pwc-archive/papers-with-abstracts huggingface dataset:
Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation
Authors: Matthias Müller, Vincent Casser, Neil Smith, Dominik L. Michels, Bernard Ghanem
Date: 2017-08-19 00:00:00
Tasks: Data Augmentation, Imitation Learning, regression

Automating the navigation of unmanned aerial vehicles (UAVs) in diverse
scenarios has gained much attention in recent years. However, teaching UAVs to
fly in challenging environments remains an unsolved problem, mainly due to the
lack of training data. In this paper, we train a deep neural network to predict
UAV controls from raw image data for the task of autonomous UAV racing in a
photo-realistic simulation. Training is done through imitation learning with
data augmentation to allow for the correction of navigation mistakes. Extensive
experiments demonstrate that our trained network (when sufficient data
augmentation is used) outperforms state-of-the-art methods and flies more
consistently than many human pilots. Additionally, we show that our optimized
network architecture can run in real-time on embedded hardware, allowing for
efficient on-board processing critical for real-world deployment. From a
broader perspective, our results underline the importance of extensive data
augmentation techniques to improve robustness in end-to-end learning setups.
A Deformable Interface for Human Touch Recognition using Stretchable Carbon Nanotube Dielectric Elastomer Sensors and Deep Neural Networks
Authors: Chris Larson, Josef Spjut, Ross Knepper, Robert Shepherd
Date: 2017-06-08 00:00:00

User interfaces provide an interactive window between physical and virtual
environments. A new concept in the field of human-computer interaction is a
soft user interface; a compliant surface that facilitates touch interaction
through deformation. Despite the potential of these interfaces, they currently
lack a signal processing framework that can efficiently extract information
from their deformation. Here we present OrbTouch, a device that uses
statistical learning algorithms, based on convolutional neural networks, to map
deformations from human touch to categorical labels (i.e., gestures) and touch
location using stretchable capacitor signals as inputs. We demonstrate this
approach by using the device to control the popular game Tetris. OrbTouch
provides a modular, robust framework to interpret deformation in soft media,
laying a foundation for new modes of human computer interaction through shape
changing solids.
PlaneMatch: Patch Coplanarity Prediction for Robust RGB-D Reconstruction
Authors: Yifei Shi, Kai Xu, Matthias Niessner, Szymon Rusinkiewicz, Thomas Funkhouser
Date: 2018-03-22 00:00:00
Proceeding: ECCV 2018 9
Tasks: Prediction, RGB-D Reconstruction

We introduce a novel RGB-D patch descriptor designed for detecting coplanar
surfaces in SLAM reconstruction. The core of our method is a deep convolutional
neural net that takes in RGB, depth, and normal information of a planar patch
in an image and outputs a descriptor that can be used to find coplanar patches
from other images.We train the network on 10 million triplets of coplanar and
non-coplanar patches, and evaluate on a new coplanarity benchmark created from
commodity RGB-D scans. Experiments show that our learned descriptor outperforms
alternatives extended for this new task by a significant margin. In addition,
we demonstrate the benefits of coplanarity matching in a robust RGBD
reconstruction formulation.We find that coplanarity constraints detected with
our method are sufficient to get reconstruction results comparable to
state-of-the-art frameworks on most scenes, but outperform other methods on
standard benchmarks when combined with a simple keypoint method.
Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications
Authors: Matthias Müller, Vincent Casser, Jean Lahoud, Neil Smith, Bernard Ghanem
Date: 2017-08-19 00:00:00
Tasks: Autonomous Driving

