pwc-archive/papers-with-abstracts huggingface dataset:
Effects of padding on LSTMs and CNNs
Authors: Mahidhar Dwarampudi, N V Subba Reddy
Date: 2019-03-18 00:00:00
Tasks: Sentiment Analysis

Long Short-Term Memory (LSTM) Networks and Convolutional Neural Networks
(CNN) have become very common and are used in many fields as they were
effective in solving many problems where the general neural networks were
inefficient. They were applied to various problems mostly related to images and
sequences. Since LSTMs and CNNs take inputs of the same length and dimension,
input images and sequences are padded to maximum length while testing and
training. This padding can affect the way the networks function and can make a
great deal when it comes to performance and accuracies. This paper studies this
and suggests the best way to pad an input sequence. This paper uses a simple
sentiment analysis task for this purpose. We use the same dataset on both the
networks with various padding to show the difference. This paper also discusses
some preprocessing techniques done on the data to ensure effective analysis of
the data.
Generating Adversarial Examples With Conditional Generative Adversarial Net
Authors: Ping Yu, Kaitao Song, Jianfeng Lu
Date: 2019-03-18 00:00:00
Tasks: Generative Adversarial Network

Recently, deep neural networks have significant progress and successful
application in various fields, but they are found vulnerable to attack
instances, e.g., adversarial examples. State-of-art attack methods can generate
attack images by adding small perturbation to the source image. These attack
images can fool the classifier but have little impact to human. Therefore, such
attack instances are difficult to generate by searching the feature space. How
to design an effective and robust generating method has become a spotlight.
Inspired by adversarial examples, we propose two novel generative models to
produce adaptive attack instances directly, in which conditional generative
adversarial network is adopted and distinctive strategy is designed for
training. Compared with the common method, such as Fast Gradient Sign Method,
our models can reduce the generating cost and improve robustness and has about
one fifth running time for producing attack instance.
POI Semantic Model with a Deep Convolutional Structure
Authors: Ji Zhao, Meiyu Yu, Huan Chen, Boning Li, Lingyu Zhang, Qi Song, Li Ma, Hua Chai, Jieping Ye
Date: 2019-03-18 00:00:00
Tasks: model, Retrieval

When using the electronic map, POI retrieval is the initial and important
step, whose quality directly affects the user experience. Similarity between
user query and POI information is the most critical feature in POI retrieval.
An accurate similarity calculation is challenging since the mismatch between a
query and a retrieval text may exist in the case of a mistyped query or an
alias inquiry. In this paper, we propose a POI latent semantic model based on
deep networks, which can effectively extract query features and POI information
features for the similarity calculation. Our model describes the semantic
information of complex texts at multiple layers, and achieves multi-field
matches by modeling POI's name and detailed address respectively. Our model is
evaluated by the POI retrieval ranking datasets, including the labeled data of
relevance and real-world user click data in POI retrieval. Results show that
our model significantly outperforms our competitors in POI retrieval ranking
tasks. The proposed algorithm has become a critical component of an online
system serving millions of people everyday.
Prototype-based classifiers in the presence of concept drift: A modelling framework
Authors: Michael Biehl, Fthi Abadi, Christina Göpfert, Barbara Hammer
Date: 2019-03-18 00:00:00
Tasks: Quantization

We present a modelling framework for the investigation of prototype-based
classifiers in non-stationary environments. Specifically, we study Learning
Vector Quantization (LVQ) systems trained from a stream of high-dimensional,
clustered data.We consider standard winner-takes-all updates known as LVQ1.
Statistical properties of the input data change on the time scale defined by
the training process. We apply analytical methods borrowed from statistical
physics which have been used earlier for the exact description of learning in
stationary environments. The suggested framework facilitates the computation of
learning curves in the presence of virtual and real concept drift. Here we
focus on timedependent class bias in the training data. First results
demonstrate that, while basic LVQ algorithms are suitable for the training in
non-stationary environments, weight decay as an explicit mechanism of
forgetting does not improve the performance under the considered drift
processes.
Emotion Recognition with Machine Learning Using EEG Signals
Authors: Omid Bazgir, Zeynab Mohammadi, Seyed Amir Hassan Habibi
Date: 2019-03-18 00:00:00
Tasks: BIG-bench Machine Learning, EEG, Electroencephalogram (EEG), Emotion Recognition

