pwc-archive/papers-with-abstracts huggingface dataset:
Toward Autonomous Rotation-Aware Unmanned Aerial Grasping
Authors: Shi-Jie Lin, Jinwang Wang, Wen Yang, GuiSong Xia
Date: 2018-11-09 00:00:00
Tasks: State Estimation

Autonomous Unmanned Aerial Manipulators (UAMs) have shown promising
potentials to transform passive sensing missions into active 3-dimension
interactive missions, but they still suffer from some difficulties impeding
their wide applications, such as target detection and stabilization. This
letter presents a vision-based autonomous UAM with a 3DoF robotic arm for
rotational grasping, with a compensation on displacement for center of gravity.
First, the hardware, software architecture and state estimation methods are
detailed. All the mechanical designs are fully provided as open-source hardware
for the reuse by the community. Then, we analyze the flow distribution
generated by rotors and plan the robotic arm's motion based on this analysis.
Next, a novel detection approach called Rotation-SqueezeDet is proposed to
enable rotation-aware grasping, which can give the target position and rotation
angle in near real-time on Jetson TX2. Finally, the effectiveness of the
proposed scheme is validated in multiple experimental trials, highlighting it's
applicability of autonomous aerial grasping in GPS-denied environments.
Evidence Transfer for Improving Clustering Tasks Using External Categorical Evidence
Authors: Athanasios Davvetas, Iraklis A. Klampanos, Vangelis Karkaletsis
Date: 2018-11-09 00:00:00
Tasks: Clustering, Representation Learning

In this paper we introduce evidence transfer for clustering, a deep learning
method that can incrementally manipulate the latent representations of an
autoencoder, according to external categorical evidence, in order to improve a
clustering outcome. By evidence transfer we define the process by which the
categorical outcome of an external, auxiliary task is exploited to improve a
primary task, in this case representation learning for clustering. Our proposed
method makes no assumptions regarding the categorical evidence presented, nor
the structure of the latent space. We compare our method, against the baseline
solution by performing k-means clustering before and after its deployment.
Experiments with three different kinds of evidence show that our method
effectively manipulates the latent representations when introduced with real
corresponding evidence, while remaining robust when presented with low quality
evidence.
ITE: A Lightweight Implementation of Stratified Reasoning for Constructive Logical Operators
Authors: Arnaud Gotlieb, Dusica Marijan, Helge Spieker
Date: 2018-11-09 00:00:00
Tasks: Negation

Constraint Programming (CP) is a powerful declarative programming paradigm where inference and search are interleaved to find feasible and optimal solutions to various type of constraint systems. However, handling logical connectors with constructive information in CP is notoriously difficult. This paper presents If Then Else (ITE), a lightweight implementation of stratified constructive reasoning for logical connectives. Stratification is introduced to cope with the risk of combinatorial explosion of constructing information from nested and combined logical operators. ITE is an open-source library built on top of SICStus Prolog clp(fd), which proposes various operators, including constructive disjunction and negation, constructive implication and conditional. These operators can be used to express global constraints and to benefit from constructive reasoning for more domain pruning during constraint filtering. Even though ITE is not competitive with specialized filtering algorithms available in some global constraints implementations, its expressiveness allows users to easily define well-tuned constraints with powerful deduction capabilities. Our extended experimental results show that ITE is more efficient than available generic approaches that handle logical constraint systems over finite domains.
Deep Ensemble Bayesian Active Learning : Addressing the Mode Collapse issue in Monte Carlo dropout via Ensembles
Authors: Remus Pop, Patric Fulop
Date: 2018-11-09 00:00:00
Tasks: Active Learning, General Classification, image-classification, Image Classification

