pwc-archive/papers-with-abstracts huggingface dataset:
Machine Learning for Quantum Dynamics: Deep Learning of Excitation Energy Transfer Properties
Authors: Florian Häse, Christoph Kreisbeck, Alán Aspuru-Guzik
Date: 2017-07-20 00:00:00
Tasks: BIG-bench Machine Learning

Understanding the relationship between the structure of light-harvesting
systems and their excitation energy transfer properties is of fundamental
importance in many applications including the development of next generation
photovoltaics. Natural light harvesting in photosynthesis shows remarkable
excitation energy transfer properties, which suggests that pigment-protein
complexes could serve as blueprints for the design of nature inspired devices.
Mechanistic insights into energy transport dynamics can be gained by leveraging
numerically involved propagation schemes such as the hierarchical equations of
motion (HEOM). Solving these equations, however, is computationally costly due
to the adverse scaling with the number of pigments. Therefore virtual
high-throughput screening, which has become a powerful tool in material
discovery, is less readily applicable for the search of novel excitonic
devices. We propose the use of artificial neural networks to bypass the
computational limitations of established techniques for exploring the
structure-dynamics relation in excitonic systems. Once trained, our neural
networks reduce computational costs by several orders of magnitudes. Our
predicted transfer times and transfer efficiencies exhibit similar or even
higher accuracies than frequently used approximate methods such as secular
Redfield theory
Sunrise or Sunset: Selective Comparison Learning for Subtle Attribute Recognition
Authors: Hong-Yu Zhou, Bin-Bin Gao, Jianxin Wu
Date: 2017-07-20 00:00:00
Tasks: Attribute

The difficulty of image recognition has gradually increased from general
category recognition to fine-grained recognition and to the recognition of some
subtle attributes such as temperature and geolocation. In this paper, we try to
focus on the classification between sunrise and sunset and hope to give a hint
about how to tell the difference in subtle attributes. Sunrise vs. sunset is a
difficult recognition task, which is challenging even for humans. Towards
understanding this new problem, we first collect a new dataset made up of over
one hundred webcams from different places. Since existing algorithmic methods
have poor accuracy, we propose a new pairwise learning strategy to learn
features from selective pairs of images. Experiments show that our approach
surpasses baseline methods by a large margin and achieves better results even
compared with humans. We also apply our approach to existing subtle attribute
recognition problems, such as temperature estimation, and achieve
state-of-the-art results.
Multi-Branch Fully Convolutional Network for Face Detection
Authors: Yancheng Bai, Bernard Ghanem
Date: 2017-07-20 00:00:00
Tasks: Face Detection, GPU

Face detection is a fundamental problem in computer vision. It is still a
challenging task in unconstrained conditions due to significant variations in
scale, pose, expressions, and occlusion. In this paper, we propose a
multi-branch fully convolutional network (MB-FCN) for face detection, which
considers both efficiency and effectiveness in the design process. Our MB-FCN
detector can deal with faces at all scale ranges with only a single pass
through the backbone network. As such, our MB-FCN model saves computation and
thus is more efficient, compared to previous methods that make multiple passes.
For each branch, the specific skip connections of the convolutional feature
maps at different layers are exploited to represent faces in specific scale
ranges. Specifically, small faces can be represented with both shallow
fine-grained and deep powerful coarse features. With this representation,
superior improvement in performance is registered for the task of detecting
small faces. We test our MB-FCN detector on two public face detection
benchmarks, including FDDB and WIDER FACE. Extensive experiments show that our
detector outperforms state-of-the-art methods on all these datasets in general
and by a substantial margin on the most challenging among them (e.g. WIDER FACE
Hard subset). Also, MB-FCN runs at 15 FPS on a GPU for images of size 640 x 480
with no assumption on the minimum detectable face size.
Deep Neural Networks for Improved, Impromptu Trajectory Tracking of Quadrotors
Authors: Qiyang Li, Jingxing Qian, Zining Zhu, Xuchan Bao, Mohamed K. Helwa, Angela P. Schoellig
Date: 2016-10-20 00:00:00
Tasks: Unity

