pwc-archive/papers-with-abstracts huggingface dataset:
Semi-Automatic RECIST Labeling on CT Scans with Cascaded Convolutional Neural Networks
Authors: Youbao Tang, Adam P. Harrison, Mohammadhadi Bagheri, Jing Xiao, Ronald M. Summers
Date: 2018-06-25 00:00:00
Tasks: Multi-Task Learning

Response evaluation criteria in solid tumors (RECIST) is the standard
measurement for tumor extent to evaluate treatment responses in cancer
patients. As such, RECIST annotations must be accurate. However, RECIST
annotations manually labeled by radiologists require professional knowledge and
are time-consuming, subjective, and prone to inconsistency among different
observers. To alleviate these problems, we propose a cascaded convolutional
neural network based method to semi-automatically label RECIST annotations and
drastically reduce annotation time. The proposed method consists of two stages:
lesion region normalization and RECIST estimation. We employ the spatial
transformer network (STN) for lesion region normalization, where a localization
network is designed to predict the lesion region and the transformation
parameters with a multi-task learning strategy. For RECIST estimation, we adapt
the stacked hourglass network (SHN), introducing a relationship constraint loss
to improve the estimation precision. STN and SHN can both be learned in an
end-to-end fashion. We train our system on the DeepLesion dataset, obtaining a
consensus model trained on RECIST annotations performed by multiple
radiologists over a multi-year period. Importantly, when judged against the
inter-reader variability of two additional radiologist raters, our system
performs more stably and with less variability, suggesting that RECIST
annotations can be reliably obtained with reduced labor and time.
Finding Optimal Solutions to Token Swapping by Conflict-based Search and Reduction to SAT
Authors: Pavel Surynek
Date: 2018-06-25 00:00:00
Tasks: Multi-Agent Path Finding

We study practical approaches to solving the token swapping (TSWAP) problem
optimally in this short paper. In TSWAP, we are given an undirected graph with
colored vertices. A colored token is placed in each vertex. A pair of tokens
can be swapped between adjacent vertices. The goal is to perform a sequence of
swaps so that token and vertex colors agree across the graph. The minimum
number of swaps is required in the optimization variant of the problem. We
observed similarities between the TSWAP problem and multi-agent path finding
(MAPF) where instead of tokens we have multiple agents that need to be moved
from their current vertices to given unique target vertices. The difference
between both problems consists in local conditions that state transitions
(swaps/moves) must satisfy. We developed two algorithms for solving TSWAP
optimally by adapting two different approaches to MAPF - CBS and MDD- SAT. This
constitutes the first attempt to design optimal solving algorithms for TSWAP.
Experimental evaluation on various types of graphs shows that the reduction to
SAT scales better than CBS in optimal TSWAP solving.
Does data interpolation contradict statistical optimality?
Authors: Mikhail Belkin, Alexander Rakhlin, Alexandre B. Tsybakov
Date: 2018-06-25 00:00:00
Tasks: Prediction, regression

We show that learning methods interpolating the training data can achieve
optimal rates for the problems of nonparametric regression and prediction with
square loss.
A Tour of Reinforcement Learning: The View from Continuous Control
Authors: Benjamin Recht
Date: 2018-06-25 00:00:00
Tasks: continuous-control, Continuous Control, Learning Theory, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Survey

This manuscript surveys reinforcement learning from the perspective of
optimization and control with a focus on continuous control applications. It
surveys the general formulation, terminology, and typical experimental
implementations of reinforcement learning and reviews competing solution
paradigms. In order to compare the relative merits of various techniques, this
survey presents a case study of the Linear Quadratic Regulator (LQR) with
unknown dynamics, perhaps the simplest and best-studied problem in optimal
control. The manuscript describes how merging techniques from learning theory
and control can provide non-asymptotic characterizations of LQR performance and
shows that these characterizations tend to match experimental behavior. In
turn, when revisiting more complex applications, many of the observed phenomena
in LQR persist. In particular, theory and experiment demonstrate the role and
importance of models and the cost of generality in reinforcement learning
algorithms. This survey concludes with a discussion of some of the challenges
in designing learning systems that safely and reliably interact with complex
and uncertain environments and how tools from reinforcement learning and
control might be combined to approach these challenges.
Compact Policies for Fully-Observable Non-Deterministic Planning as SAT
Authors: Tomas Geffner, Hector Geffner
Date: 2018-06-25 00:00:00