In this research, an emotion recognition system is developed based on valence/arousal model using electroencephalography (EEG) signals. EEG signals are decomposed into the gamma, beta, alpha and theta frequency bands using discrete wavelet transform (DWT), and spectral features are extracted from each frequency band. Principle component analysis (PCA) is applied to the extracted features by preserving the same dimensionality, as a transform, to make the features mutually uncorrelated. Support vector machine (SVM), K-nearest neighbor (KNN) and artificial neural network (ANN) are used to classify emotional states. The cross-validated SVM with radial basis function (RBF) kernel using extracted features of 10 EEG channels, performs with 91.3% accuracy for arousal and 91.1% accuracy for valence, both in the beta frequency band. Our approach shows better performance compared to existing algorithms applied to the "DEAP" dataset.
Expectation-Aware Planning: A Unifying Framework for Synthesizing and Executing Self-Explaining Plans for Human-Aware Planning
Authors: Sarath Sreedharan, Tathagata Chakraborti, Christian Muise, Subbarao Kambhampati
Date: 2019-03-18 00:00:00
Tasks: Decision Making

In this work, we present a new planning formalism called Expectation-Aware planning for decision making with humans in the loop where the human's expectations about an agent may differ from the agent's own model. We show how this formulation allows agents to not only leverage existing strategies for handling model differences but can also exhibit novel behaviors that are generated through the combination of these different strategies. Our formulation also reveals a deep connection to existing approaches in epistemic planning. Specifically, we show how we can leverage classical planning compilations for epistemic planning to solve Expectation-Aware planning problems. To the best of our knowledge, the proposed formulation is the first complete solution to decision-making in the presence of diverging user expectations that is amenable to a classical planning compilation while successfully combining previous works on explanation and explicability. We empirically show how our approach provides a computational advantage over existing approximate approaches that unnecessarily try to search in the space of models while also failing to facilitate the full gamut of behaviors enabled by our framework.
Distributed stochastic optimization with gradient tracking over strongly-connected networks
Authors: Ran Xin, Anit Kumar Sahu, Usman A. Khan, Soummya Kar
Date: 2019-03-18 00:00:00
Tasks: Stochastic Optimization

In this paper, we study distributed stochastic optimization to minimize a sum
of smooth and strongly-convex local cost functions over a network of agents,
communicating over a strongly-connected graph. Assuming that each agent has
access to a stochastic first-order oracle ($\mathcal{SFO}$), we propose a novel
distributed method, called $\mathcal{S}$-$\mathcal{AB}$, where each agent uses
an auxiliary variable to asymptotically track the gradient of the global cost
in expectation. The $\mathcal{S}$-$\mathcal{AB}$ algorithm employs row- and
column-stochastic weights simultaneously to ensure both consensus and
optimality. Since doubly-stochastic weights are not used,
$\mathcal{S}$-$\mathcal{AB}$ is applicable to arbitrary strongly-connected
graphs. We show that under a sufficiently small constant step-size,
$\mathcal{S}$-$\mathcal{AB}$ converges linearly (in expected mean-square sense)
to a neighborhood of the global minimizer. We present numerical simulations
based on real-world data sets to illustrate the theoretical results.
Intelligent Solution System towards Parts Logistics Optimization
Authors: Yaoting Huang, Bo-Yu Chen, Wenlian Lu, Zhong-Xiao Jin, Ren Zheng
Date: 2019-03-18 00:00:00
Tasks: global-optimization, Management

