pwc-archive/papers-with-abstracts huggingface dataset:
An efficient genetic algorithm for large-scale transmit power control of dense industrial wireless networks
Authors: Xu Gong, David Plets, Emmeric Tanghe, Toon De Pessemier, Luc Martens, Wout Joseph
Date: 2017-08-12 00:00:00

The industrial wireless local area network (IWLAN) is increasingly dense, not
only due to the penetration of wireless applications into factories and
warehouses, but also because of the rising need of redundancy for robust
wireless coverage. Instead of powering on all the nodes with the maximal
transmit power, it becomes an unavoidable challenge to control the transmit
power of all wireless nodes on a large scale, in order to reduce interference
and adapt coverage to the latest shadowing effects in the environment.
Therefore, this paper proposes an efficient genetic algorithm (GA) to solve
this transmit power control (TPC) problem for dense IWLANs, named GATPC.
Effective population initialization, crossover and mutation, parallel computing
as well as dedicated speedup measures are introduced to tailor GATPC for the
large-scale optimization that is intrinsically involved in this problem. In
contrast to most coverage-related optimization algorithms which cannot deal
with the prevalent shadowing effects in harsh industrial indoor environments,
an empirical one-slope path loss model considering three-dimensional obstacle
shadowing effects is used in GATPC, in order to enable accurate yet simple
coverage prediction. Experimental validation and numerical experiments in real
industrial cases show the promising performance of GATPC in terms of
scalability to a hyper-large scale, up to 37-times speedup in resolution
runtime, and solution quality to achieve adaptive coverage and to minimize
interference.
Deep Steering: Learning End-to-End Driving Model from Spatial and Temporal Visual Cues
Authors: Lu Chi, Yadong Mu
Date: 2017-08-12 00:00:00
Tasks: Autonomous Driving, object-detection, Object Detection

In recent years, autonomous driving algorithms using low-cost vehicle-mounted
cameras have attracted increasing endeavors from both academia and industry.
There are multiple fronts to these endeavors, including object detection on
roads, 3-D reconstruction etc., but in this work we focus on a vision-based
model that directly maps raw input images to steering angles using deep
networks. This represents a nascent research topic in computer vision. The
technical contributions of this work are three-fold. First, the model is
learned and evaluated on real human driving videos that are time-synchronized
with other vehicle sensors. This differs from many prior models trained from
synthetic data in racing games. Second, state-of-the-art models, such as
PilotNet, mostly predict the wheel angles independently on each video frame,
which contradicts common understanding of driving as a stateful process.
Instead, our proposed model strikes a combination of spatial and temporal cues,
jointly investigating instantaneous monocular camera observations and vehicle's
historical states. This is in practice accomplished by inserting
carefully-designed recurrent units (e.g., LSTM and Conv-LSTM) at proper network
layers. Third, to facilitate the interpretability of the learned model, we
utilize a visual back-propagation scheme for discovering and visualizing image
regions crucially influencing the final steering prediction. Our experimental
study is based on about 6 hours of human driving data provided by Udacity.
Comprehensive quantitative evaluations demonstrate the effectiveness and
robustness of our model, even under scenarios like drastic lighting changes and
abrupt turning. The comparison with other state-of-the-art models clearly
reveals its superior performance in predicting the due wheel angle for a
self-driving car.
Natural Language Generation for Spoken Dialogue System using RNN Encoder-Decoder Networks
Authors: Van-Khanh Tran, Le-Minh Nguyen
Date: 2017-06-01 00:00:00
Proceeding: CONLL 2017 8
Tasks: Decoder, Sentence, Text Generation