Fully observable non-deterministic (FOND) planning is becoming increasingly
important as an approach for computing proper policies in probabilistic
planning, extended temporal plans in LTL planning, and general plans in
generalized planning. In this work, we introduce a SAT encoding for FOND
planning that is compact and can produce compact strong cyclic policies. Simple
variations of the encodings are also introduced for strong planning and for
what we call, dual FOND planning, where some non-deterministic actions are
assumed to be fair (e.g., probabilistic) and others unfair (e.g., adversarial).
The resulting FOND planners are compared empirically with existing planners
over existing and new benchmarks. The notion of "probabilistic interesting
problems" is also revisited to yield a more comprehensive picture of the
strengths and limitations of current FOND planners and the proposed SAT
approach.
Context-Aware Pedestrian Motion Prediction In Urban Intersections
Authors: Golnaz Habibi, Nikita Jaipuria, Jonathan P. How
Date: 2018-06-25 00:00:00
Tasks: Clustering, motion prediction, Prediction

This paper presents a novel context-based approach for pedestrian motion
prediction in crowded, urban intersections, with the additional flexibility of
prediction in similar, but new, environments. Previously, Chen et. al. combined
Markovian-based and clustering-based approaches to learn motion primitives in a
grid-based world and subsequently predict pedestrian trajectories by modeling
the transition between learned primitives as a Gaussian Process (GP). This work
extends that prior approach by incorporating semantic features from the
environment (relative distance to curbside and status of pedestrian traffic
lights) in the GP formulation for more accurate predictions of pedestrian
trajectories over the same timescale. We evaluate the new approach on
real-world data collected using one of the vehicles in the MIT Mobility On
Demand fleet. The results show 12.5% improvement in prediction accuracy and a
2.65 times reduction in Area Under the Curve (AUC), which is used as a metric
to quantify the span of predicted set of trajectories, such that a lower AUC
corresponds to a higher level of confidence in the future direction of
pedestrian motion.
A Unified Model with Structured Output for Fashion Images Classification
Authors: Beatriz Quintino Ferreira, Luís Baía, João Faria, Ricardo Gamelas Sousa
Date: 2018-06-25 00:00:00
Tasks: Classification, General Classification, Specificity

A picture is worth a thousand words. Albeit a clich\'e, for the fashion
industry, an image of a clothing piece allows one to perceive its category
(e.g., dress), sub-category (e.g., day dress) and properties (e.g., white
colour with floral patterns). The seasonal nature of the fashion industry
creates a highly dynamic and creative domain with evermore data, making it
unpractical to manually describe a large set of images (of products). In this
paper, we explore the concept of visual recognition for fashion images through
an end-to-end architecture embedding the hierarchical nature of the annotations
directly into the model. Towards that goal, and inspired by the work of [7], we
have modified and adapted the original architecture proposal. Namely, we have
removed the message passing layer symmetry to cope with Farfetch category tree,
added extra layers for hierarchy level specificity, and moved the message
passing layer into an enriched latent space. We compare the proposed unified
architecture against state-of-the-art models and demonstrate the performance
advantage of our model for structured multi-level categorization on a dataset
of about 350k fashion product images.
A Transferable Pedestrian Motion Prediction Model for Intersections with Different Geometries
Authors: Nikita Jaipuria, Golnaz Habibi, Jonathan P. How
Date: 2018-06-25 00:00:00
Tasks: motion prediction, Prediction

