pwc-archive/papers-with-abstracts huggingface dataset:
Optimizing PID parameters with machine learning
Authors: Adam Nyberg
Date: 2017-09-26 00:00:00
Tasks: BIG-bench Machine Learning

This paper examines the Evolutionary programming (EP) method for optimizing
PID parameters. PID is the most common type of regulator within control theory,
partly because it's relatively simple and yields stable results for most
applications. The p, i and d parameters vary for each application; therefore,
choosing the right parameters is crucial for obtaining good results but also
somewhat difficult. EP is a derivative-free optimization algorithm which makes
it suitable for PID optimization. The experiments in this paper demonstrate the
power of EP to solve the problem of optimizing PID parameters without getting
stuck in local minimums.
Dataset Construction via Attention for Aspect Term Extraction with Distant Supervision
Authors: Athanasios Giannakopoulos, Diego Antognini, Claudiu Musat, Andreea Hossmann, Michael Baeriswyl
Date: 2017-09-26 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentence, Sentiment Analysis, Term Extraction

Aspect Term Extraction (ATE) detects opinionated aspect terms in sentences or
text spans, with the end goal of performing aspect-based sentiment analysis.
The small amount of available datasets for supervised ATE and the fact that
they cover only a few domains raise the need for exploiting other data sources
in new and creative ways. Publicly available review corpora contain a plethora
of opinionated aspect terms and cover a larger domain spectrum. In this paper,
we first propose a method for using such review corpora for creating a new
dataset for ATE. Our method relies on an attention mechanism to select
sentences that have a high likelihood of containing actual opinionated aspects.
We thus improve the quality of the extracted aspects. We then use the
constructed dataset to train a model and perform ATE with distant supervision.
By evaluating on human annotated datasets, we prove that our method achieves a
significantly improved performance over various unsupervised and supervised
baselines. Finally, we prove that sentence selection matters when it comes to
creating new datasets for ATE. Specifically, we show that, using a set of
selected sentences leads to higher ATE performance compared to using the whole
sentence set.
Understanding Infographics through Textual and Visual Tag Prediction
Authors: Zoya Bylinskii, Sami Alsheikh, Spandan Madan, Adria Recasens, Kimberli Zhong, Hanspeter Pfister, Fredo Durand, Aude Oliva
Date: 2017-09-26 00:00:00
Tasks: Diagnostic, Prediction, TAG

We introduce the problem of visual hashtag discovery for infographics:
extracting visual elements from an infographic that are diagnostic of its
topic. Given an infographic as input, our computational approach automatically
outputs textual and visual elements predicted to be representative of the
infographic content. Concretely, from a curated dataset of 29K large
infographic images sampled across 26 categories and 391 tags, we present an
automated two step approach. First, we extract the text from an infographic and
use it to predict text tags indicative of the infographic content. And second,
we use these predicted text tags as a supervisory signal to localize the most
diagnostic visual elements from within the infographic i.e. visual hashtags. We
report performances on a categorization and multi-label tag prediction problem
and compare our proposed visual hashtags to human annotations.
Mutual Alignment Transfer Learning
Authors: Markus Wulfmeier, Ingmar Posner, Pieter Abbeel
Date: 2017-07-25 00:00:00
Tasks: Reinforcement Learning, Transfer Learning

Training robots for operation in the real world is a complex, time consuming
and potentially expensive task. Despite significant success of reinforcement
learning in games and simulations, research in real robot applications has not
been able to match similar progress. While sample complexity can be reduced by
training policies in simulation, such policies can perform sub-optimally on the
real platform given imperfect calibration of model dynamics. We present an
approach -- supplemental to fine tuning on the real robot -- to further benefit
from parallel access to a simulator during training and reduce sample
requirements on the real robot. The developed approach harnesses auxiliary
rewards to guide the exploration for the real world agent based on the
proficiency of the agent in simulation and vice versa. In this context, we
demonstrate empirically that the reciprocal alignment for both agents provides
further benefit as the agent in simulation can adjust to optimize its behaviour
for states commonly visited by the real-world agent.
EDEN: Evolutionary Deep Networks for Efficient Machine Learning
Authors: Emmanuel Dufourq, Bruce A. Bassett
Date: 2017-09-26 00:00:00
Tasks: BIG-bench Machine Learning, GPU, Sentiment Analysis, Sentiment Classification