Due to the complication of the presented problem, intelligent algorithms show
great power to solve the parts logistics optimization problem related to the
vehicle routing problem (VRP). However, most of the existing research to VRP
are incomprehensive and failed to solve a real-work parts logistics problem.
  In this work, towards SAIC logistics problem, we propose a systematic
solution to this 2-Dimensional Loading Capacitated Multi-Depot Heterogeneous
VRP with Time Windows by integrating diverse types of intelligent algorithms,
including, a heuristic algorithm to initialize feasible logistics planning
schemes by imitating manual planning, the core Tabu Search algorithm for global
optimization, accelerated by a novel bundle technique, heuristically algorithms
for routing, packing and queuing associated, and a heuristic post-optimization
process to promote the optimal solution.
  Based on these algorithms, the SAIC Motor has successfully established an
intelligent management system to give a systematic solution for the parts
logistics planning, superior than manual planning in its performance,
customizability and expandability.
Annealing for Distributed Global Optimization
Authors: Brian Swenson, Soummya Kar, H. Vincent Poor, Jose' M. F. Moura
Date: 2019-03-18 00:00:00
Tasks: global-optimization

The paper proves convergence to global optima for a class of distributed
algorithms for nonconvex optimization in network-based multi-agent settings.
Agents are permitted to communicate over a time-varying undirected graph. Each
agent is assumed to possess a local objective function (assumed to be smooth,
but possibly nonconvex). The paper considers algorithms for optimizing the sum
function. A distributed algorithm of the consensus+innovations type is proposed
which relies on first-order information at the agent level. Under appropriate
conditions on network connectivity and the cost objective, convergence to the
set of global optima is achieved by an annealing-type approach, with decaying
Gaussian noise independently added into each agent's update step. It is shown
that the proposed algorithm converges in probability to the set of global
minima of the sum function.
Complex Scene Classification of PolSAR Imagery based on a Self-paced Learning Approach
Authors: Wenshuai Chen, Shuiping Gou, Xinlin Wang, Licheng Jiao, Changzhe Jiao, Alina Zare
Date: 2019-03-18 00:00:00
Tasks: General Classification, image-classification, Image Classification, Scene Classification

Existing polarimetric synthetic aperture radar (PolSAR) image classification
methods cannot achieve satisfactory performance on complex scenes characterized
by several types of land cover with significant levels of noise or similar
scattering properties across land cover types. Hence, we propose a supervised
classification method aimed at constructing a classifier based on self-paced
learning (SPL). SPL has been demonstrated to be effective at dealing with
complex data while providing classifier. In this paper, a novel Support Vector
Machine (SVM) algorithm based on SPL with neighborhood constraints (SVM_SPLNC)
is proposed. The proposed method leverages the easiest samples first to obtain
an initial parameter vector. Then, more complex samples are gradually
incorporated to update the parameter vector iteratively. Moreover, neighborhood
constraints are introduced during the training process to further improve
performance. Experimental results on three real PolSAR images show that the
proposed method performs well on complex scenes.
Counterpoint by Convolution
Authors: Cheng-Zhi Anna Huang, Tim Cooijmans, Adam Roberts, Aaron Courville, Douglas Eck
Date: 2019-03-18 00:00:00
Tasks: Music Generation, Music Modeling

Machine learning models of music typically break up the task of composition
into a chronological process, composing a piece of music in a single pass from
beginning to end. On the contrary, human composers write music in a nonlinear
fashion, scribbling motifs here and there, often revisiting choices previously
made. In order to better approximate this process, we train a convolutional
neural network to complete partial musical scores, and explore the use of
blocked Gibbs sampling as an analogue to rewriting. Neither the model nor the
generative procedure are tied to a particular causal direction of composition.
Our model is an instance of orderless NADE (Uria et al., 2014), which allows
more direct ancestral sampling. However, we find that Gibbs sampling greatly
improves sample quality, which we demonstrate to be due to some conditional
distributions being poorly modeled. Moreover, we show that even the cheap
approximate blocked Gibbs procedure from Yao et al. (2014) yields better
samples than ancestral sampling, based on both log-likelihood and human
evaluation.
An End-to-End Joint Unsupervised Learning of Deep Model and Pseudo-Classes for Remote Sensing Scene Representation
Authors: Zhiqiang Gong, Ping Zhong, Weidong Hu, Fang Liu, Bingwei Hui
Date: 2019-03-18 00:00:00