This paper presents a novel framework for accurate pedestrian intent
prediction at intersections. Given some prior knowledge of the curbside
geometry, the presented framework can accurately predict pedestrian
trajectories, even in new intersections that it has not been trained on. This
is achieved by making use of the contravariant components of trajectories in
the curbside coordinate system, which ensures that the transformation of
trajectories across intersections is affine, regardless of the curbside
geometry. Our method is based on the Augmented Semi Nonnegative Sparse Coding
(ASNSC) formulation and we use that as a baseline to show improvement in
prediction performance on real pedestrian datasets collected at two
intersections in Cambridge, with distinctly different curbside and crosswalk
geometries. We demonstrate a 7.2% improvement in prediction accuracy in the
case of same train and test intersections. Furthermore, we show a comparable
prediction performance of TASNSC when trained and tested in different
intersections with the baseline, trained and tested on the same intersection.
Prior Attention for Style-aware Sequence-to-Sequence Models
Authors: Lucas Sterckx, Johannes Deleu, Chris Develder, Thomas Demeester
Date: 2018-06-25 00:00:00
Tasks: Lexical Simplification, Sentence

We extend sequence-to-sequence models with the possibility to control the
characteristics or style of the generated output, via attention that is
generated a priori (before decoding) from a latent code vector. After training
an initial attention-based sequence-to-sequence model, we use a variational
auto-encoder conditioned on representations of input sequences and a latent
code vector space to generate attention matrices. By sampling the code vector
from specific regions of this latent space during decoding and imposing prior
attention generated from it in the seq2seq model, output can be steered towards
having certain attributes. This is demonstrated for the task of sentence
simplification, where the latent code vector allows control over output length
and lexical simplification, and enables fine-tuning to optimize for different
evaluation metrics.
Predicting Effective Control Parameters for Differential Evolution using Cluster Analysis of Objective Function Features
Authors: Sean P. Walton, M. Rowan Brown
Date: 2018-06-25 00:00:00

A methodology is introduced which uses three simple objective function features to predict effective control parameters for differential evolution. This is achieved using cluster analysis techniques to classify objective functions using these features. Information on prior performance of various control parameters for each classification is then used to determine which control parameters to use in future optimisations. Our approach is compared to state-of-the-art adaptive and non-adaptive techniques. Two accepted bench mark suites are used to compare performance and in all cases we show that the improvement resulting from our approach is statistically significant. The majority of the computational effort of this methodology is performed off-line, however even when taking into account the additional on-line cost our approach outperforms other adaptive techniques. We also investigate the key tuning parameters of our methodology, such as number of clusters, which further support the finding that the simple features selected are predictors of effective control parameters. The findings presented in this paper are significant because they show that simple to calculate features of objective functions can help to select control parameters for optimisation algorithms. This can have an immediate positive impact on the application of these optimisation algorithms on real world problems, where it is often difficult to select effective control parameters.
Propagating Uncertainty through the tanh Function with Application to Reservoir Computing
Authors: Manan Gandhi, Keuntaek Lee, Yunpeng Pan, Evangelos Theodorou
Date: 2018-06-25 00:00:00

Many neural networks use the tanh activation function, however when given a
probability distribution as input, the problem of computing the output
distribution in neural networks with tanh activation has not yet been
addressed. One important example is the initialization of the echo state
network in reservoir computing, where random initialization of the reservoir
requires time to wash out the initial conditions, thereby wasting precious data
and computational resources. Motivated by this problem, we propose a novel
solution utilizing a moment based approach to propagate uncertainty through an
Echo State Network to reduce the washout time. In this work, we contribute two
new methods to propagate uncertainty through the tanh activation function and
propose the Probabilistic Echo State Network (PESN), a method that is shown to
have better average performance than deterministic Echo State Networks given
the random initialization of reservoir states. Additionally we test single and
multi-step uncertainty propagation of our method on two regression tasks and
show that we are able to recover similar means and variances as computed by
Monte-Carlo simulations.
A Distributed Flexible Delay-tolerant Proximal Gradient Algorithm
Authors: Konstantin Mishchenko, Franck Iutzeler, Jérôme Malick
Date: 2018-06-25 00:00:00

