pwc-archive/papers-with-abstracts huggingface dataset:
Meta-Learning Priors for Efficient Online Bayesian Regression
Authors: James Harrison, Apoorva Sharma, Marco Pavone
Date: 2018-07-24 00:00:00
Tasks: Meta-Learning, regression

Gaussian Process (GP) regression has seen widespread use in robotics due to
its generality, simplicity of use, and the utility of Bayesian predictions. The
predominant implementation of GP regression is a nonparameteric kernel-based
approach, as it enables fitting of arbitrary nonlinear functions. However, this
approach suffers from two main drawbacks: (1) it is computationally
inefficient, as computation scales poorly with the number of samples; and (2)
it can be data inefficient, as encoding prior knowledge that can aid the model
through the choice of kernel and associated hyperparameters is often
challenging and unintuitive. In this work, we propose ALPaCA, an algorithm for
efficient Bayesian regression which addresses these issues. ALPaCA uses a
dataset of sample functions to learn a domain-specific, finite-dimensional
feature encoding, as well as a prior over the associated weights, such that
Bayesian linear regression in this feature space yields accurate online
predictions of the posterior predictive density. These features are neural
networks, which are trained via a meta-learning (or "learning-to-learn")
approach. ALPaCA extracts all prior information directly from the dataset,
rather than restricting prior information to the choice of kernel
hyperparameters. Furthermore, by operating in the weight space, it
substantially reduces sample complexity. We investigate the performance of
ALPaCA on two simple regression problems, two simulated robotic systems, and on
a lane-change driving task performed by humans. We find our approach
outperforms kernel-based GP regression, as well as state of the art
meta-learning approaches, thereby providing a promising plug-in tool for many
regression tasks in robotics where scalability and data-efficiency are
important.
A Structured Perspective of Volumes on Active Learning
Authors: Xiaofeng Cao, Ivor W. Tsang, Guandong Xu
Date: 2018-07-24 00:00:00
Tasks: Active Learning

Active Learning (AL) is a learning task that requires learners interactively query the labels of the sampled unlabeled instances to minimize the training outputs with human supervisions. In theoretical study, learners approximate the version space which covers all possible classification hypothesis into a bounded convex body and try to shrink the volume of it into a half-space by a given cut size. However, only the hypersphere with finite VC dimensions has obtained formal approximation guarantees that hold when the classes of Euclidean space are separable with a margin. In this paper, we approximate the version space to a structured {hypersphere} that covers most of the hypotheses, and then divide the available AL sampling approaches into two kinds of strategies: Outer Volume Sampling and Inner Volume Sampling. After providing provable guarantees for the performance of AL in version space, we aggregate the two kinds of volumes to eliminate their sampling biases via finding the optimal inscribed hyperspheres in the enclosing space of outer volume. To touch the version space from Euclidean space, we propose a theoretical bridge called Volume-based Model that increases the `sampling target-independent'. In non-linear feature space, spanned by kernel, we use sequential optimization to globally optimize the original space to a sparse space by halving the size of the kernel space. Then, the EM (Expectation Maximization) model which returns the local center helps us to find a local representation. To describe this process, we propose an easy-to-implement algorithm called Volume-based AL (VAL).
Self-produced Guidance for Weakly-supervised Object Localization
Authors: Xiaolin Zhang, Yunchao Wei, Guoliang Kang, Yi Yang, Thomas Huang
Date: 2018-07-24 00:00:00
Proceeding: ECCV 2018 9
Tasks: Classification, General Classification, Object, Object Localization, Weakly-Supervised Object Localization

Weakly supervised methods usually generate localization results based on
attention maps produced by classification networks. However, the attention maps
exhibit the most discriminative parts of the object which are small and sparse.
We propose to generate Self-produced Guidance (SPG) masks which separate the
foreground, the object of interest, from the background to provide the
classification networks with spatial correlation information of pixels. A
stagewise approach is proposed to incorporate high confident object regions to
learn the SPG masks. The high confident regions within attention maps are
utilized to progressively learn the SPG masks. The masks are then used as an
auxiliary pixel-level supervision to facilitate the training of classification
networks. Extensive experiments on ILSVRC demonstrate that SPG is effective in
producing high-quality object localizations maps. Particularly, the proposed
SPG achieves the Top-1 localization error rate of 43.83% on the ILSVRC
validation set, which is a new state-of-the-art error rate.
State-space analysis of an Ising model reveals contributions of pairwise interactions to sparseness, fluctuation, and stimulus coding of monkey V1 neurons
Authors: Jimmy Gaudreault, Hideaki Shimazaki
Date: 2018-07-24 00:00:00

