pwc-archive/papers-with-abstracts huggingface dataset:
Sim-to-Real Reinforcement Learning for Deformable Object Manipulation
Authors: Jan Matas, Stephen James, Andrew J. Davison
Date: 2018-06-20 00:00:00
Tasks: Deep Reinforcement Learning, Deformable Object Manipulation, Object, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We have seen much recent progress in rigid object manipulation, but
interaction with deformable objects has notably lagged behind. Due to the large
configuration space of deformable objects, solutions using traditional
modelling approaches require significant engineering work. Perhaps then,
bypassing the need for explicit modelling and instead learning the control in
an end-to-end manner serves as a better approach? Despite the growing interest
in the use of end-to-end robot learning approaches, only a small amount of work
has focused on their applicability to deformable object manipulation. Moreover,
due to the large amount of data needed to learn these end-to-end solutions, an
emerging trend is to learn control policies in simulation and then transfer
them over to the real world. To-date, no work has explored whether it is
possible to learn and transfer deformable object policies. We believe that if
sim-to-real methods are to be employed further, then it should be possible to
learn to interact with a wide variety of objects, and not only rigid objects.
In this work, we use a combination of state-of-the-art deep reinforcement
learning algorithms to solve the problem of manipulating deformable objects
(specifically cloth). We evaluate our approach on three tasks --- folding a
towel up to a mark, folding a face towel diagonally, and draping a piece of
cloth over a hanger. Our agents are fully trained in simulation with domain
randomisation, and then successfully deployed in the real world without having
seen any real deformable objects.
Log-sum-exp neural networks and posynomial models for convex and log-log-convex data
Authors: Giuseppe C. Calafiore, Stephane Gaubert, Corrado Possieri
Date: 2018-06-20 00:00:00

We show in this paper that a one-layer feedforward neural network with
exponential activation functions in the inner layer and logarithmic activation
in the output neuron is an universal approximator of convex functions. Such a
network represents a family of scaled log-sum exponential functions, here named
LSET. Under a suitable exponential transformation, the class of LSET functions
maps to a family of generalized posynomials GPOST, which we similarly show to
be universal approximators for log-log-convex functions. A key feature of an
LSET network is that, once it is trained on data, the resulting model is convex
in the variables, which makes it readily amenable to efficient design based on
convex optimization. Similarly, once a GPOST model is trained on data, it
yields a posynomial model that can be efficiently optimized with respect to its
variables by using geometric programming (GP). The proposed methodology is
illustrated by two numerical examples, in which, first, models are constructed
from simulation data of the two physical processes (namely, the level of
vibration in a vehicle suspension system, and the peak power generated by the
combustion of propane), and then optimization-based design is performed on
these models.
Rethinking Machine Learning Development and Deployment for Edge Devices
Authors: Liangzhen Lai, Naveen Suda
Date: 2018-06-20 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning (ML), especially deep learning is made possible by the
availability of big data, enormous compute power and, often overlooked,
development tools or frameworks. As the algorithms become mature and efficient,
more and more ML inference is moving out of datacenters/cloud and deployed on
edge devices. This model deployment process can be challenging as the
deployment environment and requirements can be substantially different from
those during model development. In this paper, we propose a new ML development
and deployment approach that is specially designed and optimized for
inference-only deployment on edge devices. We build a prototype and demonstrate
that this approach can address all the deployment challenges and result in more
efficient and high-quality solutions.
Hide and Seek tracker: Real-time recovery from target loss
Authors: Alessandro Bay, Panagiotis Sidiropoulos, Eduard Vazquez, Michele Sasdelli
Date: 2018-06-20 00:00:00
Tasks: Position

In this paper, we examine the real-time recovery of a video tracker from a
target loss, using information that is already available from the original
tracker and without a significant computational overhead. More specifically,
before using the tracker output to update the target position we estimate the
detection confidence. In the case of a low confidence, the position update is
rejected and the tracker passes to a single-frame failure mode, during which
the patch low-level visual content is used to swiftly update the object
position, before recovering from the target loss in the next frame.
Orthogonally to this improvement, we further enhance the running average method
used for creating the query model in tracking-through-similarity. The
experimental evidence provided by evaluation on standard tracking datasets
(OTB-50, OTB-100 and OTB-2013) validate that target recovery can be
successfully achieved without compromising the real-time update of the target
position.
Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergy
Authors: En Li, Zhi Zhou, Xu Chen
Date: 2018-06-20 00:00:00