Trajectory tracking control for quadrotors is important for applications
ranging from surveying and inspection, to film making. However, designing and
tuning classical controllers, such as proportional-integral-derivative (PID)
controllers, to achieve high tracking precision can be time-consuming and
difficult, due to hidden dynamics and other non-idealities. The Deep Neural
Network (DNN), with its superior capability of approximating abstract,
nonlinear functions, proposes a novel approach for enhancing trajectory
tracking control. This paper presents a DNN-based algorithm as an add-on module
that improves the tracking performance of a classical feedback controller.
Given a desired trajectory, the DNNs provide a tailored reference input to the
controller based on their gained experience. The input aims to achieve a unity
map between the desired and the output trajectory. The motivation for this work
is an interactive "fly-as-you-draw" application, in which a user draws a
trajectory on a mobile device, and a quadrotor instantly flies that trajectory
with the DNN-enhanced control system. Experimental results demonstrate that the
proposed approach improves the tracking precision for user-drawn trajectories
after the DNNs are trained on selected periodic trajectories, suggesting the
method's potential in real-world applications. Tracking errors are reduced by
around 40-50% for both training and testing trajectories from users,
highlighting the DNNs' capability of generalizing knowledge.
Automatic Segmentation of Retinal Vasculature
Authors: Renoh Johnson Chalakkal, Waleed Abdulla
Date: 2017-07-19 00:00:00
Tasks: Segmentation

Segmentation of retinal vessels from retinal fundus images is the key step in
the automatic retinal image analysis. In this paper, we propose a new
unsupervised automatic method to segment the retinal vessels from retinal
fundus images. Contrast enhancement and illumination correction are carried out
through a series of image processing steps followed by adaptive histogram
equalization and anisotropic diffusion filtering. This image is then converted
to a gray scale using weighted scaling. The vessel edges are enhanced by
boosting the detail curvelet coefficients. Optic disk pixels are removed before
applying fuzzy C-mean classification to avoid the misclassification.
Morphological operations and connected component analysis are applied to obtain
the segmented retinal vessels. The performance of the proposed method is
evaluated using DRIVE database to be able to compare with other state-of-art
supervised and unsupervised methods. The overall segmentation accuracy of the
proposed method is 95.18% which outperforms the other algorithms.
DenseNet for Dense Flow
Authors: Yi Zhu, Shawn Newsam
Date: 2017-07-19 00:00:00
Tasks: Motion Estimation, Optical Flow Estimation

Classical approaches for estimating optical flow have achieved rapid progress
in the last decade. However, most of them are too slow to be applied in
real-time video analysis. Due to the great success of deep learning, recent
work has focused on using CNNs to solve such dense prediction problems. In this
paper, we investigate a new deep architecture, Densely Connected Convolutional
Networks (DenseNet), to learn optical flow. This specific architecture is ideal
for the problem at hand as it provides shortcut connections throughout the
network, which leads to implicit deep supervision. We extend current DenseNet
to a fully convolutional network to learn motion estimation in an unsupervised
manner. Evaluation results on three standard benchmarks demonstrate that
DenseNet is a better fit than other widely adopted CNN architectures for
optical flow estimation.
Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks
Authors: Aleksander Klibisz, Derek Rose, Matthew Eicholtz, Jay Blundon, Stanislav Zakharenko
Date: 2017-07-19 00:00:00
Tasks: Deep Learning