Deep neural networks continue to show improved performance with increasing
depth, an encouraging trend that implies an explosion in the possible
permutations of network architectures and hyperparameters for which there is
little intuitive guidance. To address this increasing complexity, we propose
Evolutionary DEep Networks (EDEN), a computationally efficient
neuro-evolutionary algorithm which interfaces to any deep neural network
platform, such as TensorFlow. We show that EDEN evolves simple yet successful
architectures built from embedding, 1D and 2D convolutional, max pooling and
fully connected layers along with their hyperparameters. Evaluation of EDEN
across seven image and sentiment classification datasets shows that it reliably
finds good networks -- and in three cases achieves state-of-the-art results --
even on a single GPU, in just 6-24 hours. Our study provides a first attempt at
applying neuro-evolution to the creation of 1D convolutional networks for
sentiment analysis including the optimisation of the embedding layer.
Virtual to Real Reinforcement Learning for Autonomous Driving
Authors: Xinlei Pan, Yurong You, Ziyan Wang, Cewu Lu
Date: 2017-04-13 00:00:00
Tasks: Autonomous Driving, Domain Adaptation, Image-to-Image Translation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Synthetic-to-Real Translation, Transfer Learning, Translation

Reinforcement learning is considered as a promising direction for driving
policy learning. However, training autonomous driving vehicle with
reinforcement learning in real environment involves non-affordable
trial-and-error. It is more desirable to first train in a virtual environment
and then transfer to the real environment. In this paper, we propose a novel
realistic translation network to make model trained in virtual environment be
workable in real world. The proposed network can convert non-realistic virtual
image input into a realistic one with similar scene structure. Given realistic
frames as input, driving policy trained by reinforcement learning can nicely
adapt to real world driving. Experiments show that our proposed virtual to real
(VR) reinforcement learning (RL) works pretty well. To our knowledge, this is
the first successful case of driving policy trained by reinforcement learning
that can adapt to real world driving data.
Deep Object-Centric Representations for Generalizable Robot Learning
Authors: Coline Devin, Pieter Abbeel, Trevor Darrell, Sergey Levine
Date: 2017-08-14 00:00:00
Tasks: Object, Reinforcement Learning, Reinforcement Learning (RL)

Robotic manipulation in complex open-world scenarios requires both reliable
physical manipulation skills and effective and generalizable perception. In
this paper, we propose a method where general purpose pretrained visual models
serve as an object-centric prior for the perception system of a learned policy.
We devise an object-level attentional mechanism that can be used to determine
relevant objects from a few trajectories or demonstrations, and then
immediately incorporate those objects into a learned policy. A task-independent
meta-attention locates possible objects in the scene, and a task-specific
attention identifies which objects are predictive of the trajectories. The
scope of the task-specific attention is easily adjusted by showing
demonstrations with distractor objects or with diverse relevant objects. Our
results indicate that this approach exhibits good generalization across object
instances using very few samples, and can be used to learn a variety of
manipulation tasks using reinforcement learning.
Automatic Error Analysis of Human Motor Performance for Interactive Coaching in Virtual Reality
Authors: Felix Hülsmann, Stefan Kopp, Mario Botsch
Date: 2017-09-26 00:00:00
Tasks: Dynamic Time Warping, feature selection, General Classification, Time Series Analysis, Time Series Classification