Natural language generation (NLG) is a critical component in a spoken
dialogue system. This paper presents a Recurrent Neural Network based
Encoder-Decoder architecture, in which an LSTM-based decoder is introduced to
select, aggregate semantic elements produced by an attention mechanism over the
input elements, and to produce the required utterances. The proposed generator
can be jointly trained both sentence planning and surface realization to
produce natural language sentences. The proposed model was extensively
evaluated on four different NLG datasets. The experimental results showed that
the proposed generators not only consistently outperform the previous methods
across all the NLG domains but also show an ability to generalize from a new,
unseen domain and learn from multi-domain datasets.
Direct-Manipulation Visualization of Deep Networks
Authors: Daniel Smilkov, Shan Carter, D. Sculley, Fernanda B. Viégas, Martin Wattenberg
Date: 2017-08-12 00:00:00

The recent successes of deep learning have led to a wave of interest from
non-experts. Gaining an understanding of this technology, however, is
difficult. While the theory is important, it is also helpful for novices to
develop an intuitive feel for the effect of different hyperparameters and
structural variations. We describe TensorFlow Playground, an interactive, open
sourced visualization that allows users to experiment via direct manipulation
rather than coding, enabling them to quickly build an intuition about neural
nets.
Real-time Load Prediction with High Velocity Smart Home Data Stream
Authors: Christoph Doblander, Martin Strohbach, Holger Ziekow, Hans-Arno Jacobsen
Date: 2017-08-12 00:00:00
Tasks: Prediction, Vocal Bursts Intensity Prediction

This paper addresses the use of smart-home sensor streams for continuous
prediction of energy loads of individual households which participate as an
agent in local markets. We introduces a new device level energy consumption
dataset recorded over three years wich includes high resolution energy
measurements from electrical devices collected within a pilot program. Using
data from that pilot, we analyze the applicability of various machine learning
mechanisms for continuous load prediction. Specifically, we address short-term
load prediction that is required for load balancing in electrical micro-grids.
We report on the prediction performance and the computational requirements of a
broad range of prediction mechanisms. Furthermore we present an architecture
and experimental evaluation when this prediction is applied in the stream.
Crowdsourcing with Unsure Option
Authors: Yao-Xiang Ding, Zhi-Hua Zhou
Date: 2016-09-01 00:00:00

One of the fundamental problems in crowdsourcing is the trade-off between the
number of the workers needed for high-accuracy aggregation and the budget to
pay. For saving budget, it is important to ensure high quality of the
crowd-sourced labels, hence the total cost on label collection will be reduced.
Since the self-confidence of the workers often has a close relationship with
their abilities, a possible way for quality control is to request the workers
to return the labels only when they feel confident, by means of providing
unsure option to them. On the other hand, allowing workers to choose unsure
option also leads to the potential danger of budget waste. In this work, we
propose the analysis towards understanding when providing the unsure option
indeed leads to significant cost reduction, as well as how the confidence
threshold is set. We also propose an online mechanism, which is alternative for
threshold selection when the estimation of the crowd ability distribution is
difficult.
Noisy Softmax: Improving the Generalization Ability of DCNN via Postponing the Early Softmax Saturation
Authors: Binghui Chen, Weihong Deng, Junping Du
Date: 2017-08-12 00:00:00
Proceeding: CVPR 2017 7

Over the past few years, softmax and SGD have become a commonly used
component and the default training strategy in CNN frameworks, respectively.
However, when optimizing CNNs with SGD, the saturation behavior behind softmax
always gives us an illusion of training well and then is omitted. In this
paper, we first emphasize that the early saturation behavior of softmax will
impede the exploration of SGD, which sometimes is a reason for model converging
at a bad local-minima, then propose Noisy Softmax to mitigating this early
saturation issue by injecting annealed noise in softmax during each iteration.
This operation based on noise injection aims at postponing the early saturation
and further bringing continuous gradients propagation so as to significantly
encourage SGD solver to be more exploratory and help to find a better
local-minima. This paper empirically verifies the superiority of the early
softmax desaturation, and our method indeed improves the generalization ability
of CNN model by regularization. We experimentally find that this early
desaturation helps optimization in many tasks, yielding state-of-the-art or
competitive results on several popular benchmark datasets.
Multi-Stage Feature Selection Based Intelligent Classifier for Classification of Incipient Stage Fire in Building
Authors: Allan Melvin Andrew, Ammar Zakaria, Shaharil Mad Saad, Ali Yeon Md Shakaff
Date: 2017-08-12 00:00:00
Tasks: Classification, Dimensionality Reduction, feature selection, Fire Detection, General Classification

