pwc-archive/papers-with-abstracts huggingface dataset:
Robust Optimization through Neuroevolution
Authors: Paolo Pagliuca, Stefano Nolfi
Date: 2018-10-02 00:00:00
Tasks: Car Racing

We propose a method for evolving solutions that are robust with respect to
variations of the environmental conditions (i.e. that can operate effectively
in new conditions immediately, without the need to adapt to variations). The
obtained results show how the method proposed is effective and computational
tractable. It permits to improve performance on an extended version of the
double-pole balancing problem, to outperform the best available human-designed
controllers on a car racing problem, and to generate rather effective solutions
for a swarm robotic problem. The comparison of different algorithms indicates
that the CMA-ES and xNES methods, that operate by optimizing a distribution of
parameters, represent the best options for the evolution of robust neural
network controllers.
Sinkhorn AutoEncoders
Authors: Giorgio Patrini, Rianne van den Berg, Patrick Forré, Marcello Carioni, Samarth Bhargav, Max Welling, Tim Genewein, Frank Nielsen
Date: 2018-10-02 00:00:00
Proceeding: ICLR 2019 5
Tasks: Probabilistic Programming

Optimal transport offers an alternative to maximum likelihood for learning generative autoencoding models. We show that minimizing the p-Wasserstein distance between the generator and the true data distribution is equivalent to the unconstrained min-min optimization of the p-Wasserstein distance between the encoder aggregated posterior and the prior in latent space, plus a reconstruction error. We also identify the role of its trade-off hyperparameter as the capacity of the generator: its Lipschitz constant. Moreover, we prove that optimizing the encoder over any class of universal approximators, such as deterministic neural networks, is enough to come arbitrarily close to the optimum. We therefore advertise this framework, which holds for any metric space and prior, as a sweet-spot of current generative autoencoding objectives. We then introduce the Sinkhorn auto-encoder (SAE), which approximates and minimizes the p-Wasserstein distance in latent space via backprogation through the Sinkhorn algorithm. SAE directly works on samples, i.e. it models the aggregated posterior as an implicit distribution, with no need for a reparameterization trick for gradients estimations. SAE is thus able to work with different metric spaces and priors with minimal adaptations. We demonstrate the flexibility of SAE on latent spaces with different geometries and priors and compare with other methods on benchmark data sets.
Who is Addressed in this Comment? Automatically Classifying Meta-Comments in News Comments
Authors: Marlo Häring, Wiebke Loosen, Walid Maalej
Date: 2018-10-02 00:00:00
Tasks: Classification, General Classification

User comments have become an essential part of online journalism. However,
newsrooms are often overwhelmed by the vast number of diverse comments, for
which a manual analysis is barely feasible. Identifying meta-comments that
address or mention newsrooms, individual journalists, or moderators and that
may call for reactions is particularly critical. In this paper, we present an
automated approach to identify and classify meta-comments. We compare comment
classification based on manually extracted features with an end-to-end learning
approach. We develop, optimize, and evaluate multiple classifiers on a comment
dataset of the large German online newsroom SPIEGEL Online and the 'One Million
Posts' corpus of DER STANDARD, an Austrian newspaper. Both optimized
classification approaches achieved encouraging $F_{0.5}$ values between 76% and
91%. We report on the most significant classification features with the results
of a qualitative analysis and discuss how our work contributes to making
participation in online journalism more constructive.
The Dreaming Variational Autoencoder for Reinforcement Learning Environments
Authors: Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
Date: 2018-10-02 00:00:00
Tasks: Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning has shown great potential in generalizing over raw
sensory data using only a single neural network for value optimization. There
are several challenges in the current state-of-the-art reinforcement learning
algorithms that prevent them from converging towards the global optima. It is
likely that the solution to these problems lies in short- and long-term
planning, exploration and memory management for reinforcement learning
algorithms. Games are often used to benchmark reinforcement learning algorithms
as they provide a flexible, reproducible, and easy to control environment.
Regardless, few games feature a state-space where results in exploration,
memory, and planning are easily perceived. This paper presents The Dreaming
Variational Autoencoder (DVAE), a neural network based generative modeling
architecture for exploration in environments with sparse feedback. We further
present Deep Maze, a novel and flexible maze engine that challenges DVAE in
partial and fully-observable state-spaces, long-horizon tasks, and
deterministic and stochastic problems. We show initial findings and encourage
further work in reinforcement learning driven by generative exploration.
AI Benchmark: Running Deep Neural Networks on Android Smartphones
Authors: Andrey Ignatov, Radu Timofte, William Chou, Ke Wang, Max Wu, Tim Hartley, Luc van Gool
Date: 2018-10-02 00:00:00