We develop and analyze an asynchronous algorithm for distributed convex optimization when the objective writes a sum of smooth functions, local to each worker, and a non-smooth function. Unlike many existing methods, our distributed algorithm is adjustable to various levels of communication cost, delays, machines computational power, and functions smoothness. A unique feature is that the stepsizes do not depend on communication delays nor number of machines, which is highly desirable for scalability. We prove that the algorithm converges linearly in the strongly convex case, and provide guarantees of convergence for the non-strongly convex case. The obtained rates are the same as the vanilla proximal gradient algorithm over some introduced epoch sequence that subsumes the delays of the system. We provide numerical results on large-scale machine learning problems to demonstrate the merits of the proposed method.
Exploring Adversarial Examples: Patterns of One-Pixel Attacks
Authors: David Kügler, Alexander Distergoft, Arjan Kuijper, Anirban Mukhopadhyay
Date: 2018-06-25 00:00:00
Tasks: Pose Estimation

Failure cases of black-box deep learning, e.g. adversarial examples, might
have severe consequences in healthcare. Yet such failures are mostly studied in
the context of real-world images with calibrated attacks. To demystify the
adversarial examples, rigorous studies need to be designed. Unfortunately,
complexity of the medical images hinders such study design directly from the
medical images. We hypothesize that adversarial examples might result from the
incorrect mapping of image space to the low dimensional generation manifold by
deep networks. To test the hypothesis, we simplify a complex medical problem
namely pose estimation of surgical tools into its barest form. An analytical
decision boundary and exhaustive search of the one-pixel attack across multiple
image dimensions let us localize the regions of frequent successful one-pixel
attacks at the image space.
Accelerating likelihood optimization for ICA on real signals
Authors: Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort
Date: 2018-06-25 00:00:00

We study optimization methods for solving the maximum likelihood formulation
of independent component analysis (ICA). We consider both the the problem
constrained to white signals and the unconstrained problem. The Hessian of the
objective function is costly to compute, which renders Newton's method
impractical for large data sets. Many algorithms proposed in the literature can
be rewritten as quasi-Newton methods, for which the Hessian approximation is
cheap to compute. These algorithms are very fast on simulated data where the
linear mixture assumption really holds. However, on real signals, we observe
that their rate of convergence can be severely impaired. In this paper, we
investigate the origins of this behavior, and show that the recently proposed
Preconditioned ICA for Real Data (Picard) algorithm overcomes this issue on
both constrained and unconstrained problems.
An Unsupervised Learning Classifier with Competitive Error Performance
Authors: Daniel N. Nissani
Date: 2018-06-25 00:00:00
Tasks: Classification, General Classification

An unsupervised learning classification model is described. It achieves classification error probability competitive with that of popular supervised learning classifiers such as SVM or kNN. The model is based on the incremental execution of small step shift and rotation operations upon selected discriminative hyperplanes at the arrival of input samples. When applied, in conjunction with a selected feature extractor, to a subset of the ImageNet dataset benchmark, it yields 6.2 % Top 3 probability of error; this exceeds by merely about 2 % the result achieved by (supervised) k-Nearest Neighbor, both using same feature extractor. This result may also be contrasted with popular unsupervised learning schemes such as k-Means which is shown to be practically useless on same dataset.
Fast ASR-free and almost zero-resource keyword spotting using DTW and CNNs for humanitarian monitoring
Authors: Raghav Menon, Herman Kamper, John Quinn, Thomas Niesler
Date: 2018-06-25 00:00:00
Tasks: Dynamic Time Warping, Humanitarian, Keyword Spotting, Template Matching