In image classification tasks, the ability of deep CNNs to deal with complex
image data has proven to be unrivalled. However, they require large amounts of
labeled training data to reach their full potential. In specialised domains
such as healthcare, labeled data can be difficult and expensive to obtain.
Active Learning aims to alleviate this problem, by reducing the amount of
labelled data needed for a specific task while delivering satisfactory
performance. We propose DEBAL, a new active learning strategy designed for deep
neural networks. This method improves upon the current state-of-the-art deep
Bayesian active learning method, which suffers from the mode collapse problem.
We correct for this deficiency by making use of the expressive power and
statistical properties of model ensembles. Our proposed method manages to
capture superior data uncertainty, which translates into improved
classification performance. We demonstrate empirically that our ensemble method
yields faster convergence of CNNs trained on the MNIST and CIFAR-10 datasets.
Performance Guarantees for Homomorphisms Beyond Markov Decision Processes
Authors: Sultan Javed Majeed, Marcus Hutter
Date: 2018-11-09 00:00:00

Most real-world problems have huge state and/or action spaces. Therefore, a
naive application of existing tabular solution methods is not tractable on such
problems. Nonetheless, these solution methods are quite useful if an agent has
access to a relatively small state-action space homomorphism of the true
environment and near-optimal performance is guaranteed by the map. A plethora
of research is focused on the case when the homomorphism is a Markovian
representation of the underlying process. However, we show that near-optimal
performance is sometimes guaranteed even if the homomorphism is non-Markovian.
Moreover, we can aggregate significantly more states by lifting the Markovian
requirement without compromising on performance. In this work, we expand
Extreme State Aggregation (ESA) framework to joint state-action aggregations.
We also lift the policy uniformity condition for aggregation in ESA that allows
even coarser modeling of the true environment.
Exploiting Capacity of Sewer System Using Unsupervised Learning Algorithms Combined with Dimensionality Reduction
Authors: Duo Zhang, Geir Lindholm, Nicolas Martinez, Harsha Ratnaweera
Date: 2018-11-09 00:00:00
Tasks: Clustering, Dimensionality Reduction

Exploiting capacity of sewer system using decentralized control is a cost
effective mean of minimizing the overflow. Given the size of the real sewer
system, exploiting all the installed control structures in the sewer pipes can
be challenging. This paper presents a divide and conquer solution to implement
decentralized control measures based on unsupervised learning algorithms. A
sewer system is first divided into a number of subcatchments. A series of
natural and built factors that have the impact on sewer system performance is
then collected. Clustering algorithms are then applied to grouping
subcatchments with similar hydraulic hydrologic characteristics. Following
which, principal component analysis is performed to interpret the main features
of sub-catchment groups and identify priority control locations. Overflows
under different control scenarios are compared based on the hydraulic model.
Simulation results indicate that priority control applied to the most suitable
cluster could bring the most profitable result.
Cross and Learn: Cross-Modal Self-Supervision
Authors: Nawid Sayed, Biagio Brattoli, Björn Ommer
Date: 2018-11-09 00:00:00
Tasks: Action Recognition, Optical Flow Estimation, Representation Learning, Self-Supervised Learning, Temporal Action Localization

In this paper we present a self-supervised method for representation learning
utilizing two different modalities. Based on the observation that cross-modal
information has a high semantic meaning we propose a method to effectively
exploit this signal. For our approach we utilize video data since it is
available on a large scale and provides easily accessible modalities given by
RGB and optical flow. We demonstrate state-of-the-art performance on highly
contested action recognition datasets in the context of self-supervised
learning. We show that our feature representation also transfers to other tasks
and conduct extensive ablation studies to validate our core contributions. Code
and model can be found at https://github.com/nawidsayed/Cross-and-Learn.
Multimodal One-Shot Learning of Speech and Images
Authors: Ryan Eloff, Herman A. Engelbrecht, Herman Kamper
Date: 2018-11-09 00:00:00
Tasks: Dynamic Time Warping, One-Shot Learning