Over the last years, the computational power of mobile devices such as
smartphones and tablets has grown dramatically, reaching the level of desktop
computers available not long ago. While standard smartphone apps are no longer
a problem for them, there is still a group of tasks that can easily challenge
even high-end devices, namely running artificial intelligence algorithms. In
this paper, we present a study of the current state of deep learning in the
Android ecosystem and describe available frameworks, programming models and the
limitations of running AI on smartphones. We give an overview of the hardware
acceleration resources available on four main mobile chipset platforms:
Qualcomm, HiSilicon, MediaTek and Samsung. Additionally, we present the
real-world performance results of different mobile SoCs collected with AI
Benchmark that are covering all main existing hardware configurations.
Injective State-Image Mapping facilitates Visual Adversarial Imitation Learning
Authors: Subhajit Chaudhury, Daiki Kimura, Asim Munawar, Ryuki Tachibana
Date: 2018-10-02 00:00:00
Tasks: Imitation Learning

The growing use of virtual autonomous agents in applications like games and entertainment demands better control policies for natural-looking movements and actions. Unlike the conventional approach of hard-coding motion routines, we propose a deep learning method for obtaining control policies by directly mimicking raw video demonstrations. Previous methods in this domain rely on extracting low-dimensional features from expert videos followed by a separate hand-crafted reward estimation step. We propose an imitation learning framework that reduces the dependence on hand-engineered reward functions by jointly learning the feature extraction and reward estimation steps using Generative Adversarial Networks (GANs). Our main contribution in this paper is to show that under injective mapping between low-level joint state (angles and velocities) trajectories and corresponding raw video stream, performing adversarial imitation learning on video demonstrations is equivalent to learning from the state trajectories. Experimental results show that the proposed adversarial learning method from raw videos produces a similar performance to state-of-the-art imitation learning techniques while frequently outperforming existing hand-crafted video imitation methods. Furthermore, we show that our method can learn action policies by imitating video demonstrations on YouTube with similar performance to learned agents from true reward signals. Please see the supplementary video submission at https://ibm.biz/BdzzNA.
New elements for a network (including brain) general theory during learning period
Authors: Jean Piniello
Date: 2013-01-14 00:00:00

This study deals with the evolution of the so called 'intelligent' networks
(insect society without leader, cells of an organism, brain,...) during their
learning period. First we summarize briefly the Version 2 (published in
French), whose the main characteristics are: 1) A network connected to its
environment is considered as immersed into an information field created by this
environment which so dictates to it the learning constraints. 2) The used
formalism draws one's inspiration from the one of the Quantum field theory
(Principle of stationary action, gauge fields, invariance by symmetry
transformations,...). 3) We obtain Lagrange equations whose solutions describe
the network evolution during the whole learning period. 4) Then, while
proceeding with the same formalism inspiration, we suggest other study ways
capable of evolving the knowledge in the considered scope. In a second part,
after a reminder of the points to be improved, we exhibit the Version 5 which
brings, we think, relevant improvements. Indeed: 5) We consider the weighted
averages of the variables; this introduces probabilities. 6) We define two
observables (L average of information flux and A activity of the network) which
could be measured and so be compared with experimental results. 7) We find that
L , weighted average of information flows, is an invariant. 8) Finally, we
propose two expressions for the conactance, from which we deduce the
corresponding Lagrange equations which have to be solved to know the evolution
of the considered weighted averages. But, at the present stage, we think that
we can progress only by carrying out experiments (see projects like Human brain
project) and discovering invariants, symmetries which would allow us, like in
Physics, to classify networks and above all to understand better the
connections between them. Indeed, and that is what we propose among the future
research ways, the underlying problem is to understand how, after their
learning period, several networks can connect together to produce, in the brain
case for instance, what we call mental states.
Target Aware Network Adaptation for Efficient Representation Learning
Authors: Yang Zhong, Vladimir Li, Ryuzo Okada, Atsuto Maki
Date: 2018-10-02 00:00:00
Tasks: image-classification, Image Classification, Representation Learning, Transfer Learning

This paper presents an automatic network adaptation method that finds a
ConvNet structure well-suited to a given target task, e.g., image
classification, for efficiency as well as accuracy in transfer learning. We
call the concept target-aware transfer learning. Given only small-scale labeled
data, and starting from an ImageNet pre-trained network, we exploit a scheme of
removing its potential redundancy for the target task through iterative
operations of filter-wise pruning and network optimization. The basic
motivation is that compact networks are on one hand more efficient and should
also be more tolerant, being less complex, against the risk of overfitting
which would hinder the generalization of learned representations in the context
of transfer learning. Further, unlike existing methods involving network
simplification, we also let the scheme identify redundant portions across the
entire network, which automatically results in a network structure adapted to
the task at hand. We achieve this with a few novel ideas: (i) cumulative sum of
activation statistics for each layer, and (ii) a priority evaluation of pruning
across multiple layers. Experimental results by the method on five datasets
(Flower102, CUB200-2011, Dog120, MIT67, and Stanford40) show favorable
accuracies over the related state-of-the-art techniques while enhancing the
computational and storage efficiency of the transferred model.
Quantization-Aware Phase Retrieval
Authors: Subhadip Mukherjee, Chandra Sekhar Seelamantula
Date: 2018-10-02 00:00:00
Tasks: Quantization, Retrieval

