pwc-archive/papers-with-abstracts huggingface dataset:
Closing the Calibration Loop: An Inside-out-tracking Paradigm for Augmented Reality in Orthopedic Surgery
Authors: Jonas Hajek, Mathias Unberath, Javad Fotouhi, Bastian Bier, Sing Chun Lee, Greg Osgood, Andreas Maier, Mehran Armand, Nassir Navab
Date: 2018-03-22 00:00:00
Tasks: Anatomy, Simultaneous Localization and Mapping

In percutaneous orthopedic interventions the surgeon attempts to reduce and
fixate fractures in bony structures. The complexity of these interventions
arises when the surgeon performs the challenging task of navigating surgical
tools percutaneously only under the guidance of 2D interventional X-ray
imaging. Moreover, the intra-operatively acquired data is only visualized
indirectly on external displays. In this work, we propose a flexible Augmented
Reality (AR) paradigm using optical see-through head mounted displays. The key
technical contribution of this work includes the marker-less and dynamic
tracking concept which closes the calibration loop between patient, C-arm and
the surgeon. This calibration is enabled using Simultaneous Localization and
Mapping of the environment of the operating theater. In return, the proposed
solution provides in situ visualization of pre- and intra-operative 3D medical
data directly at the surgical site. We demonstrate pre-clinical evaluation of a
prototype system, and report errors for calibration and target registration.
Finally, we demonstrate the usefulness of the proposed inside-out tracking
system in achieving "bull's eye" view for C-arm-guided punctures. This AR
solution provides an intuitive visualization of the anatomy and can simplify
the hand-eye coordination for the orthopedic surgeon.
X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery
Authors: Bastian Bier, Mathias Unberath, Jan-Nico Zaech, Javad Fotouhi, Mehran Armand, Greg Osgood, Nassir Navab, Andreas Maier
Date: 2018-03-22 00:00:00
Tasks: Anatomical Landmark Detection, Anatomy, Decision Making, Pose Estimation

X-ray image guidance enables percutaneous alternatives to complex procedures.
Unfortunately, the indirect view onto the anatomy in addition to projective
simplification substantially increase the task-load for the surgeon. Additional
3D information such as knowledge of anatomical landmarks can benefit surgical
decision making in complicated scenarios. Automatic detection of these
landmarks in transmission imaging is challenging since image-domain features
characteristic to a certain landmark change substantially depending on the
viewing direction. Consequently and to the best of our knowledge, the above
problem has not yet been addressed. In this work, we present a method to
automatically detect anatomical landmarks in X-ray images independent of the
viewing direction. To this end, a sequential prediction framework based on
convolutional layers is trained on synthetically generated data of the pelvic
anatomy to predict 23 landmarks in single X-ray images. View independence is
contingent on training conditions and, here, is achieved on a spherical segment
covering (120 x 90) degrees in LAO/RAO and CRAN/CAUD, respectively, centered
around AP. On synthetic data, the proposed approach achieves a mean prediction
error of 5.6 +- 4.5 mm. We demonstrate that the proposed network is immediately
applicable to clinically acquired data of the pelvis. In particular, we show
that our intra-operative landmark detection together with pre-operative CT
enables X-ray pose estimation which, ultimately, benefits initialization of
image-based 2D/3D registration.
A Quantization-Friendly Separable Convolution for MobileNets
Authors: Tao Sheng, Chen Feng, Shaojie Zhuo, Xiaopeng Zhang, Liang Shen, Mickey Aleksic
Date: 2018-03-22 00:00:00
Tasks: Edge-computing, GPU, image-classification, Image Classification, Network Pruning, Quantization

As deep learning (DL) is being rapidly pushed to edge computing, researchers
invented various ways to make inference computation more efficient on
mobile/IoT devices, such as network pruning, parameter compression, and etc.
Quantization, as one of the key approaches, can effectively offload GPU, and
make it possible to deploy DL on fixed-point pipeline. Unfortunately, not all
existing networks design are friendly to quantization. For example, the popular
lightweight MobileNetV1, while it successfully reduces parameter size and
computation latency with separable convolution, our experiment shows its
quantized models have large accuracy gap against its float point models. To
resolve this, we analyzed the root cause of quantization loss and proposed a
quantization-friendly separable convolution architecture. By evaluating the
image classification task on ImageNet2012 dataset, our modified MobileNetV1
model can archive 8-bit inference top-1 accuracy in 68.03%, almost closed the
gap to the float pipeline.
DeepDRR -- A Catalyst for Machine Learning in Fluoroscopy-guided Procedures
Authors: Mathias Unberath, Jan-Nico Zaech, Sing Chun Lee, Bastian Bier, Javad Fotouhi, Mehran Armand, Nassir Navab
Date: 2018-03-22 00:00:00
Tasks: Anatomical Landmark Detection, BIG-bench Machine Learning, Diagnostic, Domain Adaptation