Imagine a robot is shown new concepts visually together with spoken tags,
e.g. "milk", "eggs", "butter". After seeing one paired audio-visual example per
class, it is shown a new set of unseen instances of these objects, and asked to
pick the "milk". Without receiving any hard labels, could it learn to match the
new continuous speech input to the correct visual instance? Although unimodal
one-shot learning has been studied, where one labelled example in a single
modality is given per class, this example motivates multimodal one-shot
learning. Our main contribution is to formally define this task, and to propose
several baseline and advanced models. We use a dataset of paired spoken and
visual digits to specifically investigate recent advances in Siamese
convolutional neural networks. Our best Siamese model achieves twice the
accuracy of a nearest neighbour model using pixel-distance over images and
dynamic time warping over speech in 11-way cross-modal matching.
Long Short-Term Memory with Dynamic Skip Connections
Authors: Tao Gui, Qi Zhang, Lujun Zhao, Yaosong Lin, Minlong Peng, Jingjing Gong, Xuanjing Huang
Date: 2018-11-09 00:00:00
Tasks: Named Entity Recognition (NER), Reinforcement Learning, Sentiment Analysis

In recent years, long short-term memory (LSTM) has been successfully used to
model sequential data of variable length. However, LSTM can still experience
difficulty in capturing long-term dependencies. In this work, we tried to
alleviate this problem by introducing a dynamic skip connection, which can
learn to directly connect two dependent words. Since there is no dependency
information in the training data, we propose a novel reinforcement
learning-based method to model the dependency relationship and connect
dependent words. The proposed model computes the recurrent transition functions
based on the skip connections, which provides a dynamic skipping advantage over
RNNs that always tackle entire sentences sequentially. Our experimental results
on three natural language processing tasks demonstrate that the proposed method
can achieve better performance than existing methods. In the number prediction
experiment, the proposed model outperformed LSTM with respect to accuracy by
nearly 20%.
Suggesting Cooking Recipes Through Simulation and Bayesian Optimization
Authors: Eduardo C. Garrido-Merchán, Alejandro Albarca-Molina
Date: 2018-11-09 00:00:00
Tasks: Bayesian Optimization

Cooking typically involves a plethora of decisions about ingredients and
tools that need to be chosen in order to write a good cooking recipe. Cooking
can be modelled in an optimization framework, as it involves a search space of
ingredients, kitchen tools, cooking times or temperatures. If we model as an
objective function the quality of the recipe, several problems arise. No
analytical expression can model all the recipes, so no gradients are available.
The objective function is subjective, in other words, it contains noise.
Moreover, evaluations are expensive both in time and human resources. Bayesian
Optimization (BO) emerges as an ideal methodology to tackle problems with these
characteristics. In this paper, we propose a methodology to suggest recipe
recommendations based on a Machine Learning (ML) model that fits real and
simulated data and BO. We provide empirical evidence with two experiments that
support the adequacy of the methodology.
Learning Semantic Representations for Novel Words: Leveraging Both Form and Context
Authors: Timo Schick, Hinrich Schütze
Date: 2018-11-09 00:00:00
Tasks: Form, Learning Semantic Representations, Word Embeddings

Word embeddings are a key component of high-performing natural language
processing (NLP) systems, but it remains a challenge to learn good
representations for novel words on the fly, i.e., for words that did not occur
in the training data. The general problem setting is that word embeddings are
induced on an unlabeled training corpus and then a model is trained that embeds
novel words into this induced embedding space. Currently, two approaches for
learning embeddings of novel words exist: (i) learning an embedding from the
novel word's surface-form (e.g., subword n-grams) and (ii) learning an
embedding from the context in which it occurs. In this paper, we propose an
architecture that leverages both sources of information - surface-form and
context - and show that it results in large increases in embedding quality. Our
architecture obtains state-of-the-art results on the Definitional Nonce and
Contextual Rare Words datasets. As input, we only require an embedding set and
an unlabeled corpus for training our architecture to produce embeddings
appropriate for the induced embedding space. Thus, our model can easily be
integrated into any existing NLP system and enhance its capability to handle
novel words.
Multimodal Grounding for Sequence-to-Sequence Speech Recognition
Authors: Ozan Caglayan, Ramon Sanabria, Shruti Palaskar, Loïc Barrault, Florian Metze
Date: 2018-11-09 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Sequence-To-Sequence Speech Recognition, speech-recognition, Speech Recognition

