pwc-archive/papers-with-abstracts huggingface dataset:
Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study
Authors: Matthias Neumann-Brosig, Alonso Marco, Dieter Schwarzmann, Sebastian Trimpe
Date: 2018-12-15 00:00:00
Tasks: Bayesian Optimization

Bayesian optimization is proposed for automatic learning of optimal
controller parameters from experimental data. A probabilistic description (a
Gaussian process) is used to model the unknown function from controller
parameters to a user-defined cost. The probabilistic model is updated with
data, which is obtained by testing a set of parameters on the physical system
and evaluating the cost. In order to learn fast, the Bayesian optimization
algorithm selects the next parameters to evaluate in a systematic way, for
example, by maximizing information gain about the optimum. The algorithm thus
iteratively finds the globally optimal parameters with only few experiments.
Taking throttle valve control as a representative industrial control example,
the proposed auto-tuning method is shown to outperform manual calibration: it
consistently achieves better performance with a low number of experiments. The
proposed auto-tuning framework is flexible and can handle different control
structures and objectives.
Likelihood Quantile Networks for Coordinating Multi-Agent Reinforcement Learning
Authors: Xueguang Lyu, Christopher Amato
Date: 2018-12-15 00:00:00
Tasks: Multi-agent Reinforcement Learning, Philosophy, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

When multiple agents learn in a decentralized manner, the environment appears non-stationary from the perspective of an individual agent due to the exploration and learning of the other agents. Recently proposed deep multi-agent reinforcement learning methods have tried to mitigate this non-stationarity by attempting to determine which samples are from other agent exploration or suboptimality and take them less into account during learning. Based on the same philosophy, this paper introduces a decentralized quantile estimator, which aims to improve performance by distinguishing non-stationary samples based on the likelihood of returns. In particular, each agent considers the likelihood that other agent exploration and policy changes are occurring, essentially utilizing the agent's own estimations to weigh the learning rate that should be applied towards the given samples. We introduce a formal method of calculating differences of our return distribution representations and methods for utilizing it to guide updates. We also explore the effect of risk-seeking strategies for adjusting learning over time and propose adaptive risk distortion functions which guides risk sensitivity. Our experiments, on traditional benchmarks and new domains, show our methods are more stable, sample efficient and more likely to converge to a joint optimal policy than previous methods.
PiCANet: Pixel-wise Contextual Attention Learning for Accurate Saliency Detection
Authors: Nian Liu, Junwei Han, Ming-Hsuan Yang
Date: 2018-12-15 00:00:00
Tasks: object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Saliency Prediction, Salient Object Detection, Semantic Segmentation

In saliency detection, every pixel needs contextual information to make
saliency prediction. Previous models usually incorporate contexts holistically.
However, for each pixel, usually only part of its context region is useful and
contributes to its prediction, while some other part may serve as noises and
distractions. In this paper, we propose a novel pixel-wise contextual attention
network, \ie PiCANet, to learn to selectively attend to informative context
locations at each pixel. Specifically, PiCANet generates an attention map over
the context region of each pixel, where each attention weight corresponds to
the relevance of a context location w.r.t the referred pixel. Then, attentive
contextual features can be constructed via selectively incorporating the
features of useful context locations with the learned attention. We propose
three specific formulations of the PiCANet via embedding the pixel-wise
contextual attention mechanism into the pooling and convolution operations with
attending to global or local contexts. All the three models are fully
differentiable and can be integrated with CNNs with joint training. We
introduce the proposed PiCANets into a U-Net architecture for salient object
detection. Experimental results indicate that the proposed PiCANets can
significantly improve the saliency detection performance. The generated global
and local attention can learn to incorporate global contrast and smoothness,
respectively, which help localize salient objects more accurately and highlight
them more uniformly. Consequently, our saliency model performs favorably
against other state-of-the-art methods. Moreover, we also validate that
PiCANets can also improve semantic segmentation and object detection
performances, which further demonstrates their effectiveness and generalization
ability.
Extending classical surrogate modelling to high-dimensions through supervised dimensionality reduction: a data-driven approach
Authors: C. Lataniotis, S. Marelli, B. Sudret
Date: 2018-12-15 00:00:00
Tasks: Dimensionality Reduction, Supervised dimensionality reduction, Uncertainty Quantification