We use dynamic time warping (DTW) as supervision for training a convolutional
neural network (CNN) based keyword spotting system using a small set of spoken
isolated keywords. The aim is to allow rapid deployment of a keyword spotting
system in a new language to support urgent United Nations (UN) relief
programmes in parts of Africa where languages are extremely under-resourced and
the development of annotated speech resources is infeasible. First, we use 1920
recorded keywords (40 keyword types, 34 minutes of speech) as exemplars in a
DTW-based template matching system and apply it to untranscribed broadcast
speech. Then, we use the resulting DTW scores as targets to train a CNN on the
same unlabelled speech. In this way we use just 34 minutes of labelled speech,
but leverage a large amount of unlabelled data for training. While the
resulting CNN keyword spotter cannot match the performance of the DTW-based
system, it substantially outperforms a CNN classifier trained only on the
keywords, improving the area under the ROC curve from 0.54 to 0.64. Because our
CNN system is several orders of magnitude faster at runtime than the DTW
system, it represents the most viable keyword spotter on this extremely limited
dataset.
Multi-objective Model-based Policy Search for Data-efficient Learning with Sparse Rewards
Authors: Rituraj Kaushik, Konstantinos Chatzilygeroudis, Jean-Baptiste Mouret
Date: 2018-06-25 00:00:00
Tasks: Continuous Control, Efficient Exploration, Reinforcement Learning

The most data-efficient algorithms for reinforcement learning in robotics are model-based policy search algorithms, which alternate between learning a dynamical model of the robot and optimizing a policy to maximize the expected return given the model and its uncertainties. However, the current algorithms lack an effective exploration strategy to deal with sparse or misleading reward scenarios: if they do not experience any state with a positive reward during the initial random exploration, it is very unlikely to solve the problem. Here, we propose a novel model-based policy search algorithm, Multi-DEX, that leverages a learned dynamical model to efficiently explore the task space and solve tasks with sparse rewards in a few episodes. To achieve this, we frame the policy search problem as a multi-objective, model-based policy optimization problem with three objectives: (1) generate maximally novel state trajectories, (2) maximize the expected return and (3) keep the system in state-space regions for which the model is as accurate as possible. We then optimize these objectives using a Pareto-based multi-objective optimization algorithm. The experiments show that Multi-DEX is able to solve sparse reward scenarios (with a simulated robotic arm) in much lower interaction time than VIME, TRPO, GEP-PG, CMA-ES and Black-DROPS.
Sparse 3D Point-cloud Map Upsampling and Noise Removal as a vSLAM Post-processing Step: Experimental Evaluation
Authors: Andrey Bokovoy, Konstantin Yakovlev
Date: 2018-06-25 00:00:00
Tasks: Simultaneous Localization and Mapping

The monocular vision-based simultaneous localization and mapping (vSLAM) is
one of the most challenging problem in mobile robotics and computer vision. In
this work we study the post-processing techniques applied to sparse 3D
point-cloud maps, obtained by feature-based vSLAM algorithms. Map
post-processing is split into 2 major steps: 1) noise and outlier removal and
2) upsampling. We evaluate different combinations of known algorithms for
outlier removing and upsampling on datasets of real indoor and outdoor
environments and identify the most promising combination. We further use it to
convert a point-cloud map, obtained by the real UAV performing indoor flight to
3D voxel grid (octo-map) potentially suitable for path planning.
Diversified Late Acceptance Search
Authors: Majid Namazi, Conrad Sanderson, M. A. Hakim Newton, M. M. A. Polash, Abdul Sattar
Date: 2018-06-25 00:00:00
Tasks: Diversity

The well-known Late Acceptance Hill Climbing (LAHC) search aims to overcome
the main downside of traditional Hill Climbing (HC) search, which is often
quickly trapped in a local optimum due to strictly accepting only non-worsening
moves within each iteration. In contrast, LAHC also accepts worsening moves, by
keeping a circular array of fitness values of previously visited solutions and
comparing the fitness values of candidate solutions against the least recent
element in the array. While this straightforward strategy has proven effective,
there are nevertheless situations where LAHC can unfortunately behave in a
similar manner to HC. For example, when a new local optimum is found, often the
same fitness value is stored many times in the array. To address this
shortcoming, we propose new acceptance and replacement strategies to take into
account worsening, improving, and sideways movement scenarios with the aim to
improve the diversity of values in the array. Compared to LAHC, the proposed
Diversified Late Acceptance Search approach is shown to lead to better quality
solutions that are obtained with a lower number of iterations on benchmark
Travelling Salesman Problems and Quadratic Assignment Problems.