We address the problem of phase retrieval (PR) from quantized measurements.
The goal is to reconstruct a signal from quadratic measurements encoded with a
finite precision, which is indeed the case in many practical applications. We
develop a rank-1 projection algorithm that recovers the signal subject to
ensuring consistency with the measurement, that is, the recovered signal when
encoded must yield the same set of measurements that one started with. The
rank-1 projection stems from the idea of lifting, originally proposed in the
context of PhaseLift. The consistency criterion is enforced using a one-sided
quadratic cost. We also determine the probability with which different vectors
lead to the same set of quantized measurements, which makes it impossible to
resolve them. Naturally, this probability depends on how correlated such
vectors are, and how coarsely/finely the measurements get quantized. The
proposed algorithm is also capable of incorporating a sparsity constraint on
the signal. An analysis of the cost function reveals that it is bounded, both
above and below, by functions that are dependent on how well correlated the
estimate is with the ground truth. We also derive the Cram\'er-Rao lower bound
(CRB) on the achievable reconstruction accuracy. A comparison with the
state-of-the- art algorithms shows that the proposed algorithm has a higher
reconstruction accuracy and is about 2 to 3 dB away from the CRB. The edge, in
terms of the reconstruction signal-to-noise ratio, over the competing
algorithms is higher (about 5 to 6 dB) when the quantization is coarse.
Ancient Coin Classification Using Graph Transduction Games
Authors: Sinem Aslan, Sebastiano Vascon, Marcello Pelillo
Date: 2018-10-02 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Recognizing the type of an ancient coin requires theoretical expertise and
years of experience in the field of numismatics. Our goal in this work is
automatizing this time consuming and demanding task by a visual classification
framework. Specifically, we propose to model ancient coin image classification
using Graph Transduction Games (GTG). GTG casts the classification problem as a
non-cooperative game where the players (the coin images) decide their
strategies (class labels) according to the choices made by the others, which
results with a global consensus at the final labeling. Experiments are
conducted on the only publicly available dataset which is composed of 180
images of 60 types of Roman coins. We demonstrate that our approach outperforms
the literature work on the same dataset with the classification accuracy of
73.6% and 87.3% when there are one and two images per class in the training
set, respectively.
Learning to Perform Local Rewriting for Combinatorial Optimization
Authors: Xinyun Chen, Yuandong Tian
Date: 2018-09-30 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Combinatorial Optimization, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

Search-based methods for hard combinatorial optimization are often guided by heuristics. Tuning heuristics in various conditions and situations is often time-consuming. In this paper, we propose NeuRewriter that learns a policy to pick heuristics and rewrite the local components of the current solution to iteratively improve it until convergence. The policy factorizes into a region-picking and a rule-picking component, each parameterized by a neural network trained with actor-critic methods in reinforcement learning. NeuRewriter captures the general structure of combinatorial problems and shows strong performance in three versatile tasks: expression simplification, online job scheduling and vehicle routing problems. NeuRewriter outperforms the expression simplification component in Z3; outperforms DeepRM and Google OR-tools in online job scheduling; and outperforms recent neural baselines and Google OR-tools in vehicle routing problems.
Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning
Authors: Charles H. Martin, Michael W. Mahoney
Date: 2018-10-02 00:00:00

Random Matrix Theory (RMT) is applied to analyze weight matrices of Deep
Neural Networks (DNNs), including both production quality, pre-trained models
such as AlexNet and Inception, and smaller models trained from scratch, such as
LeNet5 and a miniature-AlexNet. Empirical and theoretical results clearly
indicate that the DNN training process itself implicitly implements a form of
Self-Regularization. The empirical spectral density (ESD) of DNN layer matrices
displays signatures of traditionally-regularized statistical models, even in
the absence of exogenously specifying traditional forms of explicit
regularization. Building on relatively recent results in RMT, most notably its
extension to Universality classes of Heavy-Tailed matrices, we develop a theory
to identify 5+1 Phases of Training, corresponding to increasing amounts of
Implicit Self-Regularization. These phases can be observed during the training
process as well as in the final learned DNNs. For smaller and/or older DNNs,
this Implicit Self-Regularization is like traditional Tikhonov regularization,
in that there is a "size scale" separating signal from noise. For
state-of-the-art DNNs, however, we identify a novel form of Heavy-Tailed
Self-Regularization, similar to the self-organization seen in the statistical
physics of disordered systems. This results from correlations arising at all
size scales, which arises implicitly due to the training process itself. This
implicit Self-Regularization can depend strongly on the many knobs of the
training process. By exploiting the generalization gap phenomena, we
demonstrate that we can cause a small model to exhibit all 5+1 phases of
training simply by changing the batch size. This demonstrates that---all else
being equal---DNN optimization with larger batch sizes leads to less-well
implicitly-regularized models, and it provides an explanation for the
generalization gap phenomena.
NU-LiteNet: Mobile Landmark Recognition using Convolutional Neural Networks
Authors: Chakkrit Termritthikun, Surachet Kanprachar, Paisarn Muneesawang
Date: 2018-10-02 00:00:00
Tasks: Landmark Recognition