In this study, an early fire detection algorithm has been proposed based on
low cost array sensing system, utilizing gas sensors, dust particles and
ambient sensors such as temperature and humidity sensor. The odor or
smell-print emanated from various fire sources and building construction
materials at early stage are measured. For this purpose, odor profile data from
five common fire sources and three common building construction materials were
used to develop the classification model. Normalized feature extractions of the
smell print data were performed before subjected to prediction classifier.
These features represent the odor signals in the time domain. The obtained
features undergo the proposed multi-stage feature selection technique and
lastly, further reduced by Principal Component Analysis (PCA), a dimension
reduction technique. The hybrid PCA-PNN based approach has been applied on
different datasets from in-house developed system and the portable electronic
nose unit. Experimental classification results show that the dimension
reduction process performed by PCA has improved the classification accuracy and
provided high reliability, regardless of ambient temperature and humidity
variation, baseline sensor drift, the different gas concentration level and
exposure towards different heating temperature range.
Calipso: Physics-based Image and Video Editing through CAD Model Proxies
Authors: Nazim Haouchine, Frederick Roy, Hadrien Courtecuisse, Matthias Nießner, Stephane Cotin
Date: 2017-08-12 00:00:00
Tasks: Video Editing

We present Calipso, an interactive method for editing images and videos in a
physically-coherent manner. Our main idea is to realize physics-based
manipulations by running a full physics simulation on proxy geometries given by
non-rigidly aligned CAD models. Running these simulations allows us to apply
new, unseen forces to move or deform selected objects, change physical
parameters such as mass or elasticity, or even add entire new objects that
interact with the rest of the underlying scene. In Calipso, the user makes
edits directly in 3D; these edits are processed by the simulation and then
transfered to the target 2D content using shape-to-image correspondences in a
photo-realistic rendering process. To align the CAD models, we introduce an
efficient CAD-to-image alignment procedure that jointly minimizes for rigid and
non-rigid alignment while preserving the high-level structure of the input
shape. Moreover, the user can choose to exploit image flow to estimate scene
motion, producing coherent physical behavior with ambient dynamics. We
demonstrate Calipso's physics-based editing on a wide range of examples
producing myriad physical behavior while preserving geometric and visual
consistency.
Cross-Sentence N-ary Relation Extraction with Graph LSTMs
Authors: Nanyun Peng, Hoifung Poon, Chris Quirk, Kristina Toutanova, Wen-tau Yih
Date: 2017-08-12 00:00:00
Proceeding: TACL 2017 1
Tasks: Multi-Task Learning, Relation, Relation Extraction, Sentence

Past work in relation extraction has focused on binary relations in single
sentences. Recent NLP inroads in high-value domains have sparked interest in
the more general setting of extracting n-ary relations that span multiple
sentences. In this paper, we explore a general relation extraction framework
based on graph long short-term memory networks (graph LSTMs) that can be easily
extended to cross-sentence n-ary relation extraction. The graph formulation
provides a unified way of exploring different LSTM approaches and incorporating
various intra-sentential and inter-sentential dependencies, such as sequential,
syntactic, and discourse relations. A robust contextual representation is
learned for the entities, which serves as input to the relation classifier.
This simplifies handling of relations with arbitrary arity, and enables
multi-task learning with related relations. We evaluate this framework in two
important precision medicine settings, demonstrating its effectiveness with
both conventional supervised learning and distant supervision. Cross-sentence
extraction produced larger knowledge bases. and multi-task learning
significantly improved extraction accuracy. A thorough analysis of various LSTM
approaches yielded useful insight the impact of linguistic analysis on
extraction accuracy.
Online Convex Optimization with Stochastic Constraints
Authors: Hao Yu, Michael J. Neely, Xiaohan Wei
Date: 2017-08-12 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Scheduling