Thanks to their versatility, ease of deployment and high-performance, surrogate models have become staple tools in the arsenal of uncertainty quantification (UQ). From local interpolants to global spectral decompositions, surrogates are characterised by their ability to efficiently emulate complex computational models based on a small set of model runs used for training. An inherent limitation of many surrogate models is their susceptibility to the curse of dimensionality, which traditionally limits their applicability to a maximum of $\mathcal{O}(10^2)$ input dimensions. We present a novel approach at high-dimensional surrogate modelling that is model-, dimensionality reduction- and surrogate model- agnostic (black box), and can enable the solution of high dimensional (i.e. up to $\mathcal{O}(10^4)$) problems. After introducing the general algorithm, we demonstrate its performance by combining Kriging and polynomial chaos expansions surrogates and kernel principal component analysis. In particular, we compare the generalisation performance that the resulting surrogates achieve to the classical sequential application of dimensionality reduction followed by surrogate modelling on several benchmark applications, comprising an analytical function and two engineering applications of increasing dimensionality and complexity.
Generative adversarial networks for generation and classification of physical rehabilitation movement episodes
Authors: L. Li, A. Vakanski
Date: 2018-12-15 00:00:00
Tasks: General Classification, Generative Adversarial Network

This article proposes a method for mathematical modeling of human movements
related to patient exercise episodes performed during physical therapy sessions
by using artificial neural networks. The generative adversarial network
structure is adopted, whereby a discriminative and a generative model are
trained concurrently in an adversarial manner. Different network architectures
are examined, with the discriminative and generative models structured as deep
subnetworks of hidden layers comprised of convolutional or recurrent
computational units. The models are validated on a data set of human movements
recorded with an optical motion tracker. The results demonstrate an ability of
the networks for classification of new instances of motions, and for generation
of motion examples that resemble the recorded motion sequences.
Multi-Tasking Genetic Algorithm (MTGA) for Fuzzy System Optimization
Authors: Dongrui Wu, Xianfeng Tan
Date: 2018-12-15 00:00:00
Tasks: Multi-Task Learning

Multi-task learning uses auxiliary data or knowledge from relevant tasks to facilitate the learning in a new task. Multi-task optimization applies multi-task learning to optimization to study how to effectively and efficiently tackle multiple optimization problems simultaneously. Evolutionary multi-tasking, or multi-factorial optimization, is an emerging subfield of multi-task optimization, which integrates evolutionary computation and multi-task learning. This paper proposes a novel and easy-to-implement multi-tasking genetic algorithm (MTGA), which copes well with significantly different optimization tasks by estimating and using the bias among them. Comparative studies with eight state-of-the-art single- and multi-task approaches in the literature on nine benchmarks demonstrated that on average the MTGA outperformed all of them, and had lower computational cost than six of them. Based on the MTGA, a simultaneous optimization strategy for fuzzy system design is also proposed. Experiments on simultaneous optimization of type-1 and interval type-2 fuzzy logic controllers for couple-tank water level control demonstrated that the MTGA can find better fuzzy logic controllers than other approaches.
Analysis of the $(μ/μ_I,λ)$-$σ$-Self-Adaptation Evolution Strategy with Repair by Projection Applied to a Conically Constrained Problem
Authors: Patrick Spettel, Hans-Georg Beyer
Date: 2018-12-15 00:00:00

A theoretical performance analysis of the
$(\mu/\mu_I,\lambda)$-$\sigma$-Self-Adaptation Evolution Strategy
($\sigma$SA-ES) is presented considering a conically constrained problem.
Infeasible offspring are repaired using projection onto the boundary of the
feasibility region. Closed-form approximations are used for the one-generation
progress of the evolution strategy. Approximate deterministic evolution
equations are formulated for analyzing the strategy's dynamics. By iterating
the evolution equations with the approximate one-generation expressions, the
evolution strategy's dynamics can be predicted. The derived theoretical results
are compared to experiments for assessing the approximation quality. It is
shown that in the steady state the $(\mu/\mu_I,\lambda)$-$\sigma$SA-ES exhibits
a performance as if the ES were optimizing a sphere model. Unlike the
non-recombinative $(1,\lambda)$-ES, the parental steady state behavior does not
evolve on the cone boundary but stays away from the boundary to a certain
extent.
Residual Policy Learning
Authors: Tom Silver, Kelsey Allen, Josh Tenenbaum, Leslie Kaelbling
Date: 2018-12-15 00:00:00
Tasks: Deep Reinforcement Learning, MuJoCo, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present Residual Policy Learning (RPL): a simple method for improving
nondifferentiable policies using model-free deep reinforcement learning. RPL
thrives in complex robotic manipulation tasks where good but imperfect
controllers are available. In these tasks, reinforcement learning from scratch
remains data-inefficient or intractable, but learning a residual on top of the
initial controller can yield substantial improvements. We study RPL in six
challenging MuJoCo tasks involving partial observability, sensor noise, model
misspecification, and controller miscalibration. For initial controllers, we
consider both hand-designed policies and model-predictive controllers with
known or learned transition models. By combining learning with control
algorithms, RPL can perform long-horizon, sparse-reward tasks for which
reinforcement learning alone fails. Moreover, we find that RPL consistently and
substantially improves on the initial controllers. We argue that RPL is a
promising approach for combining the complementary strengths of deep
reinforcement learning and robotic control, pushing the boundaries of what
either can achieve independently. Video and code at
https://k-r-allen.github.io/residual-policy-learning/.
Hinted Networks
Authors: Joel Lamy-Poirier, Anqi Xu
Date: 2018-12-15 00:00:00
Tasks: Camera Relocalization, Indoor Localization, regression