Machine learning-based approaches outperform competing methods in most
disciplines relevant to diagnostic radiology. Interventional radiology,
however, has not yet benefited substantially from the advent of deep learning,
in particular because of two reasons: 1) Most images acquired during the
procedure are never archived and are thus not available for learning, and 2)
even if they were available, annotations would be a severe challenge due to the
vast amounts of data. When considering fluoroscopy-guided procedures, an
interesting alternative to true interventional fluoroscopy is in silico
simulation of the procedure from 3D diagnostic CT. In this case, labeling is
comparably easy and potentially readily available, yet, the appropriateness of
resulting synthetic data is dependent on the forward model. In this work, we
propose DeepDRR, a framework for fast and realistic simulation of fluoroscopy
and digital radiography from CT scans, tightly integrated with the software
platforms native to deep learning. We use machine learning for material
decomposition and scatter estimation in 3D and 2D, respectively, combined with
analytic forward projection and noise injection to achieve the required
performance. On the example of anatomical landmark detection in X-ray images of
the pelvis, we demonstrate that machine learning models trained on DeepDRRs
generalize to unseen clinically acquired data without the need for re-training
or domain adaptation. Our results are promising and promote the establishment
of machine learning in fluoroscopy-guided procedures.
Selective Video Object Cutout
Authors: Wenguan Wang, Jianbing Shen, Fatih Porikli
Date: 2017-02-28 00:00:00
Tasks: Computational Efficiency, Object, Segmentation, Video Segmentation, Video Semantic Segmentation

Conventional video segmentation approaches rely heavily on appearance models.
Such methods often use appearance descriptors that have limited discriminative
power under complex scenarios. To improve the segmentation performance, this
paper presents a pyramid histogram based confidence map that incorporates
structure information into appearance statistics. It also combines geodesic
distance based dynamic models. Then, it employs an efficient measure of
uncertainty propagation using local classifiers to determine the image regions
where the object labels might be ambiguous. The final foreground cutout is
obtained by refining on the uncertain regions. Additionally, to reduce manual
labeling, our method determines the frames to be labeled by the human operator
in a principled manner, which further boosts the segmentation performance and
minimizes the labeling effort. Our extensive experimental analyses on two big
benchmarks demonstrate that our solution achieves superior performance,
favorable computational efficiency, and reduced manual labeling in comparison
to the state-of-the-art.
Deep Visual Attention Prediction
Authors: Wenguan Wang, Jianbing Shen
Date: 2017-05-07 00:00:00
Proceeding: journal 2017 11
Tasks: Prediction, Saliency Prediction

In this work, we aim to predict human eye fixation with view-free scenes
based on an end-to-end deep learning architecture. Although Convolutional
Neural Networks (CNNs) have made substantial improvement on human attention
prediction, it is still needed to improve CNN based attention models by
efficiently leveraging multi-scale features. Our visual attention network is
proposed to capture hierarchical saliency information from deep, coarse layers
with global saliency information to shallow, fine layers with local saliency
response. Our model is based on a skip-layer network structure, which predicts
human attention from multiple convolutional layers with various reception
fields. Final saliency prediction is achieved via the cooperation of those
global and local predictions. Our model is learned in a deep supervision
manner, where supervision is directly fed into multi-level layers, instead of
previous approaches of providing supervision only at the output layer and
propagating this supervision back to earlier layers. Our model thus
incorporates multi-level saliency predictions within a single network, which
significantly decreases the redundancy of previous approaches of learning
multiple network streams with different input scales. Extensive experimental
analysis on various challenging benchmark datasets demonstrate our method
yields state-of-the-art performance with competitive inference time.
Prototypicality effects in global semantic description of objects
Authors: Omar Vidal Pino, Erickson Rangel Nascimento, Mario Fernando Montenegro Campos
Date: 2018-01-12 00:00:00
Tasks: General Classification, Object