This work develops a novel end-to-end deep unsupervised learning method based
on convolutional neural network (CNN) with pseudo-classes for remote sensing
scene representation. First, we introduce center points as the centers of the
pseudo classes and the training samples can be allocated with pseudo labels
based on the center points. Therefore, the CNN model, which is used to extract
features from the scenes, can be trained supervised with the pseudo labels.
Moreover, a pseudo-center loss is developed to decrease the variance between
the samples and the corresponding pseudo center point. The pseudo-center loss
is important since it can update both the center points with the training
samples and the CNN model with the center points in the training process
simultaneously. Finally, joint learning of the pseudo-center loss and the
pseudo softmax loss which is formulated with the samples and the pseudo labels
is developed for unsupervised remote sensing scene representation to obtain
discriminative representations from the scenes. Experiments are conducted over
two commonly used remote sensing scene datasets to validate the effectiveness
of the proposed method and the experimental results show the superiority of the
proposed method when compared with other state-of-the-art methods.
Multidimensional ground reaction forces and moments from wearable sensor accelerations via deep learning
Authors: William R. Johnson, Ajmal Mian, Mark A. Robinson, Jasper Verheul, David G. Lloyd, Jacqueline A. Alderson
Date: 2019-03-18 00:00:00

Monitoring athlete internal workload exposure, including prevention of catastrophic non-contact knee injuries, relies on the existence of a custom early-warning detection system. This system must be able to estimate accurate, reliable, and valid musculoskeletal joint loads, for sporting maneuvers in near real-time and during match play. However, current methods are constrained to laboratory instrumentation, are labor and cost intensive, and require highly trained specialist knowledge, thereby limiting their ecological validity and wider deployment. An informative next step towards this goal would be a new method to obtain ground kinetics in the field. Here we show that kinematic data obtained from wearable sensor accelerometers, in lieu of embedded force platforms, can leverage recent supervised learning techniques to predict near real-time multidimensional ground reaction forces and moments (GRF/M). Competing convolutional neural network (CNN) deep learning models were trained using laboratory-derived stance phase GRF/M data and simulated sensor accelerations for running and sidestepping maneuvers derived from nearly half a million legacy motion trials. Then, predictions were made from each model driven by five sensor accelerations recorded during independent inter-laboratory data capture sessions. The proposed deep learning workbench achieved correlations to ground truth, by maximum discrete GRF component, of vertical Fz 0.97, anterior Fy 0.96 (both running), and lateral Fx 0.87 (sidestepping), with the strongest mean recorded across GRF components 0.89, and for GRM 0.65 (both sidestepping). These best-case correlations indicate the plausibility of the approach although the range of results was disappointing. The goal to accurately estimate near real-time on-field GRF/M will be improved by the lessons learned in this study [truncated].
A Control Lyapunov Perspective on Episodic Learning via Projection to State Stability
Authors: Andrew J. Taylor, Victor D. Dorobantu, Meera Krishnamoorthy, Hoang M. Le, Yisong Yue, Aaron D. Ames
Date: 2019-03-18 00:00:00

The goal of this paper is to understand the impact of learning on control
synthesis from a Lyapunov function perspective. In particular, rather than
consider uncertainties in the full system dynamics, we employ Control Lyapunov
Functions (CLFs) as low-dimensional projections. To understand and characterize
the uncertainty that these projected dynamics introduce in the system, we
introduce a new notion: Projection to State Stability (PSS). PSS can be viewed
as a variant of Input to State Stability defined on projected dynamics, and
enables characterizing robustness of a CLF with respect to the data used to
learn system uncertainties. We use PSS to bound uncertainty in affine control,
and demonstrate that a practical episodic learning approach can use PSS to
characterize uncertainty in the CLF for robust control synthesis.
AttoNets: Compact and Efficient Deep Neural Networks for the Edge via Human-Machine Collaborative Design
Authors: Alexander Wong, Zhong Qiu Lin, Brendan Chwyl
Date: 2019-03-18 00:00:00
Tasks: object-detection, Object Detection, Object Recognition, Semantic Segmentation