This paper considers online convex optimization (OCO) with stochastic
constraints, which generalizes Zinkevich's OCO over a known simple fixed set by
introducing multiple stochastic functional constraints that are i.i.d.
generated at each round and are disclosed to the decision maker only after the
decision is made. This formulation arises naturally when decisions are
restricted by stochastic environments or deterministic environments with noisy
observations. It also includes many important problems as special cases, such
as OCO with long term constraints, stochastic constrained convex optimization,
and deterministic constrained convex optimization. To solve this problem, this
paper proposes a new algorithm that achieves $O(\sqrt{T})$ expected regret and
constraint violations and $O(\sqrt{T}\log(T))$ high probability regret and
constraint violations. Experiments on a real-world data center scheduling
problem further verify the performance of the new algorithm.
Facial Expression Recognition Using a Hybrid CNN-SIFT Aggregator
Authors: Mundher Al-Shabi, Wooi Ping Cheah, Tee Connie
Date: 2016-08-09 00:00:00
Tasks: Face Recognition, Facial Expression Recognition, Facial Expression Recognition (FER), Small Data Image Classification

Deriving an effective facial expression recognition component is important
for a successful human-computer interaction system. Nonetheless, recognizing
facial expression remains a challenging task. This paper describes a novel
approach towards facial expression recognition task. The proposed method is
motivated by the success of Convolutional Neural Networks (CNN) on the face
recognition problem. Unlike other works, we focus on achieving good accuracy
while requiring only a small sample data for training. Scale Invariant Feature
Transform (SIFT) features are used to increase the performance on small data as
SIFT does not require extensive training data to generate useful features. In
this paper, both Dense SIFT and regular SIFT are studied and compared when
merged with CNN features. Moreover, an aggregator of the models is developed.
The proposed approach is tested on the FER-2013 and CK+ datasets. Results
demonstrate the superiority of CNN with Dense SIFT over conventional CNN and
CNN with SIFT. The accuracy even increased when all the models are aggregated
which generates state-of-art results on FER-2013 and CK+ datasets, where it
achieved 73.4% on FER-2013 and 99.1% on CK+.
Face Parsing via a Fully-Convolutional Continuous CRF Neural Network
Authors: Lei Zhou, Zhi Liu, Xiangjian He
Date: 2017-08-12 00:00:00
Tasks: Face Parsing

In this work, we address the face parsing task with a Fully-Convolutional
continuous CRF Neural Network (FC-CNN) architecture. In contrast to previous
face parsing methods that apply region-based subnetwork hundreds of times, our
FC-CNN is fully convolutional with high segmentation accuracy. To achieve this
goal, FC-CNN integrates three subnetworks, a unary network, a pairwise network
and a continuous Conditional Random Field (C-CRF) network into a unified
framework. The high-level semantic information and low-level details across
different convolutional layers are captured by the convolutional and
deconvolutional structures in the unary network. The semantic edge context is
learnt by the pairwise network branch to construct pixel-wise affinity. Based
on a differentiable superpixel pooling layer and a differentiable C-CRF layer,
the unary network and pairwise network are combined via a novel continuous CRF
network to achieve spatial consistency in both training and test procedure of a
deep neural network. Comprehensive evaluations on LFW-PL and HELEN datasets
demonstrate that FC-CNN achieves better performance over the other
state-of-arts for accurate face labeling on challenging images.
Deep Learning for Passive Synthetic Aperture Radar
Authors: Bariscan Yonel, Eric Mason, Birsen Yazıcı
Date: 2017-08-12 00:00:00
Tasks: Deep Learning, Image Reconstruction