In this study, we analyzed the activity of monkey V1 neurons responding to
grating stimuli of different orientations using inference methods for a
time-dependent Ising model. The method provides optimal estimation of
time-dependent neural interactions with credible intervals according to the
sequential Bayes estimation algorithm. Furthermore, it allows us to trace
dynamics of macroscopic network properties such as entropy, sparseness, and
fluctuation. Here we report that, in all examined stimulus conditions, pairwise
interactions contribute to increasing sparseness and fluctuation. We then
demonstrate that the orientation of the grating stimulus is in part encoded in
the pairwise interactions of the neural populations. These results demonstrate
the utility of the state-space Ising model in assessing contributions of neural
interactions during stimulus processing.
ClusterNet: 3D Instance Segmentation in RGB-D Images
Authors: Lin Shao, Ye Tian, Jeannette Bohg
Date: 2018-07-24 00:00:00
Tasks: 3D Instance Segmentation, Clustering, Decision Making, Instance Segmentation, Object, Segmentation, Semantic Segmentation

We propose a method for instance-level segmentation that uses RGB-D data as
input and provides detailed information about the location, geometry and number
of individual objects in the scene. This level of understanding is fundamental
for autonomous robots. It enables safe and robust decision-making under the
large uncertainty of the real-world. In our model, we propose to use the first
and second order moments of the object occupancy function to represent an
object instance. We train an hourglass Deep Neural Network (DNN) where each
pixel in the output votes for the 3D position of the corresponding object
center and for the object's size and pose. The final instance segmentation is
achieved through clustering in the space of moments. The object-centric
training loss is defined on the output of the clustering. Our method
outperforms the state-of-the-art instance segmentation method on our
synthesized dataset. We show that our method generalizes well on real-world
data achieving visually better segmentation results.
Skin Lesion Segmentation Using Atrous Convolution via DeepLab v3
Authors: Yujie Wang, Simon Sun, Jahow Yu, Dr. Limin Yu
Date: 2018-07-24 00:00:00
Tasks: Image Segmentation, Lesion Segmentation, Segmentation, Semantic Segmentation, Skin Lesion Segmentation

As melanoma diagnoses increase across the US, automated efforts to identify
malignant lesions become increasingly of interest to the research community.
Segmentation of dermoscopic images is the first step in this process, thus
accuracy is crucial. Although techniques utilizing convolutional neural
networks have been used in the past for lesion segmentation, we present a
solution employing the recently published DeepLab 3, an atrous convolution
method for image segmentation. Although the results produced by this run are
not ideal, with a mean Jaccard index of 0.498, we believe that with further
adjustments and modifications to the compatibility with the DeepLab code and
with training on more powerful processing units, this method may achieve better
results in future trials.
Supporting Very Large Models using Automatic Dataflow Graph Partitioning
Authors: Minjie Wang, Chien-chin Huang, Jinyang Li
Date: 2018-07-24 00:00:00
Tasks: GPU, graph partitioning

This paper presents Tofu, a system that partitions very large DNN models
across multiple GPU devices to reduce per-GPU memory footprint. Tofu is
designed to partition a dataflow graph of fine-grained tensor operators in
order to work transparently with a general-purpose deep learning platform like
MXNet. In order to automatically partition each operator, we propose to
describe the semantics of an operator in a simple language which represents
tensors as lambda functions mapping from tensor coordinates to values. To
optimally partition different operators in a dataflow graph, Tofu uses a
recursive search algorithm that minimizes the total communication cost. Our
experiments on an 8-GPU machine show that Tofu enables the training of very
large CNN and RNN models. It also achieves 25% - 400% speedup over alternative
approaches to train very large models.
Top-Down Feedback for Crowd Counting Convolutional Neural Network
Authors: Deepak Babu Sam, R. Venkatesh Babu
Date: 2018-07-24 00:00:00
Tasks: Crowd Counting