In the context of fitness coaching or for rehabilitation purposes, the motor
actions of a human participant must be observed and analyzed for errors in
order to provide effective feedback. This task is normally carried out by human
coaches, and it needs to be solved automatically in technical applications that
are to provide automatic coaching (e.g. training environments in VR). However,
most coaching systems only provide coarse information on movement quality, such
as a scalar value per body part that describes the overall deviation from the
correct movement. Further, they are often limited to static body postures or
rather simple movements of single body parts. While there are many approaches
to distinguish between different types of movements (e.g., between walking and
jumping), the detection of more subtle errors in a motor performance is less
investigated. We propose a novel approach to classify errors in sports or
rehabilitation exercises such that feedback can be delivered in a rapid and
detailed manner: Homogeneous sub-sequences of exercises are first temporally
aligned via Dynamic Time Warping. Next, we extract a feature vector from the
aligned sequences, which serves as a basis for feature selection using Random
Forests. The selected features are used as input for Support Vector Machines,
which finally classify the movement errors. We compare our algorithm to a well
established state-of-the-art approach in time series classification, 1-Nearest
Neighbor combined with Dynamic Time Warping, and show our algorithm's
superiority regarding classification quality as well as computational cost.
Output Range Analysis for Deep Neural Networks
Authors: Souradeep Dutta, Susmit Jha, Sriram Sanakaranarayanan, Ashish Tiwari
Date: 2017-09-26 00:00:00
Tasks: General Classification, image-classification, Image Classification

Deep neural networks (NN) are extensively used for machine learning tasks
such as image classification, perception and control of autonomous systems.
Increasingly, these deep NNs are also been deployed in high-assurance
applications. Thus, there is a pressing need for developing techniques to
verify neural networks to check whether certain user-expected properties are
satisfied. In this paper, we study a specific verification problem of computing
a guaranteed range for the output of a deep neural network given a set of
inputs represented as a convex polyhedron. Range estimation is a key primitive
for verifying deep NNs. We present an efficient range estimation algorithm that
uses a combination of local search and linear programming problems to
efficiently find the maximum and minimum values taken by the outputs of the NN
over the given input set. In contrast to recently proposed "monolithic"
optimization approaches, we use local gradient descent to repeatedly find and
eliminate local minima of the function. The final global optimum is certified
using a mixed integer programming instance. We implement our approach and
compare it with Reluplex, a recently proposed solver for deep neural networks.
We demonstrate the effectiveness of the proposed approach for verification of
NNs used in automated control as well as those used in classification.
Optimal deep neural networks for sparse recovery via Laplace techniques
Authors: Steffen Limmer, Slawomir Stanczak
Date: 2017-09-04 00:00:00

This paper introduces Laplace techniques for designing a neural network, with
the goal of estimating simplex-constraint sparse vectors from compressed
measurements. To this end, we recast the problem of MMSE estimation (w.r.t. a
pre-defined uniform input distribution) as the problem of computing the
centroid of some polytope that results from the intersection of the simplex and
an affine subspace determined by the measurements. Owing to the specific
structure, it is shown that the centroid can be computed analytically by
extending a recent result that facilitates the volume computation of polytopes
via Laplace transformations. A main insight of this paper is that the desired
volume and centroid computations can be performed by a classical deep neural
network comprising threshold functions, rectified linear (ReLU) and rectified
polynomial (ReP) activation functions. The proposed construction of a deep
neural network for sparse recovery is completely analytic so that
time-consuming training procedures are not necessary. Furthermore, we show that
the number of layers in our construction is equal to the number of measurements
which might enable novel low-latency sparse recovery algorithms for a larger
class of signals than that assumed in this paper. To assess the applicability
of the proposed uniform input distribution, we showcase the recovery
performance on samples that are soft-classification vectors generated by two
standard datasets. As both volume and centroid computation are known to be
computationally hard, the network width grows exponentially in the worst-case.
It can be, however, decreased by inducing sparse connectivity in the neural
network via a well-suited basis of the affine subspace. Finally, the presented
analytical construction may serve as a viable initialization to be further
optimized and trained using particular input datasets at hand.
Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledge
Authors: Ryota Hinami, Tao Mei, Shin'ichi Satoh
Date: 2017-09-26 00:00:00
Proceeding: ICCV 2017 10
Tasks: Anomaly Detection, Event Detection, Open-Ended Question Answering