In this paper, we introduce a novel approach for semantic description of
object features based on the prototypicality effects of the Prototype Theory.
Our prototype-based description model encodes and stores the semantic meaning
of an object, while describing its features using the semantic prototype
computed by CNN-classifications models. Our method uses semantic prototypes to
create discriminative descriptor signatures that describe an object
highlighting its most distinctive features within the category. Our experiments
show that: i) our descriptor preserves the semantic information used by the
CNN-models in classification tasks; ii) our distance metric can be used as the
object's typicality score; iii) our descriptor signatures are semantically
interpretable and enables the simulation of the prototypical organization of
objects within a category.
Field-Programmable Deep Neural Network (DNN) Learning and Inference accelerator: a concept
Authors: Luiz M Franca-Neto
Date: 2018-02-14 00:00:00
Tasks: CPU, GPU

An accelerator is a specialized integrated circuit designed to perform
specific computations faster than if those were performed by CPU or GPU. A
Field-Programmable DNN learning and inference accelerator (FProg-DNN) using
hybrid systolic and non-systolic techniques, distributed information-control
and deep pipelined structure is proposed and its microarchitecture and
operation presented here. Reconfigurability attends diverse DNN designs and
allows for different number of workers to be assigned to different layers as a
function of the relative difference in computational load among layers. The
computational delay per layer is made roughly the same along pipelined
accelerator structure. VGG-16 and recently proposed Inception Modules are used
for showing the flexibility of the FProg-DNN reconfigurability. Special
structures were also added for a combination of convolution layer, map
coincidence and feedback for state of the art learning with small set of
examples, which is the focus of a companion paper by the author (Franca-Neto,
2018). The accelerator described is able to reconfigure from (1) allocating all
a DNN computations to a single worker in one extreme of sub-optimal performance
to (2) optimally allocating workers per layer according to computational load
in each DNN layer to be realized. Due the pipelined architecture, more than 50x
speedup is achieved relative to GPUs or TPUs. This speed-up is consequence of
hiding the delay in transporting activation outputs from one layer to the next
in a DNN behind the computations in the receiving layer. This FProg-DNN concept
has been simulated and validated at behavioral-functional level.
Learning State Representations for Query Optimization with Deep Reinforcement Learning
Authors: Jennifer Ortiz, Magdalena Balazinska, Johannes Gehrke, S. Sathiya Keerthi
Date: 2018-03-22 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning is quickly changing the field of artificial
intelligence. These models are able to capture a high level understanding of
their environment, enabling them to learn difficult dynamic tasks in a variety
of domains. In the database field, query optimization remains a difficult
problem. Our goal in this work is to explore the capabilities of deep
reinforcement learning in the context of query optimization. At each state, we
build queries incrementally and encode properties of subqueries through a
learned representation. The challenge here lies in the formation of the state
transition function, which defines how the current subquery state combines with
the next query operation (action) to yield the next state. As a first step in
this direction, we focus the state representation problem and the formation of
the state transition function. We describe our approach and show preliminary
results. We further discuss how we can use the state representation to improve
query optimization using reinforcement learning.
Maximum Consensus Parameter Estimation by Reweighted $\ell_1$ Methods
Authors: Pulak Purkait, Christopher Zach, Anders Eriksson
Date: 2018-03-22 00:00:00
Tasks: parameter estimation

Robust parameter estimation in computer vision is frequently accomplished by
solving the maximum consensus (MaxCon) problem. Widely used randomized methods
for MaxCon, however, can only produce {random} approximate solutions, while
global methods are too slow to exercise on realistic problem sizes. Here we
analyse MaxCon as iterative reweighted algorithms on the data residuals. We
propose a smooth surrogate function, the minimization of which leads to an
extremely simple iteratively reweighted algorithm for MaxCon. We show that our
algorithm is very efficient and in many cases, yields the global solution. This
makes it an attractive alternative for randomized methods and global
optimizers. The convergence analysis of our method and its fundamental
differences from the other iteratively reweighted methods are also presented.
Lower error bounds for the stochastic gradient descent optimization algorithm: Sharp convergence rates for slowly and fast decaying learning rates
Authors: Arnulf Jentzen, Philippe von Wurstemberger
Date: 2018-03-22 00:00:00
Tasks: Stochastic Optimization