Counting people in dense crowds is a demanding task even for humans. This is
primarily due to the large variability in appearance of people. Often people
are only seen as a bunch of blobs. Occlusions, pose variations and background
clutter further compound the difficulty. In this scenario, identifying a person
requires larger spatial context and semantics of the scene. But the current
state-of-the-art CNN regressors for crowd counting are feedforward and use only
limited spatial context to detect people. They look for local crowd patterns to
regress the crowd density map, resulting in false predictions. Hence, we
propose top-down feedback to correct the initial prediction of the CNN. Our
architecture consists of a bottom-up CNN along with a separate top-down CNN to
generate feedback. The bottom-up network, which regresses the crowd density
map, has two columns of CNN with different receptive fields. Features from
various layers of the bottom-up CNN are fed to the top-down network. The
feedback, thus generated, is applied on the lower layers of the bottom-up
network in the form of multiplicative gating. This masking weighs activations
of the bottom-up network at spatial as well as feature levels to correct the
density prediction. We evaluate the performance of our model on all major crowd
datasets and show the effectiveness of top-down feedback.
StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction
Authors: Sameh Khamis, Sean Fanello, Christoph Rhemann, Adarsh Kowdle, Julien Valentin, Shahram Izadi
Date: 2018-07-24 00:00:00
Proceeding: ECCV 2018 9
Tasks: Depth Estimation, Depth Prediction, Quantization, Stereo Depth Estimation, Stereo Matching, Stereo Matching Hand

This paper presents StereoNet, the first end-to-end deep architecture for
real-time stereo matching that runs at 60 fps on an NVidia Titan X, producing
high-quality, edge-preserved, quantization-free disparity maps. A key insight
of this paper is that the network achieves a sub-pixel matching precision than
is a magnitude higher than those of traditional stereo matching approaches.
This allows us to achieve real-time performance by using a very low resolution
cost volume that encodes all the information needed to achieve high disparity
precision. Spatial precision is achieved by employing a learned edge-aware
upsampling function. Our model uses a Siamese network to extract features from
the left and right image. A first estimate of the disparity is computed in a
very low resolution cost volume, then hierarchically the model re-introduces
high-frequency details through a learned upsampling function that uses compact
pixel-to-pixel refinement networks. Leveraging color input as a guide, this
function is capable of producing high-quality edge-aware output. We achieve
compelling results on multiple benchmarks, showing how the proposed method
offers extreme flexibility at an acceptable computational budget.
Toward a language-theoretic foundation for planning and filtering
Authors: Fatemeh Zahra Saberifar, Shervin Ghasemlou, Dylan A. Shell, Jason M. O'Kane
Date: 2018-07-23 00:00:00

We address problems underlying the algorithmic question of automating the
co-design of robot hardware in tandem with its apposite software. Specifically,
we consider the impact that degradations of a robot's sensor and actuation
suites may have on the ability of that robot to complete its tasks. We
introduce a new formal structure that generalizes and consolidates a variety of
well-known structures including many forms of plans, planning problems, and
filters, into a single data structure called a procrustean graph, and give
these graph structures semantics in terms of ideas based in formal language
theory. We describe a collection of operations on procrustean graphs (both
semantics-preserving and semantics-mutating), and show how a family of
questions about the destructiveness of a change to the robot hardware can be
answered by applying these operations. We also highlight the connections
between this new approach and existing threads of research, including
combinatorial filtering, Erdmann's strategy complexes, and hybrid automata.
Weak in the NEES?: Auto-tuning Kalman Filters with Bayesian Optimization
Authors: Zhaozhong Chen, Christoffer Heckman, Simon Julier, Nisar Ahmed
Date: 2018-07-23 00:00:00
Tasks: Bayesian Optimization, Simultaneous Localization and Mapping, Uncertainty Quantification