The growth of high-performance mobile devices has resulted in more research
into on-device image recognition. The research problems are the latency and
accuracy of automatic recognition, which remains obstacles to its real-world
usage. Although the recently developed deep neural networks can achieve
accuracy comparable to that of a human user, some of them still lack the
necessary latency. This paper describes the development of the architecture of
a new convolutional neural network model, NU-LiteNet. For this, SqueezeNet was
developed to reduce the model size to a degree suitable for smartphones. The
model size of NU-LiteNet is therefore 2.6 times smaller than that of
SqueezeNet. The recognition accuracy of NU-LiteNet also compared favorably with
other recently developed deep neural networks, when experiments were conducted
on two standard landmark databases.
Cloud Chaser: Real Time Deep Learning Computer Vision on Low Computing Power Devices
Authors: Zhengyi Luo, Austin Small, Liam Dugan, Stephen Lane
Date: 2018-10-02 00:00:00
Tasks: Cloud Computing, Deep Learning

Internet of Things(IoT) devices, mobile phones, and robotic systems are often denied the power of deep learning algorithms due to their limited computing power. However, to provide time-critical services such as emergency response, home assistance, surveillance, etc, these devices often need real-time analysis of their camera data. This paper strives to offer a viable approach to integrate high-performance deep learning-based computer vision algorithms with low-resource and low-power devices by leveraging the computing power of the cloud. By offloading the computation work to the cloud, no dedicated hardware is needed to enable deep neural networks on existing low computing power devices. A Raspberry Pi based robot, Cloud Chaser, is built to demonstrate the power of using cloud computing to perform real-time vision tasks. Furthermore, to reduce latency and improve real-time performance, compression algorithms are proposed and evaluated for streaming real-time video frames to the cloud.
Improving Sentence Representations with Consensus Maximisation
Authors: Shuai Tang, Virginia R. de Sa
Date: 2018-10-02 00:00:00
Proceeding: ICLR 2019 5
Tasks: Self-Supervised Learning, Sentence

Consensus maximisation learning can provide self-supervision when different views are available of the same data. The distributional hypothesis provides another form of useful self-supervision from adjacent sentences which are plentiful in large unlabelled corpora. Motivated by the observation that different learning architectures tend to emphasise different aspects of sentence meaning, we present a new self-supervised learning framework for learning sentence representations which minimises the disagreement between two views of the same sentence where one view encodes the sentence with a recurrent neural network (RNN), and the other view encodes the same sentence with a simple linear model. After learning, the individual views (networks) result in higher quality sentence representations than their single-view learnt counterparts (learnt using only the distributional hypothesis) as judged by performance on standard downstream tasks. An ensemble of both views provides even better generalisation on both supervised and unsupervised downstream tasks. Also, importantly the ensemble of views trained with consensus maximisation between the two different architectures performs better on downstream tasks than an analogous ensemble made from the single-view trained counterparts.
Feature Selection Approach with Missing Values Conducted for Statistical Learning: A Case Study of Entrepreneurship Survival Dataset
Authors: Diego Nascimento, Anderson Ara, Francisco Louzada Neto
Date: 2018-10-02 00:00:00
Tasks: feature selection, General Classification, Imputation, Missing Values

In this article, we investigate the features which enhanced discriminate the
survival in the micro and small business (MSE) using the approach of data
mining with feature selection. According to the complexity of the data set, we
proposed a comparison of three data imputation methods such as mean imputation
(MI), k-nearest neighbor (KNN) and expectation maximization (EM) using mutually
the selection of variables technique, whereby t-test, then through the data
mining process using logistic regression classification methods, naive Bayes
algorithm, linear discriminant analysis and support vector machine hence
comparing their respective performances. The experimental results will be
spread in developing a model to predict the MSE survival, providing a better
understanding in the topic once it is a significant part of the Brazilian' GPA
and macroeconomy.