Calcium imaging is a technique for observing neuron activity as a series of
images showing indicator fluorescence over time. Manually segmenting neurons is
time-consuming, leading to research on automated calcium imaging segmentation
(ACIS). We evaluated several deep learning models for ACIS on the Neurofinder
competition datasets and report our best model: U-Net2DS, a fully convolutional
network that operates on 2D mean summary images. U-Net2DS requires minimal
domain-specific pre/post-processing and parameter adjustment, and predictions
are made on full $512\times512$ images at $\approx$9K images per minute. It
ranks third in the Neurofinder competition ($F_1=0.569$) and is the best model
to exclusively use deep learning. We also demonstrate useful segmentations on
data from outside the competition. The model's simplicity, speed, and quality
results make it a practical choice for ACIS and a strong baseline for more
complex models in the future.
Guided Open Vocabulary Image Captioning with Constrained Beam Search
Authors: Peter Anderson, Basura Fernando, Mark Johnson, Stephen Gould
Date: 2016-12-02 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Image Captioning, TAG, Word Embeddings

Existing image captioning models do not generalize well to out-of-domain
images containing novel scenes or objects. This limitation severely hinders the
use of these models in real world applications dealing with images in the wild.
We address this problem using a flexible approach that enables existing deep
captioning architectures to take advantage of image taggers at test time,
without re-training. Our method uses constrained beam search to force the
inclusion of selected tag words in the output, and fixed, pretrained word
embeddings to facilitate vocabulary expansion to previously unseen tag words.
Using this approach we achieve state of the art results for out-of-domain
captioning on MSCOCO (and improved results for in-domain captioning). Perhaps
surprisingly, our results significantly outperform approaches that incorporate
the same tag predictions into the learning algorithm. We also show that we can
significantly improve the quality of generated ImageNet captions by leveraging
ground-truth labels.
Learning from Simulated and Unsupervised Images through Adversarial Training
Authors: Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Josh Susskind, Wenda Wang, Russ Webb
Date: 2016-12-22 00:00:00
Proceeding: CVPR 2017 7
Tasks: Domain Adaptation, Gaze Estimation, Hand Pose Estimation, Image-to-Image Translation, Pose Estimation

With recent progress in graphics, it has become more tractable to train
models on synthetic images, potentially avoiding the need for expensive
annotations. However, learning from synthetic images may not achieve the
desired performance due to a gap between synthetic and real image
distributions. To reduce this gap, we propose Simulated+Unsupervised (S+U)
learning, where the task is to learn a model to improve the realism of a
simulator's output using unlabeled real data, while preserving the annotation
information from the simulator. We develop a method for S+U learning that uses
an adversarial network similar to Generative Adversarial Networks (GANs), but
with synthetic images as inputs instead of random vectors. We make several key
modifications to the standard GAN algorithm to preserve annotations, avoid
artifacts, and stabilize training: (i) a 'self-regularization' term, (ii) a
local adversarial loss, and (iii) updating the discriminator using a history of
refined images. We show that this enables generation of highly realistic
images, which we demonstrate both qualitatively and with a user study. We
quantitatively evaluate the generated images by training models for gaze
estimation and hand pose estimation. We show a significant improvement over
using synthetic images, and achieve state-of-the-art results on the MPIIGaze
dataset without any labeled real data.
Reward-Balancing for Statistical Spoken Dialogue Systems using Multi-objective Reinforcement Learning
Authors: Stefan Ultes, Paweł Budzianowski, Iñigo Casanueva, Nikola Mrkšić, Lina Rojas-Barahona, Pei-Hao Su, Tsung-Hsien Wen, Milica Gašić, Steve Young
Date: 2017-07-19 00:00:00
Proceeding: WS 2017 8
Tasks: Multi-Objective Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Spoken Dialogue Systems

Reinforcement learning is widely used for dialogue policy optimization where
the reward function often consists of more than one component, e.g., the
dialogue success and the dialogue length. In this work, we propose a structured
method for finding a good balance between these components by searching for the
optimal reward component weighting. To render this search feasible, we use
multi-objective reinforcement learning to significantly reduce the number of
training dialogues required. We apply our proposed method to find optimized
component weights for six domains and compare them to a default baseline.
STag: A Stable Fiducial Marker System
Authors: Burak Benligiray, Cihan Topal, Cuneyt Akinlar
Date: 2017-07-19 00:00:00
Tasks: Homography Estimation, Pose Estimation