Kalman filters are routinely used for many data fusion applications including
navigation, tracking, and simultaneous localization and mapping problems.
However, significant time and effort is frequently required to tune various
Kalman filter model parameters, e.g. process noise covariance, pre-whitening
filter models for non-white noise, etc. Conventional optimization techniques
for tuning can get stuck in poor local minima and can be expensive to implement
with real sensor data. To address these issues, a new "black box" Bayesian
optimization strategy is developed for automatically tuning Kalman filters. In
this approach, performance is characterized by one of two stochastic objective
functions: normalized estimation error squared (NEES) when ground truth state
models are available, or the normalized innovation error squared (NIS) when
only sensor data is available. By intelligently sampling the parameter space to
both learn and exploit a nonparametric Gaussian process surrogate function for
the NEES/NIS costs, Bayesian optimization can efficiently identify multiple
local minima and provide uncertainty quantification on its results.
Lesion segmentation using U-Net network
Authors: Adrien Motsch, Sebastien Motsch, Thibaut Saguet
Date: 2018-07-23 00:00:00
Tasks: Lesion Segmentation, Segmentation

This paper explains the method used in the segmentation challenge (Task 1) in
the International Skin Imaging Collaboration's (ISIC) Skin Lesion Analysis
Towards Melanoma Detection challenge held in 2018. We have trained a U-Net
network to perform the segmentation. The key elements for the training were
first to adjust the loss function to incorporate unbalanced proportion of
background and second to perform post-processing operation to adjust the
contour of the prediction.
Hierarchical Classification using Binary Data
Authors: Denali Molitor, Deanna Needell
Date: 2018-07-23 00:00:00
Tasks: Classification, General Classification

In classification problems, especially those that categorize data into a
large number of classes, the classes often naturally follow a hierarchical
structure. That is, some classes are likely to share similar structures and
features. Those characteristics can be captured by considering a hierarchical
relationship among the class labels. Here, we extend a recent simple
classification approach on binary data in order to efficiently classify
hierarchical data. In certain settings, specifically, when some classes are
significantly easier to identify than others, we showcase computational and
accuracy advantages.
RAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data
Authors: Yanbo Xu, Siddharth Biswal, Shriprasad R Deshpande, Kevin O Maher, Jimeng Sun
Date: 2018-07-23 00:00:00
Tasks: Decompensation

With the improvement of medical data capturing, vast amount of continuous
patient monitoring data, e.g., electrocardiogram (ECG), real-time vital signs
and medications, become available for clinical decision support at intensive
care units (ICUs). However, it becomes increasingly challenging to model such
data, due to high density of the monitoring data, heterogeneous data types and
the requirement for interpretable models. Integration of these high-density
monitoring data with the discrete clinical events (including diagnosis,
medications, labs) is challenging but potentially rewarding since richness and
granularity in such multimodal data increase the possibilities for accurate
detection of complex problems and predicting outcomes (e.g., length of stay and
mortality). We propose Recurrent Attentive and Intensive Model (RAIM) for
jointly analyzing continuous monitoring data and discrete clinical events. RAIM
introduces an efficient attention mechanism for continuous monitoring data
(e.g., ECG), which is guided by discrete clinical events (e.g, medication
usage). We apply RAIM in predicting physiological decompensation and length of
stay in those critically ill patients at ICU. With evaluations on MIMIC- III
Waveform Database Matched Subset, we obtain an AUC-ROC score of 90.18% for
predicting decompensation and an accuracy of 86.82% for forecasting length of
stay with our final model, which outperforms our six baseline models.
PCNNA: A Photonic Convolutional Neural Network Accelerator
Authors: Armin Mehrabian, Yousra Al-Kabani, Volker J. Sorger, Tarek El-Ghazawi
Date: 2018-07-23 00:00:00