As the backbone technology of machine learning, deep neural networks (DNNs)
have have quickly ascended to the spotlight. Running DNNs on
resource-constrained mobile devices is, however, by no means trivial, since it
incurs high performance and energy overhead. While offloading DNNs to the cloud
for execution suffers unpredictable performance, due to the uncontrolled long
wide-area network latency. To address these challenges, in this paper, we
propose Edgent, a collaborative and on-demand DNN co-inference framework with
device-edge synergy. Edgent pursues two design knobs: (1) DNN partitioning that
adaptively partitions DNN computation between device and edge, in order to
leverage hybrid computation resources in proximity for real-time DNN inference.
(2) DNN right-sizing that accelerates DNN inference through early-exit at a
proper intermediate DNN layer to further reduce the computation latency. The
prototype implementation and extensive evaluations based on Raspberry Pi
demonstrate Edgent's effectiveness in enabling on-demand low-latency edge
intelligence.
How Bad is Good enough: Noisy annotations for instrument pose estimation
Authors: David Kügler, Anirban Mukhopadhyay
Date: 2018-06-20 00:00:00
Tasks: Deep Learning, Medical Image Analysis, Pose Estimation, regression

Though analysis of Medical Images by Deep Learning achieves unprecedented
results across various applications, the effect of \emph{noisy training
annotations} is rarely studied in a systematic manner. In Medical Image
Analysis, most reports addressing this question concentrate on studying
segmentation performance of deep learning classifiers. The absence of
continuous ground truth annotations in these studies limits the value of
conclusions for applications, where regression is the primary method of choice.
In the application of surgical instrument pose estimation, where precision has
a direct clinical impact on patient outcome, studying the effect of \emph{noisy
annotations} on deep learning pose estimation techniques is of supreme
importance. Real x-ray images are inadequate for this evaluation due to the
unavailability of ground truth annotations. We circumvent this problem by
generating synthetic radiographs, where the ground truth pose is known and
therefore the pose estimation error made by the medical-expert can be estimated
from experiments. Furthermore, this study shows the property of deep neural
networks to learn dominant signals from noisy annotations with sufficient data
in a regression setting.
StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing
Authors: Pengcheng Yin, Chunting Zhou, Junxian He, Graham Neubig
Date: 2018-06-20 00:00:00
Proceeding: ACL 2018 7
Tasks: Code Generation, Semantic Parsing

Semantic parsing is the task of transducing natural language (NL) utterances
into formal meaning representations (MRs), commonly represented as tree
structures. Annotating NL utterances with their corresponding MRs is expensive
and time-consuming, and thus the limited availability of labeled data often
becomes the bottleneck of data-driven, supervised models. We introduce
StructVAE, a variational auto-encoding model for semisupervised semantic
parsing, which learns both from limited amounts of parallel data, and
readily-available unlabeled NL utterances. StructVAE models latent MRs not
observed in the unlabeled data as tree-structured latent variables. Experiments
on semantic parsing on the ATIS domain and Python code generation show that
with extra unlabeled data, StructVAE outperforms strong supervised models.
Unsupervised Learning of Object Landmarks through Conditional Image Generation
Authors: Tomas Jakab, Ankush Gupta, Hakan Bilen, Andrea Vedaldi
Date: 2018-06-20 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Conditional Image Generation, Image Generation, Object, Unsupervised Facial Landmark Detection