We present Hinted Networks: a collection of architectural transformations for
improving the accuracies of neural network models for regression tasks, through
the injection of a prior for the output prediction (i.e. a hint). We ground our
investigations within the camera relocalization domain, and propose two
variants, namely the Hinted Embedding and Hinted Residual networks, both
applied to the PoseNet base model for regressing camera pose from an image. Our
evaluations show practical improvements in localization accuracy for standard
outdoor and indoor localization datasets, without using additional information.
We further assess the range of accuracy gains within an aerial-view
localization setup, simulated across vast areas at different times of the year.
A short review on Applications of Deep learning for Cyber security
Authors: Mohammed Harun Babu R, Vinayakumar R, Soman Kp
Date: 2018-12-15 00:00:00
Tasks: Android Malware Detection, BIG-bench Machine Learning, Deep Learning, General Classification, Intrusion Detection, Malware Classification, Malware Detection

Deep learning is an advanced model of traditional machine learning. This has
the capability to extract optimal feature representation from raw input
samples. This has been applied towards various use cases in cyber security such
as intrusion detection, malware classification, android malware detection, spam
and phishing detection and binary analysis. This paper outlines the survey of
all the works related to deep learning based solutions for various cyber
security use cases. Keywords: Deep learning, intrusion detection, malware
detection, Android malware detection, spam & phishing detection, traffic
analysis, binary analysis.
Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia
Authors: Ikuya Yamada, Akari Asai, Jin Sakuma, Hiroyuki Shindo, Hideaki Takeda, Yoshiyasu Takefuji, Yuji Matsumoto
Date: 2018-12-15 00:00:00
Proceeding: EMNLP 2020 11
Tasks: World Knowledge

The embeddings of entities in a large knowledge base (e.g., Wikipedia) are highly beneficial for solving various natural language tasks that involve real world knowledge. In this paper, we present Wikipedia2Vec, a Python-based open-source tool for learning the embeddings of words and entities from Wikipedia. The proposed tool enables users to learn the embeddings efficiently by issuing a single command with a Wikipedia dump file as an argument. We also introduce a web-based demonstration of our tool that allows users to visualize and explore the learned embeddings. In our experiments, our tool achieved a state-of-the-art result on the KORE entity relatedness dataset, and competitive results on various standard benchmark datasets. Furthermore, our tool has been used as a key component in various recent studies. We publicize the source code, demonstration, and the pretrained embeddings for 12 languages at https://wikipedia2vec.github.io.
PVSNet: Palm Vein Authentication Siamese Network Trained using Triplet Loss and Adaptive Hard Mining by Learning Enforced Domain Specific Features
Authors: Daksh Thapar, Gaurav Jaswal, Aditya Nigam, Vivek Kanhangad
Date: 2018-12-15 00:00:00
Tasks: Decoder, Triplet

Designing an end-to-end deep learning network to match the biometric features
with limited training samples is an extremely challenging task. To address this
problem, we propose a new way to design an end-to-end deep CNN framework i.e.,
PVSNet that works in two major steps: first, an encoder-decoder network is used
to learn generative domain-specific features followed by a Siamese network in
which convolutional layers are pre-trained in an unsupervised fashion as an
autoencoder. The proposed model is trained via triplet loss function that is
adjusted for learning feature embeddings in a way that minimizes the distance
between embedding-pairs from the same subject and maximizes the distance with
those from different subjects, with a margin. In particular, a triplet Siamese
matching network using an adaptive margin based hard negative mining has been
suggested. The hyper-parameters associated with the training strategy, like the
adaptive margin, have been tuned to make the learning more effective on
biometric datasets. In extensive experimentation, the proposed network
outperforms most of the existing deep learning solutions on three type of
typical vein datasets which clearly demonstrates the effectiveness of our
proposed method.
Hierarchical Discrete Distribution Decomposition for Match Density Estimation
Authors: Zhichao Yin, Trevor Darrell, Fisher Yu
Date: 2018-12-15 00:00:00
Proceeding: CVPR 2019 6
Tasks: Density Estimation, Optical Flow Estimation, Stereo Matching, Stereo Matching Hand