Fiducial markers provide better-defined features than the ones naturally available in the scene. For this reason, they are widely utilized in computer vision applications where reliable pose estimation is required. Factors such as imaging noise and subtle changes in illumination induce jitter on the estimated pose. Jitter impairs robustness in vision and robotics applications, and deteriorates the sense of presence and immersion in AR/VR applications. In this paper, we propose STag, a fiducial marker system that provides stable pose estimation. STag is designed to be robust against jitter factors, thus sustains pose stability better than the existing solutions. This is achieved by utilizing geometric features that can be localized more repeatably. The outer square border of the marker is used for detection and homography estimation. This is followed by a novel homography refinement step using the inner circular border. After refinement, the pose can be estimated stably and robustly across viewing conditions. These features are demonstrated with a comprehensive set of experiments, including comparisons with the state of the art fiducial marker systems.
Pose-Invariant Face Alignment with a Single CNN
Authors: Amin Jourabloo, Mao Ye, Xiaoming Liu, Liu Ren
Date: 2017-07-19 00:00:00
Proceeding: ICCV 2017 10
Tasks: Face Alignment, Facial Landmark Detection

Face alignment has witnessed substantial progress in the last decade. One of
the recent focuses has been aligning a dense 3D face shape to face images with
large head poses. The dominant technology used is based on the cascade of
regressors, e.g., CNN, which has shown promising results. Nonetheless, the
cascade of CNNs suffers from several drawbacks, e.g., lack of end-to-end
training, hand-crafted features and slow training speed. To address these
issues, we propose a new layer, named visualization layer, that can be
integrated into the CNN architecture and enables joint optimization with
different loss functions. Extensive evaluation of the proposed method on
multiple datasets demonstrates state-of-the-art accuracy, while reducing the
training time by more than half compared to the typical cascade of CNNs. In
addition, we compare multiple CNN architectures with the visualization layer to
further demonstrate the advantage of its utilization.
Shape Generation using Spatially Partitioned Point Clouds
Authors: Matheus Gadelha, Subhransu Maji, Rui Wang
Date: 2017-07-19 00:00:00

We propose a method to generate 3D shapes using point clouds. Given a
point-cloud representation of a 3D shape, our method builds a kd-tree to
spatially partition the points. This orders them consistently across all
shapes, resulting in reasonably good correspondences across all shapes. We then
use PCA analysis to derive a linear shape basis across the spatially
partitioned points, and optimize the point ordering by iteratively minimizing
the PCA reconstruction error. Even with the spatial sorting, the point clouds
are inherently noisy and the resulting distribution over the shape coefficients
can be highly multi-modal. We propose to use the expressive power of neural
networks to learn a distribution over the shape coefficients in a
generative-adversarial framework. Compared to 3D shape generative models
trained on voxel-representations, our point-based method is considerably more
light-weight and scalable, with little loss of quality. It also outperforms
simpler linear factor models such as Probabilistic PCA, both qualitatively and
quantitatively, on a number of categories from the ShapeNet dataset.
Furthermore, our method can easily incorporate other point attributes such as
normal and color information, an additional advantage over voxel-based
representations.
Deformable Registration through Learning of Context-Specific Metric Aggregation
Authors: Enzo Ferrante, Puneet K. Dokania, Rafael Marini, Nikos Paragios
Date: 2017-07-19 00:00:00

We propose a novel weakly supervised discriminative algorithm for learning
context specific registration metrics as a linear combination of conventional
similarity measures. Conventional metrics have been extensively used over the
past two decades and therefore both their strengths and limitations are known.
The challenge is to find the optimal relative weighting (or parameters) of
different metrics forming the similarity measure of the registration algorithm.
Hand-tuning these parameters would result in sub optimal solutions and quickly
become infeasible as the number of metrics increases. Furthermore, such
hand-crafted combination can only happen at global scale (entire volume) and
therefore will not be able to account for the different tissue properties. We
propose a learning algorithm for estimating these parameters locally,
conditioned to the data semantic classes. The objective function of our
formulation is a special case of non-convex function, difference of convex
function, which we optimize using the concave convex procedure. As a proof of
concept, we show the impact of our approach on three challenging datasets for
different anatomical structures and modalities.
Non-Asymptotic Uniform Rates of Consistency for k-NN Regression
Authors: Heinrich Jiang
Date: 2017-07-19 00:00:00
Tasks: regression