This paper addresses the problem of joint detection and recounting of
abnormal events in videos. Recounting of abnormal events, i.e., explaining why
they are judged to be abnormal, is an unexplored but critical task in video
surveillance, because it helps human observers quickly judge if they are false
alarms or not. To describe the events in the human-understandable form for
event recounting, learning generic knowledge about visual concepts (e.g.,
object and action) is crucial. Although convolutional neural networks (CNNs)
have achieved promising results in learning such concepts, it remains an open
question as to how to effectively use CNNs for abnormal event detection, mainly
due to the environment-dependent nature of the anomaly detection. In this
paper, we tackle this problem by integrating a generic CNN model and
environment-dependent anomaly detectors. Our approach first learns CNN with
multiple visual tasks to exploit semantic information that is useful for
detecting and recounting abnormal events. By appropriately plugging the model
into anomaly detectors, we can detect and recount abnormal events while taking
advantage of the discriminative power of CNNs. Our approach outperforms the
state-of-the-art on Avenue and UCSD Ped2 benchmarks for abnormal event
detection and also produces promising results of abnormal event recounting.
Integration of Japanese Papers Into the DBLP Data Set
Authors: Paul Christian Sommerhoff
Date: 2017-09-26 00:00:00

If someone is looking for a certain publication in the field of computer
science, the searching person is likely to use the DBLP to find the desired
publication. The DBLP data set is continuously extended with new publications,
or rather their metadata, for example the names of involved authors, the title
and the publication date. While the size of the data set is already remarkable,
specific areas can still be improved. The DBLP offers a huge collection of
English papers because most papers concerning computer science are published in
English. Nevertheless, there are official publications in other languages which
are supposed to be added to the data set. One kind of these are Japanese
papers. This diploma thesis will show a way to automatically process
publication lists of Japanese papers and to make them ready for an import into
the DBLP data set. Especially important are the problems along the way of
processing, such as transcription handling and Personal Name Matching with
Japanese names.
Tensors Come of Age: Why the AI Revolution will help HPC
Authors: John L. Gustafson, Lenore M. Mullin
Date: 2017-09-26 00:00:00

This article discusses how the automation of tensor algorithms, based on A
Mathematics of Arrays and Psi Calculus, and a new way to represent numbers,
Unum Arithmetic, enables mechanically provable, scalable, portable, and more
numerically accurate software.
Region-Based Image Retrieval Revisited
Authors: Ryota Hinami, Yusuke Matsui, Shin'ichi Satoh
Date: 2017-09-26 00:00:00
Tasks: Attribute, Image Retrieval, Object, Re-Ranking, Retrieval

Region-based image retrieval (RBIR) technique is revisited. In early attempts
at RBIR in the late 90s, researchers found many ways to specify region-based
queries and spatial relationships; however, the way to characterize the
regions, such as by using color histograms, were very poor at that time. Here,
we revisit RBIR by incorporating semantic specification of objects and
intuitive specification of spatial relationships. Our contributions are the
following. First, to support multiple aspects of semantic object specification
(category, instance, and attribute), we propose a multitask CNN feature that
allows us to use deep learning technique and to jointly handle multi-aspect
object specification. Second, to help users specify spatial relationships among
objects in an intuitive way, we propose recommendation techniques of spatial
relationships. In particular, by mining the search results, a system can
recommend feasible spatial relationships among the objects. The system also can
recommend likely spatial relationships by assigned object category names based
on language prior. Moreover, object-level inverted indexing supports very fast
shortlist generation, and re-ranking based on spatial constraints provides
users with instant RBIR experiences.
Adaptive Nonparametric Clustering
Authors: Kirill Efimov, Larisa Adamyan, Vladimir Spokoiny
Date: 2017-09-26 00:00:00
Tasks: Clustering, Nonparametric Clustering