While deep neural networks have achieved state-of-the-art performance across
a large number of complex tasks, it remains a big challenge to deploy such
networks for practical, on-device edge scenarios such as on mobile devices,
consumer devices, drones, and vehicles. In this study, we take a deeper
exploration into a human-machine collaborative design approach for creating
highly efficient deep neural networks through a synergy between principled
network design prototyping and machine-driven design exploration. The efficacy
of human-machine collaborative design is demonstrated through the creation of
AttoNets, a family of highly efficient deep neural networks for on-device edge
deep learning. Each AttoNet possesses a human-specified network-level
macro-architecture comprising of custom modules with unique machine-designed
module-level macro-architecture and micro-architecture designs, all driven by
human-specified design requirements. Experimental results for the task of
object recognition showed that the AttoNets created via human-machine
collaborative design has significantly fewer parameters and computational costs
than state-of-the-art networks designed for efficiency while achieving
noticeably higher accuracy (with the smallest AttoNet achieving ~1.8% higher
accuracy while requiring ~10x fewer multiply-add operations and parameters than
MobileNet-V1). Furthermore, the efficacy of the AttoNets is demonstrated for
the task of instance-level object segmentation and object detection, where an
AttoNet-based Mask R-CNN network was constructed with significantly fewer
parameters and computational costs (~5x fewer multiply-add operations and ~2x
fewer parameters) than a ResNet-50 based Mask R-CNN network.
Model-Free Model Reconciliation
Authors: Sarath Sreedharan, Alberto Olmo, Aditya Prasad Mishra, Subbarao Kambhampati
Date: 2019-03-17 00:00:00
Tasks: Decision Making, model, Philosophy

Designing agents capable of explaining complex sequential decisions remain a
significant open problem in automated decision-making. Recently, there has been
a lot of interest in developing approaches for generating such explanations for
various decision-making paradigms. One such approach has been the idea of {\em
explanation as model-reconciliation}. The framework hypothesizes that one of
the common reasons for the user's confusion could be the mismatch between the
user's model of the task and the one used by the system to generate the
decisions. While this is a general framework, most works that have been
explicitly built on this explanatory philosophy have focused on settings where
the model of user's knowledge is available in a declarative form. Our goal in
this paper is to adapt the model reconciliation approach to the cases where
such user models are no longer explicitly provided. We present a simple and
easy to learn labeling model that can help an explainer decide what information
could help achieve model reconciliation between the user and the agent.
Robust superpixels using color and contour features along linear path
Authors: Rémi Giraud, Vinh-Thong Ta, Nicolas Papadakis
Date: 2019-03-17 00:00:00
Tasks: Superpixels

Superpixel decomposition methods are widely used in computer vision and image
processing applications. By grouping homogeneous pixels, the accuracy can be
increased and the decrease of the number of elements to process can drastically
reduce the computational burden. For most superpixel methods, a trade-off is
computed between 1) color homogeneity, 2) adherence to the image contours and
3) shape regularity of the decomposition. In this paper, we propose a framework
that jointly enforces all these aspects and provides accurate and regular
Superpixels with Contour Adherence using Linear Path (SCALP). During the
decomposition, we propose to consider color features along the linear path
between the pixel and the corresponding superpixel barycenter. A contour prior
is also used to prevent the crossing of image boundaries when associating a
pixel to a superpixel. Finally, in order to improve the decomposition accuracy
and the robustness to noise, we propose to integrate the pixel neighborhood
information, while preserving the same computational complexity. SCALP is
extensively evaluated on standard segmentation dataset, and the obtained
results outperform the ones of the state-of-the-art methods. SCALP is also
extended for supervoxel decomposition on MRI images.
Learning-Based Animation of Clothing for Virtual Try-On
Authors: Igor Santesteban, Miguel A. Otaduy, Dan Casas
Date: 2019-03-17 00:00:00
Tasks: Virtual Try-on

This paper presents a learning-based clothing animation method for highly
efficient virtual try-on simulation. Given a garment, we preprocess a rich
database of physically-based dressed character simulations, for multiple body
shapes and animations. Then, using this database, we train a learning-based
model of cloth drape and wrinkles, as a function of body shape and dynamics. We
propose a model that separates global garment fit, due to body shape, from
local garment wrinkles, due to both pose dynamics and body shape. We use a
recurrent neural network to regress garment wrinkles, and we achieve highly
plausible nonlinear effects, in contrast to the blending artifacts suffered by
previous methods. At runtime, dynamic virtual try-on animations are produced in
just a few milliseconds for garments with thousands of triangles. We show
qualitative and quantitative analysis of results