We propose a method for learning landmark detectors for visual objects (such
as the eyes and the nose in a face) without any manual supervision. We cast
this as the problem of generating images that combine the appearance of the
object as seen in a first example image with the geometry of the object as seen
in a second example image, where the two examples differ by a viewpoint change
and/or an object deformation. In order to factorize appearance and geometry, we
introduce a tight bottleneck in the geometry-extraction process that selects
and distils geometry-related features. Compared to standard image generation
problems, which often use generative adversarial networks, our generation task
is conditioned on both appearance and geometry and thus is significantly less
ambiguous, to the point that adopting a simple perceptual loss formulation is
sufficient. We demonstrate that our approach can learn object landmarks from
synthetic image deformations or videos, all without manual supervision, while
outperforming state-of-the-art unsupervised landmark detectors. We further show
that our method is applicable to a large variety of datasets - faces, people,
3D objects, and digits - without any modifications.
Learning Neural Parsers with Deterministic Differentiable Imitation Learning
Authors: Tanmay Shankar, Nicholas Rhinehart, Katharina Muelling, Kris M. Kitani
Date: 2018-06-20 00:00:00
Tasks: Imitation Learning, Reinforcement Learning

We explore the problem of learning to decompose spatial tasks into segments,
as exemplified by the problem of a painting robot covering a large object.
Inspired by the ability of classical decision tree algorithms to construct
structured partitions of their input spaces, we formulate the problem of
decomposing objects into segments as a parsing approach. We make the insight
that the derivation of a parse-tree that decomposes the object into segments
closely resembles a decision tree constructed by ID3, which can be done when
the ground-truth available. We learn to imitate an expert parsing oracle, such
that our neural parser can generalize to parse natural images without ground
truth. We introduce a novel deterministic policy gradient update, DRAG (i.e.,
DeteRministically AGgrevate) in the form of a deterministic actor-critic
variant of AggreVaTeD, to train our neural parser. From another perspective,
our approach is a variant of the Deterministic Policy Gradient suitable for the
imitation learning setting. The deterministic policy representation offered by
training our neural parser with DRAG allows it to outperform state of the art
imitation and reinforcement learning approaches.
Disentangling Multiple Conditional Inputs in GANs
Authors: Gökhan Yildirim, Calvin Seward, Urs Bergmann
Date: 2018-06-20 00:00:00
Tasks: Articles

In this paper, we propose a method that disentangles the effects of multiple
input conditions in Generative Adversarial Networks (GANs). In particular, we
demonstrate our method in controlling color, texture, and shape of a generated
garment image for computer-aided fashion design. To disentangle the effect of
input attributes, we customize conditional GANs with consistency loss
functions. In our experiments, we tune one input at a time and show that we can
guide our network to generate novel and realistic images of clothing articles.
In addition, we present a fashion design process that estimates the input
attributes of an existing garment and modifies them using our generator.
Metric-Driven Learning of Correspondence Weighting for 2-D/3-D Image Registration
Authors: Roman Schaffert, Jian Wang, Peter Fischer, Anja Borsdorf, Andreas Maier
Date: 2018-06-20 00:00:00
Tasks: Image Registration, Motion Estimation

Registration of pre-operative 3-D volumes to intra-operative 2-D X-ray images
is important in minimally invasive medical procedures. Rigid registration can
be performed by estimating a global rigid motion that optimizes the alignment
of local correspondences. However, inaccurate correspondences challenge the
registration performance. To minimize their influence, we estimate optimal
weights for correspondences using PointNet. We train the network directly with
the criterion to minimize the registration error. We propose an objective
function which includes point-to-plane correspondence-based motion estimation
and projection error computation, thereby enabling the learning of a weighting
strategy that optimally fits the underlying formulation of the registration
task in an end-to-end fashion. For single-vertebra registration, we achieve an
accuracy of 0.74$\pm$0.26 mm and highly improved robustness. The success rate
is increased from 79.3 % to 94.3 % and the capture range from 3 mm to 13 mm.
Stochastic Nested Variance Reduction for Nonconvex Optimization
Authors: Dongruo Zhou, Pan Xu, Quanquan Gu
Date: 2018-06-20 00:00:00
Proceeding: NeurIPS 2018 12