We present a photo-realistic training and evaluation simulator (Sim4CV) with
extensive applications across various fields of computer vision. Built on top
of the Unreal Engine, the simulator integrates full featured physics based
cars, unmanned aerial vehicles (UAVs), and animated human actors in diverse
urban and suburban 3D environments. We demonstrate the versatility of the
simulator with two case studies: autonomous UAV-based tracking of moving
objects and autonomous driving using supervised learning. The simulator fully
integrates both several state-of-the-art tracking algorithms with a benchmark
evaluation tool and a deep neural network (DNN) architecture for training
vehicles to drive autonomously. It generates synthetic photo-realistic datasets
with automatic ground truth annotations to easily extend existing real-world
datasets and provides extensive synthetic data variety through its ability to
reconfigure synthetic worlds on the fly using an automatic world generation
tool. The supplementary video can be viewed a https://youtu.be/SqAxzsQ7qUU
Gradient descent in Gaussian random fields as a toy model for high-dimensional optimisation in deep learning
Authors: Mariano Chouza, Stephen Roberts, Stefan Zohren
Date: 2018-03-24 00:00:00

In this paper we model the loss function of high-dimensional optimization
problems by a Gaussian random field, or equivalently a Gaussian process. Our
aim is to study gradient descent in such loss functions or energy landscapes
and compare it to results obtained from real high-dimensional optimization
problems such as encountered in deep learning. In particular, we analyze the
distribution of the improved loss function after a step of gradient descent,
provide analytic expressions for the moments as well as prove asymptotic
normality as the dimension of the parameter space becomes large. Moreover, we
compare this with the expectation of the global minimum of the landscape
obtained by means of the Euler characteristic of excursion sets. Besides
complementing our analytical findings with numerical results from simulated
Gaussian random fields, we also compare it to loss functions obtained from
optimisation problems on synthetic and real data sets by proposing a "black
box" random field toy-model for a deep neural network loss function.
Optimization Beyond the Convolution: Generalizing Spatial Relations with End-to-End Metric Learning
Authors: Philipp Jund, Andreas Eitel, Nichola Abdo, Wolfram Burgard
Date: 2017-07-04 00:00:00
Tasks: Metric Learning

To operate intelligently in domestic environments, robots require the ability to understand arbitrary spatial relations between objects and to generalize them to objects of varying sizes and shapes. In this work, we present a novel end-to-end approach to generalize spatial relations based on distance metric learning. We train a neural network to transform 3D point clouds of objects to a metric space that captures the similarity of the depicted spatial relations, using only geometric models of the objects. Our approach employs gradient-based optimization to compute object poses in order to imitate an arbitrary target relation by reducing the distance to it under the learned metric. Our results based on simulated and real-world experiments show that the proposed method enables robots to generalize spatial relations to unknown objects over a continuous spectrum.
Augmented Artificial Intelligence: a Conceptual Framework
Authors: Alexander N. Gorban, Bogdan Grechuk, Ivan Y. Tyukin
Date: 2018-02-06 00:00:00

All artificial Intelligence (AI) systems make errors. These errors are
unexpected, and differ often from the typical human mistakes ("non-human"
errors). The AI errors should be corrected without damage of existing skills
and, hopefully, avoiding direct human expertise. This paper presents an initial
summary report of project taking new and systematic approach to improving the
intellectual effectiveness of the individual AI by communities of AIs. We
combine some ideas of learning in heterogeneous multiagent systems with new and
original mathematical approaches for non-iterative corrections of errors of
legacy AI systems. The mathematical foundations of AI non-destructive
correction are presented and a series of new stochastic separation theorems is
proven. These theorems provide a new instrument for the development, analysis,
and assessment of machine learning methods and algorithms in high dimension.
They demonstrate that in high dimensions and even for exponentially large
samples, linear classifiers in their classical Fisher's form are powerful
enough to separate errors from correct responses with high probability and to
provide efficient solution to the non-destructive corrector problem. In
particular, we prove some hypotheses formulated in our paper `Stochastic
Separation Theorems' (Neural Networks, 94, 255--259, 2017), and answer one
general problem published by Donoho and Tanner in 2009.
Similar Elements and Metric Labeling on Complete Graphs
Authors: Pedro F. Felzenszwalb
Date: 2018-03-21 00:00:00
Tasks: BIG-bench Machine Learning