We derive high-probability finite-sample uniform rates of consistency for
$k$-NN regression that are optimal up to logarithmic factors under mild
assumptions. We moreover show that $k$-NN regression adapts to an unknown lower
intrinsic dimension automatically. We then apply the $k$-NN regression rates to
establish new results about estimating the level sets and global maxima of a
function from noisy observations.
Learning Approximate Neural Estimators for Wireless Channel State Information
Authors: Timothy J. O'Shea, Kiran Karra, T. Charles Clancy
Date: 2017-07-19 00:00:00

Estimation is a critical component of synchronization in wireless and signal
processing systems. There is a rich body of work on estimator derivation,
optimization, and statistical characterization from analytic system models
which are used pervasively today. We explore an alternative approach to
building estimators which relies principally on approximate regression using
large datasets and large computationally efficient artificial neural network
models capable of learning non-linear function mappings which provide compact
and accurate estimates. For single carrier PSK modulation, we explore the
accuracy and computational complexity of such estimators compared with the
current gold-standard analytically derived alternatives. We compare performance
in various wireless operating conditions and consider the trade offs between
the two different classes of systems. Our results show the learned estimators
can provide improvements in areas such as short-time estimation and estimation
under non-trivial real world channel conditions such as fading or other
non-linear hardware or propagation effects.
Quicksilver: Fast Predictive Image Registration - a Deep Learning Approach
Authors: Xiao Yang, Roland Kwitt, Martin Styner, Marc Niethammer
Date: 2017-03-31 00:00:00
Tasks: Decoder, Deep Learning, Image Registration, Prediction

This paper introduces Quicksilver, a fast deformable image registration
method. Quicksilver registration for image-pairs works by patch-wise prediction
of a deformation model based directly on image appearance. A deep
encoder-decoder network is used as the prediction model. While the prediction
strategy is general, we focus on predictions for the Large Deformation
Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the
momentum-parameterization of LDDMM, which facilitates a patch-wise prediction
strategy while maintaining the theoretical properties of LDDMM, such as
guaranteed diffeomorphic mappings for sufficiently strong regularization. We
also provide a probabilistic version of our prediction network which can be
sampled during the testing time to calculate uncertainties in the predicted
deformations. Finally, we introduce a new correction network which greatly
increases the prediction accuracy of an already existing prediction network. We
show experimental results for uni-modal atlas-to-image as well as uni- / multi-
modal image-to-image registrations. These experiments demonstrate that our
method accurately predicts registrations obtained by numerical optimization, is
very fast, achieves state-of-the-art registration results on four standard
validation datasets, and can jointly learn an image similarity measure.
Quicksilver is freely available as an open-source software.
Acceleration and Averaging in Stochastic Mirror Descent Dynamics
Authors: Walid Krichene, Peter L. Bartlett
Date: 2017-07-19 00:00:00

We formulate and study a general family of (continuous-time) stochastic
dynamics for accelerated first-order minimization of smooth convex functions.
Building on an averaging formulation of accelerated mirror descent, we propose
a stochastic variant in which the gradient is contaminated by noise, and study
the resulting stochastic differential equation. We prove a bound on the rate of
change of an energy function associated with the problem, then use it to derive
estimates of convergence rates of the function values, (a.s. and in
expectation) both for persistent and asymptotically vanishing noise. We discuss
the interaction between the parameters of the dynamics (learning rate and
averaging weights) and the covariation of the noise process, and show, in
particular, how the asymptotic rate of covariation affects the choice of
parameters and, ultimately, the convergence rate.