We study finite-sum nonconvex optimization problems, where the objective function is an average of $n$ nonconvex functions. We propose a new stochastic gradient descent algorithm based on nested variance reduction. Compared with conventional stochastic variance reduced gradient (SVRG) algorithm that uses two reference points to construct a semi-stochastic gradient with diminishing variance in each iteration, our algorithm uses $K+1$ nested reference points to build a semi-stochastic gradient to further reduce its variance in each iteration. For smooth nonconvex functions, the proposed algorithm converges to an $\epsilon$-approximate first-order stationary point (i.e., $\|\nabla F(\mathbf{x})\|_2\leq \epsilon$) within $\tilde O(n\land \epsilon^{-2}+\epsilon^{-3}\land n^{1/2}\epsilon^{-2})$ number of stochastic gradient evaluations. This improves the best known gradient complexity of SVRG $O(n+n^{2/3}\epsilon^{-2})$ and that of SCSG $O(n\land \epsilon^{-2}+\epsilon^{-10/3}\land n^{2/3}\epsilon^{-2})$. For gradient dominated functions, our algorithm also achieves better gradient complexity than the state-of-the-art algorithms. Thorough experimental results on different nonconvex optimization problems back up our theory.
Learning One-hidden-layer ReLU Networks via Gradient Descent
Authors: Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu
Date: 2018-06-20 00:00:00

We study the problem of learning one-hidden-layer neural networks with
Rectified Linear Unit (ReLU) activation function, where the inputs are sampled
from standard Gaussian distribution and the outputs are generated from a noisy
teacher network. We analyze the performance of gradient descent for training
such kind of neural networks based on empirical risk minimization, and provide
algorithm-dependent guarantees. In particular, we prove that tensor
initialization followed by gradient descent can converge to the ground-truth
parameters at a linear rate up to some statistical error. To the best of our
knowledge, this is the first work characterizing the recovery guarantee for
practical learning of one-hidden-layer ReLU networks with multiple neurons.
Numerical experiments verify our theoretical findings.
Quaternion Convolutional Neural Networks for End-to-End Automatic Speech Recognition
Authors: Titouan Parcollet, Ying Zhang, Mohamed Morchid, Chiheb Trabelsi, Georges Linarès, Renato De Mori, Yoshua Bengio
Date: 2018-06-20 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Phoneme Recognition, speech-recognition, Speech Recognition

Recently, the connectionist temporal classification (CTC) model coupled with
recurrent (RNN) or convolutional neural networks (CNN), made it easier to train
speech recognition systems in an end-to-end fashion. However in real-valued
models, time frame components such as mel-filter-bank energies and the cepstral
coefficients obtained from them, together with their first and second order
derivatives, are processed as individual elements, while a natural alternative
is to process such components as composed entities. We propose to group such
elements in the form of quaternions and to process these quaternions using the
established quaternion algebra. Quaternion numbers and quaternion neural
networks have shown their efficiency to process multidimensional inputs as
entities, to encode internal dependencies, and to solve many tasks with less
learning parameters than real-valued models. This paper proposes to integrate
multiple feature views in quaternion-valued convolutional neural network
(QCNN), to be used for sequence-to-sequence mapping with the CTC model.
Promising results are reported using simple QCNNs in phoneme recognition
experiments with the TIMIT corpus. More precisely, QCNNs obtain a lower phoneme
error rate (PER) with less learning parameters than a competing model based on
real-valued CNNs.
Random Feature Stein Discrepancies
Authors: Jonathan H. Huggins, Lester Mackey
Date: 2018-06-20 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Bayesian Inference

Computable Stein discrepancies have been deployed for a variety of applications, ranging from sampler selection in posterior inference to approximate Bayesian inference to goodness-of-fit testing. Existing convergence-determining Stein discrepancies admit strong theoretical guarantees but suffer from a computational cost that grows quadratically in the sample size. While linear-time Stein discrepancies have been proposed for goodness-of-fit testing, they exhibit avoidable degradations in testing power -- even when power is explicitly optimized. To address these shortcomings, we introduce feature Stein discrepancies ($\Phi$SDs), a new family of quality measures that can be cheaply approximated using importance sampling. We show how to construct $\Phi$SDs that provably determine the convergence of a sample to its target and develop high-accuracy approximations -- random $\Phi$SDs (R$\Phi$SDs) -- which are computable in near-linear time. In our experiments with sampler selection for approximate posterior inference and goodness-of-fit testing, R$\Phi$SDs perform as well or better than quadratic-time KSDs while being orders of magnitude faster to compute.
Opinion Dynamics Modeling for Movie Review Transcripts Classification with Hidden Conditional Random Fields
Authors: Valentin Barriere, Chloé Clavel, Slim Essid
Date: 2018-06-20 00:00:00
Tasks: General Classification