We introduce a deep learning (DL) framework for inverse problems in imaging,
and demonstrate the advantages and applicability of this approach in passive
synthetic aperture radar (SAR) image reconstruction. We interpret image recon-
struction as a machine learning task and utilize deep networks as forward and
inverse solvers for imaging. Specifically, we design a recurrent neural network
(RNN) architecture as an inverse solver based on the iterations of proximal
gradient descent optimization methods. We further adapt the RNN architecture to
image reconstruction problems by transforming the network into a recurrent
auto-encoder, thereby allowing for unsupervised training. Our DL based inverse
solver is particularly suitable for a class of image formation problems in
which the forward model is only partially known. The ability to learn forward
models and hyper parameters combined with unsupervised training approach
establish our recurrent auto-encoder suitable for real world applications. We
demonstrate the performance of our method in passive SAR image reconstruction.
In this regime a source of opportunity, with unknown location and transmitted
waveform, is used to illuminate a scene of interest. We investigate recurrent
auto- encoder architecture based on the 1 and 0 constrained least- squares
problem. We present a projected stochastic gradient descent based training
scheme which incorporates constraints of the unknown model parameters. We
demonstrate through extensive numerical simulations that our DL based approach
out performs conventional sparse coding methods in terms of computation and
reconstructed image quality, specifically, when no information about the
transmitter is available.
Gram-CTC: Automatic Unit Selection and Target Decomposition for Sequence Labelling
Authors: Hairong Liu, Zhenyao Zhu, Xiangang Li, Sanjeev Satheesh
Date: 2017-03-01 00:00:00
Proceeding: ICML 2017 8
Tasks: Computational Efficiency, speech-recognition, Speech Recognition, TAR

Most existing sequence labelling models rely on a fixed decomposition of a
target sequence into a sequence of basic units. These methods suffer from two
major drawbacks: 1) the set of basic units is fixed, such as the set of words,
characters or phonemes in speech recognition, and 2) the decomposition of
target sequences is fixed. These drawbacks usually result in sub-optimal
performance of modeling sequences. In this pa- per, we extend the popular CTC
loss criterion to alleviate these limitations, and propose a new loss function
called Gram-CTC. While preserving the advantages of CTC, Gram-CTC automatically
learns the best set of basic units (grams), as well as the most suitable
decomposition of tar- get sequences. Unlike CTC, Gram-CTC allows the model to
output variable number of characters at each time step, which enables the model
to capture longer term dependency and improves the computational efficiency. We
demonstrate that the proposed Gram-CTC improves CTC in terms of both
performance and efficiency on the large vocabulary speech recognition task at
multiple scales of data, and that with Gram-CTC we can outperform the
state-of-the-art on a standard speech benchmark.
OpenML Benchmarking Suites
Authors: Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Pieter Gijsbers, Frank Hutter, Michel Lang, Rafael G. Mantovani, Jan N. van Rijn, Joaquin Vanschoren
Date: 2017-08-11 00:00:00
Tasks: Benchmarking, BIG-bench Machine Learning, General Classification

Machine learning research depends on objectively interpretable, comparable, and reproducible algorithm benchmarks. We advocate the use of curated, comprehensive suites of machine learning tasks to standardize the setup, execution, and reporting of benchmarks. We enable this through software tools that help to create and leverage these benchmarking suites. These are seamlessly integrated into the OpenML platform, and accessible through interfaces in Python, Java, and R. OpenML benchmarking suites (a) are easy to use through standardized data formats, APIs, and client libraries; (b) come with extensive meta-information on the included datasets; and (c) allow benchmarks to be shared and reused in future studies. We then present a first, carefully curated and practical benchmarking suite for classification: the OpenML Curated Classification benchmarking suite 2018 (OpenML-CC18). Finally, we discuss use cases and applications which demonstrate the usefulness of OpenML benchmarking suites and the OpenML-CC18 in particular.