Humans are capable of processing speech by making use of multiple sensory
modalities. For example, the environment where a conversation takes place
generally provides semantic and/or acoustic context that helps us to resolve
ambiguities or to recall named entities. Motivated by this, there have been
many works studying the integration of visual information into the speech
recognition pipeline. Specifically, in our previous work, we propose a
multistep visual adaptive training approach which improves the accuracy of an
audio-based Automatic Speech Recognition (ASR) system. This approach, however,
is not end-to-end as it requires fine-tuning the whole model with an adaptation
layer. In this paper, we propose novel end-to-end multimodal ASR systems and
compare them to the adaptive approach by using a range of visual
representations obtained from state-of-the-art convolutional neural networks.
We show that adaptive training is effective for S2S models leading to an
absolute improvement of 1.4% in word error rate. As for the end-to-end systems,
although they perform better than baseline, the improvements are slightly less
than adaptive training, 0.8 absolute WER reduction in single-best models. Using
ensemble decoding, end-to-end models reach a WER of 15% which is the lowest
score among all systems.
Targeting Solutions in Bayesian Multi-Objective Optimization: Sequential and Batch Versions
Authors: David Gaudrie, Rodolphe Le Riche, Victor Picheny, Benoit Enaux, Vincent Herbert
Date: 2018-11-09 00:00:00
Tasks: Gaussian Processes

Multi-objective optimization aims at finding trade-off solutions to conflicting objectives. These constitute the Pareto optimal set. In the context of expensive-to-evaluate functions, it is impossible and often non-informative to look for the entire set. As an end-user would typically prefer a certain part of the objective space, we modify the Bayesian multi-objective optimization algorithm which uses Gaussian Processes to maximize the Expected Hypervolume Improvement, to focus the search in the preferred region. The cumulated effects of the Gaussian Processes and the targeting strategy lead to a particularly efficient convergence to the desired part of the Pareto set. To take advantage of parallel computing, a multi-point extension of the targeting criterion is proposed and analyzed.
Sample-Efficient Policy Learning based on Completely Behavior Cloning
Authors: Qiming Zou, Ling Wang, Ke Lu, Yu Li
Date: 2018-11-09 00:00:00
Tasks: Model Predictive Control, Reinforcement Learning

Direct policy search is one of the most important algorithm of reinforcement
learning. However, learning from scratch needs a large amount of experience
data and can be easily prone to poor local optima. In addition to that, a
partially trained policy tends to perform dangerous action to agent and
environment. In order to overcome these challenges, this paper proposed a
policy initialization algorithm called Policy Learning based on Completely
Behavior Cloning (PLCBC). PLCBC first transforms the Model Predictive Control
(MPC) controller into a piecewise affine (PWA) function using multi-parametric
programming, and uses a neural network to express this function. By this way,
PLCBC can completely clone the MPC controller without any performance loss, and
is totally training-free. The experiments show that this initialization
strategy can help agent learn at the high reward state region, and converge
faster and better.
MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets
Authors: Corentin Hardy, Erwan Le Merrer, Bruno Sericola
Date: 2018-11-09 00:00:00
Tasks: Federated Learning

A recent technical breakthrough in the domain of machine learning is the
discovery and the multiple applications of Generative Adversarial Networks
(GANs). Those generative models are computationally demanding, as a GAN is
composed of two deep neural networks, and because it trains on large datasets.
A GAN is generally trained on a single server.
  In this paper, we address the problem of distributing GANs so that they are
able to train over datasets that are spread on multiple workers. MD-GAN is
exposed as the first solution for this problem: we propose a novel learning
procedure for GANs so that they fit this distributed setup. We then compare the
performance of MD-GAN to an adapted version of Federated Learning to GANs,
using the MNIST and CIFAR10 datasets. MD-GAN exhibits a reduction by a factor
of two of the learning complexity on each worker node, while providing better
performances than federated learning on both datasets. We finally discuss the
practical implications of distributing GANs.
An Average of the Human Ear Canal: Recovering Acoustical Properties via Shape Analysis
Authors: Sune Darkner, Stefan Sommer, Andreas Schuhmacher, Henrik Ingerslev Anders O. Baandrup, Carsten Thomsen, Søren Jønsson
Date: 2018-11-09 00:00:00
Tasks: Anatomy, Image Registration, Navigate