In this paper, the main goal is to detect a movie reviewer's opinion using
hidden conditional random fields. This model allows us to capture the dynamics
of the reviewer's opinion in the transcripts of long unsegmented audio reviews
that are analyzed by our system. High level linguistic features are computed at
the level of inter-pausal segments. The features include syntactic features, a
statistical word embedding model and subjectivity lexicons. The proposed system
is evaluated on the ICT-MMMO corpus. We obtain a F1-score of 82\%, which is
better than logistic regression and recurrent neural network approaches. We
also offer a discussion that sheds some light on the capacity of our system to
adapt the word embedding model learned from general written texts data to
spoken movie reviews and thus model the dynamics of the opinion.
Histological images segmentation of mucous glands
Authors: A. Khvostikov, A. Krylov, O. Kharlova, N. Oleynikova, I. Mikhailov, P. Malkov
Date: 2018-06-20 00:00:00
Tasks: Diagnostic, Segmentation

Mucous glands lesions analysis and assessing of malignant potential of colon
polyps are very important tasks of surgical pathology. However, differential
diagnosis of colon polyps often seems impossible by classical methods and it is
necessary to involve computer methods capable of assessing minimal differences
to extend the capabilities of the classical pathology examination. Accurate
segmentation of mucous glands from histology images is a crucial step to obtain
reliable morphometric criteria for quantitative diagnostic methods. We review
major trends in histological images segmentation and design a new convolutional
neural network for mucous gland segmentation.
Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT
Authors: Per Welander, Simon Karlsson, Anders Eklund
Date: 2018-06-20 00:00:00
Tasks: Computed Tomography (CT), Image-to-Image Translation, Medical Image Generation, Translation

In medical imaging, a general problem is that it is costly and time consuming
to collect high quality data from healthy and diseased subjects. Generative
adversarial networks (GANs) is a deep learning method that has been developed
for synthesizing data. GANs can thereby be used to generate more realistic
training data, to improve classification performance of machine learning
algorithms. Another application of GANs is image-to-image translations, e.g.
generating magnetic resonance (MR) images from computed tomography (CT) images,
which can be used to obtain multimodal datasets from a single modality. Here,
we evaluate two unsupervised GAN models (CycleGAN and UNIT) for image-to-image
translation of T1- and T2-weighted MR images, by comparing generated synthetic
MR images to ground truth images. We also evaluate two supervised models; a
modification of CycleGAN and a pure generator model. A small perceptual study
was also performed to evaluate how visually realistic the synthesized images
are. It is shown that the implemented GAN models can synthesize visually
realistic MR images (incorrectly labeled as real by a human). It is also shown
that models producing more visually realistic synthetic images not necessarily
have better quantitative error measurements, when compared to ground truth
data. Code is available at https://github.com/simontomaskarlsson/GAN-MRI
Accurate and Diverse Sampling of Sequences based on a "Best of Many" Sample Objective
Authors: Apratim Bhattacharyya, Bernt Schiele, Mario Fritz
Date: 2018-06-20 00:00:00
Tasks: Human Pose Forecasting

For autonomous agents to successfully operate in the real world, anticipation
of future events and states of their environment is a key competence. This
problem has been formalized as a sequence extrapolation problem, where a number
of observations are used to predict the sequence into the future. Real-world
scenarios demand a model of uncertainty of such predictions, as predictions
become increasingly uncertain -- in particular on long time horizons. While
impressive results have been shown on point estimates, scenarios that induce
multi-modal distributions over future sequences remain challenging. Our work
addresses these challenges in a Gaussian Latent Variable model for sequence
prediction. Our core contribution is a "Best of Many" sample objective that
leads to more accurate and more diverse predictions that better capture the
true variations in real-world sequence data. Beyond our analysis of improved
model fit, our models also empirically outperform prior work on three diverse
tasks ranging from traffic scenes to weather data.