This paper presents a new approach to non-parametric cluster analysis called
Adaptive Weights Clustering (AWC). The idea is to identify the clustering
structure by checking at different points and for different scales on departure
from local homogeneity. The proposed procedure describes the clustering
structure in terms of weights \( w_{ij} \) each of them measures the degree of
local inhomogeneity for two neighbor local clusters using statistical tests of
"no gap" between them. % The procedure starts from very local scale, then the
parameter of locality grows by some factor at each step. The method is fully
adaptive and does not require to specify the number of clusters or their
structure. The clustering results are not sensitive to noise and outliers, the
procedure is able to recover different clusters with sharp edges or manifold
structure. The method is scalable and computationally feasible. An intensive
numerical study shows a state-of-the-art performance of the method in various
artificial examples and applications to text data. Our theoretical study states
optimal sensitivity of AWC to local inhomogeneity.
LabelFusion: A Pipeline for Generating Ground Truth Labels for Real RGBD Data of Cluttered Scenes
Authors: Pat Marion, Peter R. Florence, Lucas Manuelli, Russ Tedrake
Date: 2017-07-15 00:00:00
Tasks: 3D Reconstruction, Object, Pose Estimation, Semantic Segmentation

Deep neural network (DNN) architectures have been shown to outperform
traditional pipelines for object segmentation and pose estimation using RGBD
data, but the performance of these DNN pipelines is directly tied to how
representative the training data is of the true data. Hence a key requirement
for employing these methods in practice is to have a large set of labeled data
for your specific robotic manipulation task, a requirement that is not
generally satisfied by existing datasets. In this paper we develop a pipeline
to rapidly generate high quality RGBD data with pixelwise labels and object
poses. We use an RGBD camera to collect video of a scene from multiple
viewpoints and leverage existing reconstruction techniques to produce a 3D
dense reconstruction. We label the 3D reconstruction using a human assisted
ICP-fitting of object meshes. By reprojecting the results of labeling the 3D
scene we can produce labels for each RGBD image of the scene. This pipeline
enabled us to collect over 1,000,000 labeled object instances in just a few
days. We use this dataset to answer questions related to how much training data
is required, and of what quality the data must be, to achieve high performance
from a DNN architecture.
Scale Adaptive Clustering of Multiple Structures
Authors: Xiang Yang, Peter Meer
Date: 2017-09-26 00:00:00
Tasks: Clustering

We propose the segmentation of noisy datasets into Multiple Inlier Structures
with a new Robust Estimator (MISRE). The scale of each individual structure is
estimated adaptively from the input data and refined by mean shift, without
tuning any parameter in the process, or manually specifying thresholds for
different estimation problems. Once all the data points were classified into
separate structures, these structures are sorted by their densities with the
strongest inlier structures coming out first. Several 2D and 3D synthetic and
real examples are presented to illustrate the efficiency, robustness and the
limitations of the MISRE algorithm.
Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis
Authors: Xiang Zhang, Lina Yao, Dalin Zhang, Xianzhi Wang, Quan Z. Sheng, Tao Gu
Date: 2017-09-26 00:00:00
Tasks: Activity Recognition, EEG, Electroencephalogram (EEG), Feature Engineering

An electroencephalography (EEG) based brain activity recognition is a
fundamental field of study for a number of significant applications such as
intention prediction, appliance control, and neurological disease diagnosis in
smart home and smart healthcare domains. Existing techniques mostly focus on
binary brain activity recognition for a single person, which limits their
deployment in wider and complex practical scenarios. Therefore, multi-person
and multi-class brain activity recognition has obtained popularity recently.
Another challenge faced by brain activity recognition is the low recognition
accuracy due to the massive noises and the low signal-to-noise ratio in EEG
signals. Moreover, the feature engineering in EEG processing is time-consuming
and highly re- lies on the expert experience. In this paper, we attempt to
solve the above challenges by proposing an approach which has better EEG
interpretation ability via raw Electroencephalography (EEG) signal analysis for
multi-person and multi-class brain activity recognition. Specifically, we
analyze inter-class and inter-person EEG signal characteristics, based on which
to capture the discrepancy of inter-class EEG data. Then, we adopt an
Autoencoder layer to automatically refine the raw EEG signals by eliminating
various artifacts. We evaluate our approach on both a public and a local EEG
datasets and conduct extensive experiments to explore the effect of several
factors (such as normalization methods, training data size, and Autoencoder
hidden neuron size) on the recognition results. The experimental results show
that our approach achieves a high accuracy comparing to competitive
state-of-the-art methods, indicating its potential in promoting future research
on multi-person EEG recognition.