Humans are highly dependent on the ability to process audio in order to
interact through conversation and navigate from sound. For this, the shape of
the ear acts as a mechanical audio filter. The anatomy of the outer human ear
canal to approximately 15-20 mm beyond the Tragus is well described because of
its importance for customized hearing aid production. This is however not the
case for the part of the ear canal that is embedded in the skull, until the
typanic membrane. Due to the sensitivity of the outer ear, this part, referred
to as the bony part, has only been described in a few population studies and
only ex-vivo. We present a study of the entire ear canal including the bony
part and the tympanic membrane. We form an average ear canal from a number of
MRI scans using standard image registration methods. We show that the obtained
representation is realistic in the sense that it has acoustical properties
almost identical to a real ear.
Adaptive Regularization Algorithms with Inexact Evaluations for Nonconvex Optimization
Authors: S. Bellavia, G. Gurioli, B. Morini, Ph. L. Toint
Date: 2018-11-09 00:00:00

A regularization algorithm using inexact function values and inexact
derivatives is proposed and its evaluation complexity analyzed. This algorithm
is applicable to unconstrained problems and to problems with inexpensive
constraints (that is constraints whose evaluation and enforcement has
negligible cost) under the assumption that the derivative of highest degree is
$\beta$-H\"{o}lder continuous. It features a very flexible adaptive mechanism
for determining the inexactness which is allowed, at each iteration, when
computing objective function values and derivatives. The complexity analysis
covers arbitrary optimality order and arbitrary degree of available approximate
derivatives. It extends results of Cartis, Gould and Toint (2018) on the
evaluation complexity to the inexact case: if a $q$th order minimizer is sought
using approximations to the first $p$ derivatives, it is proved that a suitable
approximate minimizer within $\epsilon$ is computed by the proposed algorithm
in at most $O(\epsilon^{-\frac{p+\beta}{p-q+\beta}})$ iterations and at most
$O(|\log(\epsilon)|\epsilon^{-\frac{p+\beta}{p-q+\beta}})$ approximate
evaluations. An algorithmic variant, although more rigid in practice, can be
proved to find such an approximate minimizer in
$O(|\log(\epsilon)|+\epsilon^{-\frac{p+\beta}{p-q+\beta}})$ evaluations.While
the proposed framework remains so far conceptual for high degrees and orders,
it is shown to yield simple and computationally realistic inexact methods when
specialized to the unconstrained and bound-constrained first- and second-order
cases. The deterministic complexity results are finally extended to the
stochastic context, yielding adaptive sample-size rules for subsampling methods
typical of machine learning.
Image-Level Attentional Context Modeling Using Nested-Graph Neural Networks
Authors: Guillaume Jaume, Behzad Bozorgtabar, Hazim Kemal Ekenel, Jean-Philippe Thiran, Maria Gabrani
Date: 2018-11-09 00:00:00
Tasks: Graph Generation, Graph Neural Network, Object, Scene Graph Generation

We introduce a new scene graph generation method called image-level
attentional context modeling (ILAC). Our model includes an attentional graph
network that effectively propagates contextual information across the graph
using image-level features. Whereas previous works use an object-centric
context, we build an image-level context agent to encode the scene properties.
The proposed method comprises a single-stream network that iteratively refines
the scene graph with a nested graph neural network. We demonstrate that our
approach achieves competitive performance with the state-of-the-art for scene
graph generation on the Visual Genome dataset, while requiring fewer parameters
than other methods. We also show that ILAC can improve regular object detectors
by incorporating relational image-level information.