Explicit representations of the global match distributions of pixel-wise
correspondences between pairs of images are desirable for uncertainty
estimation and downstream applications. However, the computation of the match
density for each pixel may be prohibitively expensive due to the large number
of candidates. In this paper, we propose Hierarchical Discrete Distribution
Decomposition (HD^3), a framework suitable for learning probabilistic pixel
correspondences in both optical flow and stereo matching. We decompose the full
match density into multiple scales hierarchically, and estimate the local
matching distributions at each scale conditioned on the matching and warping at
coarser scales. The local distributions can then be composed together to form
the global match density. Despite its simplicity, our probabilistic method
achieves state-of-the-art results for both optical flow and stereo matching on
established benchmarks. We also find the estimated uncertainty is a good
indication of the reliability of the predicted correspondences.
3DTI-Net: Learn Inner Transform Invariant 3D Geometry Features using Dynamic GCN
Authors: Guanghua Pan, Jun Wang, Rendong Ying, Peilin Liu
Date: 2018-12-15 00:00:00
Tasks: 3D geometry, 3D Object Classification, Classification, Deep Learning, General Classification, Segmentation

Deep learning on point clouds has made a lot of progress recently. Many point
cloud dedicated deep learning frameworks, such as PointNet and PointNet++, have
shown advantages in accuracy and speed comparing to those using traditional 3D
convolution algorithms. However, nearly all of these methods face a challenge,
since the coordinates of the point cloud are decided by the coordinate system,
they cannot handle the problem of 3D transform invariance properly. In this
paper, we propose a general framework for point cloud learning. We achieve
transform invariance by learning inner 3D geometry feature based on local graph
representation, and propose a feature extraction network based on graph
convolution network. Through experiments on classification and segmentation
tasks, our method achieves state-of-the-art performance in rotated 3D object
classification, and achieve competitive performance with the state-of-the-art
in classification and segmentation tasks with fixed coordinate value.
Flatten-T Swish: a thresholded ReLU-Swish-like activation function for deep learning
Authors: Hock Hung Chieng, Noorhaniza Wahid, Pauline Ong, Sai Raj Kishore Perla
Date: 2018-12-15 00:00:00
Tasks: General Classification, image-classification, Image Classification

Activation functions are essential for deep learning methods to learn and
perform complex tasks such as image classification. Rectified Linear Unit
(ReLU) has been widely used and become the default activation function across
the deep learning community since 2012. Although ReLU has been popular,
however, the hard zero property of the ReLU has heavily hindered the negative
values from propagating through the network. Consequently, the deep neural
network has not been benefited from the negative representations. In this work,
an activation function called Flatten-T Swish (FTS) that leverage the benefit
of the negative values is proposed. To verify its performance, this study
evaluates FTS with ReLU and several recent activation functions. Each
activation function is trained using MNIST dataset on five different deep fully
connected neural networks (DFNNs) with depth vary from five to eight layers.
For a fair evaluation, all DFNNs are using the same configuration settings.
Based on the experimental results, FTS with a threshold value, T=-0.20 has the
best overall performance. As compared with ReLU, FTS (T=-0.20) improves MNIST
classification accuracy by 0.13%, 0.70%, 0.67%, 1.07% and 1.15% on wider 5
layers, slimmer 5 layers, 6 layers, 7 layers and 8 layers DFNNs respectively.
Apart from this, the study also noticed that FTS converges twice as fast as
ReLU. Although there are other existing activation functions are also
evaluated, this study elects ReLU as the baseline activation function.
Algorithmic Theory of ODEs and Sampling from Well-conditioned Logconcave Densities
Authors: Yin Tat Lee, Zhao Song, Santosh S. Vempala
Date: 2018-12-15 00:00:00

Sampling logconcave functions arising in statistics and machine learning has
been a subject of intensive study. Recent developments include analyses for
Langevin dynamics and Hamiltonian Monte Carlo (HMC). While both approaches have
dimension-independent bounds for the underlying $\mathit{continuous}$ processes
under sufficiently strong smoothness conditions, the resulting discrete
algorithms have complexity and number of function evaluations growing with the
dimension. Motivated by this problem, in this paper, we give a general
algorithm for solving multivariate ordinary differential equations whose
solution is close to the span of a known basis of functions (e.g., polynomials
or piecewise polynomials). The resulting algorithm has polylogarithmic depth
and essentially tight runtime - it is nearly linear in the size of the
representation of the solution.
  We apply this to the sampling problem to obtain a nearly linear
implementation of HMC for a broad class of smooth, strongly logconcave
densities, with the number of iterations (parallel depth) and gradient
evaluations being $\mathit{polylogarithmic}$ in the dimension (rather than
polynomial as in previous work). This class includes the widely-used loss
function for logistic regression with incoherent weight matrices and has been
subject of much study recently. We also give a faster algorithm with $
\mathit{polylogarithmic~depth}$ for the more general and standard class of
strongly convex functions with Lipschitz gradient. These results are based on
(1) an improved contraction bound for the exact HMC process and (2) logarithmic
bounds on the degree of polynomials that approximate solutions of the
differential equations arising in implementing HMC.