We consider a problem that involves finding similar elements in a collection
of sets. The problem is motivated by applications in machine learning and
pattern recognition. We formulate the similar elements problem as an
optimization and give an efficient approximation algorithm that finds a
solution within a factor of 2 of the optimal. The similar elements problem is a
special case of the metric labeling problem and we also give an efficient
2-approximation algorithm for the metric labeling problem on complete graphs.
Machine Learning and Applied Linguistics
Authors: Sowmya Vajjala
Date: 2018-03-24 00:00:00
Tasks: BIG-bench Machine Learning

This entry introduces the topic of machine learning and provides an overview
of its relevance for applied linguistics and language learning. The discussion
will focus on giving an introduction to the methods and applications of machine
learning in applied linguistics, and will provide references for further study.
A Resourceful Reframing of Behavior Trees
Authors: Chris Martens, Eric Butler, Joseph C. Osborn
Date: 2018-03-24 00:00:00
Tasks: Vocal Bursts Type Prediction

Designers of autonomous agents, whether in physical or virtual environments,
need to express nondeterminisim, failure, and parallelism in behaviors, as well
as accounting for synchronous coordination between agents. Behavior Trees are a
semi-formalism deployed widely for this purpose in the games industry, but with
challenges to scalability, reasoning, and reuse of common sub-behaviors.
  We present an alternative formulation of behavior trees through a language
design perspective, giving a formal operational semantics, type system, and
corresponding implementation. We express specifications for atomic behaviors as
linear logic formulas describing how they transform the environment, and our
type system uses linear sequent calculus to derive a compositional type
assignment to behavior tree expressions. These types expose the conditions
required for behaviors to succeed and allow abstraction over parameters to
behaviors, enabling the development of behavior "building blocks" amenable to
compositional reasoning and reuse.
Online Product Quantization
Authors: Donna Xu, Ivor W. Tsang, Ying Zhang
Date: 2017-11-29 00:00:00
Tasks: Quantization

Approximate nearest neighbor (ANN) search has achieved great success in many
tasks. However, existing popular methods for ANN search, such as hashing and
quantization methods, are designed for static databases only. They cannot
handle well the database with data distribution evolving dynamically, due to
the high computational effort for retraining the model based on the new
database. In this paper, we address the problem by developing an online product
quantization (online PQ) model and incrementally updating the quantization
codebook that accommodates to the incoming streaming data. Moreover, to further
alleviate the issue of large scale computation for the online PQ update, we
design two budget constraints for the model to update partial PQ codebook
instead of all. We derive a loss bound which guarantees the performance of our
online PQ model. Furthermore, we develop an online PQ model over a sliding
window with both data insertion and deletion supported, to reflect the
real-time behaviour of the data. The experiments demonstrate that our online PQ
model is both time-efficient and effective for ANN search in dynamic large
scale databases compared with baseline methods and the idea of partial PQ
codebook update further reduces the update cost.
Comparing Generative Adversarial Network Techniques for Image Creation and Modification
Authors: Mathijs Pieters, Marco Wiering
Date: 2018-03-24 00:00:00
Tasks: Generative Adversarial Network

Generative adversarial networks (GANs) have demonstrated to be successful at
generating realistic real-world images. In this paper we compare various GAN
techniques, both supervised and unsupervised. The effects on training stability
of different objective functions are compared. We add an encoder to the
network, making it possible to encode images to the latent space of the GAN.
The generator, discriminator and encoder are parameterized by deep
convolutional neural networks. For the discriminator network we experimented
with using the novel Capsule Network, a state-of-the-art technique for
detecting global features in images. Experiments are performed using a digit
and face dataset, with various visualizations illustrating the results. The
results show that using the encoder network it is possible to reconstruct
images. With the conditional GAN we can alter visual attributes of generated or
encoded images. The experiments with the Capsule Network as discriminator
result in generated images of a lower quality, compared to a standard
convolutional neural network.
Adversarial Framework for Unsupervised Learning of Motion Dynamics in Videos
Authors: C. Spampinato, S. Palazzo, P. D'Oro, D. Giordano, M. Shah
Date: 2018-03-24 00:00:00
Tasks: Motion Estimation, Prediction, Semantic Segmentation, Temporal Action Localization, Unsupervised Video Object Segmentation, Video Generation, Video Object Segmentation, Video Semantic Segmentation

