical Fisher vector pipeline approaches, i.e.
dimensionality reduction by principal component analysis (PCA) projection,
Gaussian mixture model (GMM) and Fisher vector descriptor extraction as network
layers, can be modified into a hybrid network that combines the benefits of
both unsupervised and supervised training methods, resulting in a model that
learns a semi-supervised Fisher vector descriptor of the input data. We
evaluate the proposed model at image classification and action recognition
problems and show how the model's classification performance improves as the
amount of unlabeled data increases during training.
Detecting Offensive Language in Tweets Using Deep Learning
Authors: Georgios K. Pitsilis, Heri Ramampiaro, Helge Langseth
Date: 2018-01-13 00:00:00
Tasks: 16k, Abuse Detection, Deep Learning, General Classification

This paper addresses the important problem of discerning hateful content in
social media. We propose a detection scheme that is an ensemble of Recurrent
Neural Network (RNN) classifiers, and it incorporates various features
associated with user-related information, such as the users' tendency towards
racism or sexism. These data are fed as input to the above classifiers along
with the word frequency vectors derived from the textual content. Our approach
has been evaluated on a publicly available corpus of 16k tweets, and the
results demonstrate its effectiveness in comparison to existing state of the
art solutions. More specifically, our scheme can successfully distinguish
racism and sexism messages from normal text, and achieve higher classification
quality than current state-of-the-art algorithms.
Autoencoder-Driven Weather Clustering for Source Estimation during Nuclear Events
Authors: I. A. Klampanos, A. Davvetas, S. Andronopoulos, C. Pappas, A. Ikonomopoulos, V. Karkaletsis
Date: 2017-09-18 00:00:00
Tasks: Clustering, General Classification

Emergency response applications for nuclear or radiological events can be
significantly improved via deep feature learning due to the hidden complexity
of the data and models involved. In this paper we present a novel methodology
for rapid source estimation during radiological releases based on deep feature
extraction and weather clustering. Atmospheric dispersions are then calculated
based on identified predominant weather patterns and are matched against
simulated incidents indicated by radiation readings on the ground. We evaluate
the accuracy of our methods over multiple years of weather reanalysis data in
the European region. We juxtapose these results with deep classification
convolution networks and discuss advantages and disadvantages.
Towards a more efficient representation of imputation operators in TPOT
Authors: Unai Garciarena, Alexander Mendiburu, Roberto Santana
Date: 2018-01-13 00:00:00
Tasks: BIG-bench Machine Learning, Imputation, Model Selection

Automated Machine Learning encompasses a set of meta-algorithms intended to
design and apply machine learning techniques (e.g., model selection,
hyperparameter tuning, model assessment, etc.). TPOT, a software for optimizing
machine learning pipelines based on genetic programming (GP), is a novel
example of this kind of applications. Recently we have proposed a way to
introduce imputation methods as part of TPOT. While our approach was able to
deal with problems with missing data, it can produce a high number of
unfeasible pipelines. In this paper we propose a strongly-typed-GP based
approach that enforces constraint satisfaction by GP solutions. The enhancement
we introduce is based on the redefinition of the operators and implicit
enforcement of constraints in the generation of the GP trees. We evaluate the
method to introduce imputation methods as part of TPOT. We show that the method
can notably increase the efficiency of the GP search for optimal pipelines.