Convolutional Neural Networks (CNN) have been the centerpiece of many
applications including but not limited to computer vision, speech processing,
and Natural Language Processing (NLP). However, the computationally expensive
convolution operations impose many challenges to the performance and
scalability of CNNs. In parallel, photonic systems, which are traditionally
employed for data communication, have enjoyed recent popularity for data
processing due to their high bandwidth, low power consumption, and
reconfigurability. Here we propose a Photonic Convolutional Neural Network
Accelerator (PCNNA) as a proof of concept design to speedup the convolution
operation for CNNs. Our design is based on the recently introduced silicon
photonic microring weight banks, which use broadcast-and-weight protocol to
perform Multiply And Accumulate (MAC) operation and move data through layers of
a neural network. Here, we aim to exploit the synergy between the inherent
parallelism of photonics in the form of Wavelength Division Multiplexing (WDM)
and sparsity of connections between input feature maps and kernels in CNNs.
While our full system design offers up to more than 3 orders of magnitude
speedup in execution time, its optical core potentially offers more than 5
order of magnitude speedup compared to state-of-the-art electronic
counterparts.
Fast Vessel Segmentation and Tracking in Ultra High-Frequency Ultrasound Images
Authors: Tejas Sudharshan Mathai, Lingbo Jin, Vijay Gorantla, John Galeotti
Date: 2018-07-23 00:00:00
Tasks: GPU, Vocal Bursts Intensity Prediction

Ultra High Frequency Ultrasound (UHFUS) enables the visualization of highly
deformable small and medium vessels in the hand. Intricate vessel-based
measurements, such as intimal wall thickness and vessel wall compliance,
require sub-millimeter vessel tracking between B-scans. Our fast GPU-based
approach combines the advantages of local phase analysis, a
distance-regularized level set, and an Extended Kalman Filter (EKF), to rapidly
segment and track the deforming vessel contour. We validated on 35 UHFUS
sequences of vessels in the hand, and we show the transferability of the
approach to 5 more diverse datasets acquired by a traditional High Frequency
Ultrasound (HFUS) machine. To the best of our knowledge, this is the first
algorithm capable of rapidly segmenting and tracking deformable vessel contours
in 2D UHFUS images. It is also the fastest and most accurate system for 2D HFUS
images.
Peeking Behind Objects: Layered Depth Prediction from a Single Image
Authors: Helisa Dhamo, Keisuke Tateno, Iro Laina, Nassir Navab, Federico Tombari
Date: 2018-07-23 00:00:00
Tasks: Depth Estimation, Depth Prediction

While conventional depth estimation can infer the geometry of a scene from a
single RGB image, it fails to estimate scene regions that are occluded by
foreground objects. This limits the use of depth prediction in augmented and
virtual reality applications, that aim at scene exploration by synthesizing the
scene from a different vantage point, or at diminished reality. To address this
issue, we shift the focus from conventional depth map prediction to the
regression of a specific data representation called Layered Depth Image (LDI),
which contains information about the occluded regions in the reference frame
and can fill in occlusion gaps in case of small view changes. We propose a
novel approach based on Convolutional Neural Networks (CNNs) to jointly predict
depth maps and foreground separation masks used to condition Generative
Adversarial Networks (GANs) for hallucinating plausible color and depths in the
initially occluded areas. We demonstrate the effectiveness of our approach for
novel scene view synthesis from a single image.
Identity Preserving Face Completion for Large Ocular Region Occlusion
Authors: Yajie Zhao, Weikai Chen, Jun Xing, Xiaoming Li, Zach Bessinger, Fuchang Liu, WangMeng Zuo, Ruigang Yang
Date: 2018-07-23 00:00:00
Tasks: Facial Inpainting

We present a novel deep learning approach to synthesize complete face images
in the presence of large ocular region occlusions. This is motivated by recent
surge of VR/AR displays that hinder face-to-face communications. Different from
the state-of-the-art face inpainting methods that have no control over the
synthesized content and can only handle frontal face pose, our approach can
faithfully recover the missing content under various head poses while
preserving the identity. At the core of our method is a novel generative
network with dedicated constraints to regularize the synthesis process. To
preserve the identity, our network takes an arbitrary occlusion-free image of
the target identity to infer the missing content, and its high-level CNN
features as an identity prior to regularize the searching space of generator.
Since the input reference image may have a different pose, a pose map and a
novel pose discriminator are further adopted to supervise the learning of
implicit pose transformations. Our method is capable of generating coherent
facial inpainting with consistent identity over videos with large variations of
head motions. Experiments on both synthesized and real data demonstrate that
our method greatly outperforms the state-of-the-art methods in terms of both
synthesis quality and robustness.