Human behavior understanding in videos is a complex, still unsolved problem and requires to accurately model motion at both the local (pixel-wise dense prediction) and global (aggregation of motion cues) levels. Current approaches based on supervised learning require large amounts of annotated data, whose scarce availability is one of the main limiting factors to the development of general solutions. Unsupervised learning can instead leverage the vast amount of videos available on the web and it is a promising solution for overcoming the existing limitations. In this paper, we propose an adversarial GAN-based framework that learns video representations and dynamics through a self-supervision mechanism in order to perform dense and global prediction in videos. Our approach synthesizes videos by 1) factorizing the process into the generation of static visual content and motion, 2) learning a suitable representation of a motion latent space in order to enforce spatio-temporal coherency of object trajectories, and 3) incorporating motion estimation and pixel-wise dense prediction into the training procedure. Self-supervision is enforced by using motion masks produced by the generator, as a co-product of its generation process, to supervise the discriminator network in performing dense prediction. Performance evaluation, carried out on standard benchmarks, shows that our approach is able to learn, in an unsupervised way, both local and global video dynamics. The learned representations, then, support the training of video object segmentation methods with sensibly less (about 50%) annotations, giving performance comparable to the state of the art. Furthermore, the proposed method achieves promising performance in generating realistic videos, outperforming state-of-the-art approaches especially on motion-related metrics.
Simple Large-scale Relation Extraction from Unstructured Text
Authors: Christos Christodoulopoulos, Arpit Mittal
Date: 2018-03-24 00:00:00
Proceeding: LREC 2018 5
Tasks: Question Answering, Relation, Relation Extraction

Knowledge-based question answering relies on the availability of facts, the
majority of which cannot be found in structured sources (e.g. Wikipedia
info-boxes, Wikidata). One of the major components of extracting facts from
unstructured text is Relation Extraction (RE). In this paper we propose a novel
method for creating distant (weak) supervision labels for training a
large-scale RE system. We also provide new evidence about the effectiveness of
neural network approaches by decoupling the model architecture from the feature
design of a state-of-the-art neural network system. Surprisingly, a much
simpler classifier trained on similar features performs on par with the highly
complex neural network system (at 75x reduction to the training time),
suggesting that the features are a bigger contributor to the final performance.
A Proximal Block Coordinate Descent Algorithm for Deep Neural Network Training
Authors: Tim Tsz-Kit Lau, Jinshan Zeng, Baoyuan Wu, Yuan Yao
Date: 2018-03-24 00:00:00

Training deep neural networks (DNNs) efficiently is a challenge due to the
associated highly nonconvex optimization. The backpropagation (backprop)
algorithm has long been the most widely used algorithm for gradient computation
of parameters of DNNs and is used along with gradient descent-type algorithms
for this optimization task. Recent work have shown the efficiency of block
coordinate descent (BCD) type methods empirically for training DNNs. In view of
this, we propose a novel algorithm based on the BCD method for training DNNs
and provide its global convergence results built upon the powerful framework of
the Kurdyka-Lojasiewicz (KL) property. Numerical experiments on standard
datasets demonstrate its competitive efficiency against standard optimizers
with backprop.
AAANE: Attention-based Adversarial Autoencoder for Multi-scale Network Embedding
Authors: Lei Sang, Min Xu, Shengsheng Qian, Xindong Wu
Date: 2018-03-24 00:00:00
Tasks: Network Embedding

Network embedding represents nodes in a continuous vector space and preserves
structure information from the Network. Existing methods usually adopt a
"one-size-fits-all" approach when concerning multi-scale structure information,
such as first- and second-order proximity of nodes, ignoring the fact that
different scales play different roles in the embedding learning. In this paper,
we propose an Attention-based Adversarial Autoencoder Network Embedding(AAANE)
framework, which promotes the collaboration of different scales and lets them
vote for robust representations. The proposed AAANE consists of two components:
1) Attention-based autoencoder effectively capture the highly non-linear
network structure, which can de-emphasize irrelevant scales during training. 2)
An adversarial regularization guides the autoencoder learn robust
representations by matching the posterior distribution of the latent embeddings
to given prior distribution. This is the first attempt to introduce attention
mechanisms to multi-scale network embedding. Experimental results on real-world
networks show that our learned attention parameters are different for every
network and the proposed approach outperforms existing state-of-the-art
approaches for network embedding.
Predicting Hurricane Trajectories using a Recurrent Neural Network
Authors: Sheila Alemany, Jonathan Beltran, Adrian Perez, Sam Ganzfried
Date: 2018-02-01 00:00:00

Hurricanes are cyclones circulating about a defined center whose closed wind
speeds exceed 75 mph originating over tropical and subtropical waters. At
landfall, hurricanes can result in severe disasters. The accuracy of predicting
their trajectory paths is critical to reduce economic loss and save human
lives. Given the complexity and nonlinearity of weather data, a recurrent
neural network (RNN) could be beneficial in modeling hurricane behavior. We
propose the application of a fully connected RNN to predict the trajectory of
hurricanes. We employed the RNN over a fine grid to reduce typical truncation
errors. We utilized their latitude, longitude, wind speed, and pressure
publicly provided by the National Hurricane Center (NHC) to predict the
trajectory of a hurricane at 6-hour intervals. Results show that this proposed
technique is competitive to methods currently employed by the NHC and can
predict up to approximately 120 hours of hurricane path.
MirBot: A collaborative object recognition system for smartphones using convolutional neural networks
Authors: Antonio Pertusa, Antonio-Javier Gallego, Marisa Bernabeu
Date: 2017-06-09 00:00:00
Tasks: image-classification, Image Classification, Object, Object Recognition, Transfer Learning

MirBot is a collaborative application for smartphones that allows users to
perform object recognition. This app can be used to take a photograph of an
object, select the region of interest and obtain the most likely class (dog,
chair, etc.) by means of similarity search using features extracted from a
convolutional neural network (CNN). The answers provided by the system can be
validated by the user so as to improve the results for future queries. All the
images are stored together with a series of metadata, thus enabling a
multimodal incremental dataset labeled with synset identifiers from the WordNet
ontology. This dataset grows continuously thanks to the users' feedback, and is
publicly available for research. This work details the MirBot object
recognition system, analyzes the statistics gathered after more than four years
of usage, describes the image classification methodology, and performs an
exhaustive evaluation using handcrafted features, convolutional neural codes
and different transfer learning techniques. After comparing various models and
transformation methods, the results show that the CNN features maintain the
accuracy of MirBot constant over time, despite the increasing number of new
classes. The app is freely available at the Apple and Google Play stores.
Multi-range Reasoning for Machine Comprehension
Authors: Yi Tay, Luu Anh Tuan, Siu Cheung Hui
Date: 2018-03-24 00:00:00
Tasks: Reading Comprehension

We propose MRU (Multi-Range Reasoning Units), a new fast compositional
encoder for machine comprehension (MC). Our proposed MRU encoders are
characterized by multi-ranged gating, executing a series of parameterized
contract-and-expand layers for learning gating vectors that benefit from long
and short-term dependencies. The aims of our approach are as follows: (1)
learning representations that are concurrently aware of long and short-term
context, (2) modeling relationships between intra-document blocks and (3) fast
and efficient sequence encoding. We show that our proposed encoder demonstrates
promising results both as a standalone encoder and as well as a complementary
building block. We conduct extensive experiments on three challenging MC
datasets, namely RACE, SearchQA and NarrativeQA, achieving highly competitive
performance on all. On the RACE benchmark, our model outperforms DFN (Dynamic
Fusion Networks) by 1.5%-6% without using any recurrent or convolution layers.
Similarly, we achieve competitive performance relative to AMANDA on the
SearchQA benchmark and BiDAF on the NarrativeQA benchmark without using any
LSTM/GRU layers. Finally, incorporating MRU encoders with standard BiLSTM
architectures further improves performance, achieving state-of-the-art results.