The stochastic gradient descent (SGD) optimization algorithm plays a central
role in a series of machine learning applications. The scientific literature
provides a vast amount of upper error bounds for the SGD method. Much less
attention as been paid to proving lower error bounds for the SGD method. It is
the key contribution of this paper to make a step in this direction. More
precisely, in this article we establish for every $\gamma, \nu \in (0,\infty)$
essentially matching lower and upper bounds for the mean square error of the
SGD process with learning rates $(\frac{\gamma}{n^\nu})_{n \in \mathbb{N}}$
associated to a simple quadratic stochastic optimization problem. This allows
us to precisely quantify the mean square convergence rate of the SGD method in
dependence on the asymptotic behavior of the learning rates.
Pairwise Confusion for Fine-Grained Visual Classification
Authors: Abhimanyu Dubey, Otkrist Gupta, Pei Guo, Ramesh Raskar, Ryan Farrell, Nikhil Naik
Date: 2017-05-22 00:00:00
Proceeding: ECCV 2018 9
Tasks: Classification, Fine-Grained Image Classification, General Classification

Fine-Grained Visual Classification (FGVC) datasets contain small sample
sizes, along with significant intra-class variation and inter-class similarity.
While prior work has addressed intra-class variation using localization and
segmentation techniques, inter-class similarity may also affect feature
learning and reduce classification performance. In this work, we address this
problem using a novel optimization procedure for the end-to-end neural network
training on FGVC tasks. Our procedure, called Pairwise Confusion (PC) reduces
overfitting by intentionally {introducing confusion} in the activations. With
PC regularization, we obtain state-of-the-art performance on six of the most
widely-used FGVC datasets and demonstrate improved localization ability. {PC}
is easy to implement, does not need excessive hyperparameter tuning during
training, and does not add significant overhead during test time.
A Large Self-Annotated Corpus for Sarcasm
Authors: Mikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli
Date: 2017-04-19 00:00:00
Proceeding: LREC 2018 5
Tasks: Sarcasm Detection

We introduce the Self-Annotated Reddit Corpus (SARC), a large corpus for
sarcasm research and for training and evaluating systems for sarcasm detection.
The corpus has 1.3 million sarcastic statements -- 10 times more than any
previous dataset -- and many times more instances of non-sarcastic statements,
allowing for learning in both balanced and unbalanced label regimes. Each
statement is furthermore self-annotated -- sarcasm is labeled by the author,
not an independent annotator -- and provided with user, topic, and conversation
context. We evaluate the corpus for accuracy, construct benchmarks for sarcasm
detection, and evaluate baseline methods.
Deep contextualized word representations
Authors: Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, Luke Zettlemoyer
Date: 2018-02-15 00:00:00
Proceeding: NAACL 2018 6
Tasks: Citation Intent Classification, Conversational Response Selection, Coreference Resolution, Language Modeling, Language Modelling, Named Entity Recognition (NER), Natural Language Inference, Only Connect Walls Dataset Task 1 (Grouping), Question Answering, Semantic Role Labeling, Sentiment Analysis

We introduce a new type of deep contextualized word representation that
models both (1) complex characteristics of word use (e.g., syntax and
semantics), and (2) how these uses vary across linguistic contexts (i.e., to
model polysemy). Our word vectors are learned functions of the internal states
of a deep bidirectional language model (biLM), which is pre-trained on a large
text corpus. We show that these representations can be easily added to existing
models and significantly improve the state of the art across six challenging
NLP problems, including question answering, textual entailment and sentiment
analysis. We also present an analysis showing that exposing the deep internals
of the pre-trained network is crucial, allowing downstream models to mix
different types of semi-supervision signals.
Improving Network Robustness against Adversarial Attacks with Compact Convolution
Authors: Rajeev Ranjan, Swami Sankaranarayanan, Carlos D. Castillo, Rama Chellappa
Date: 2017-12-03 00:00:00
Tasks: Face Verification

Though Convolutional Neural Networks (CNNs) have surpassed human-level
performance on tasks such as object classification and face verification, they
can easily be fooled by adversarial attacks. These attacks add a small
perturbation to the input image that causes the network to misclassify the
sample. In this paper, we focus on neutralizing adversarial attacks by compact
feature learning. In particular, we show that learning features in a closed and
bounded space improves the robustness of the network. We explore the effect of
L2-Softmax Loss, that enforces compactness in the learned features, thus
resulting in enhanced robustness to adversarial perturbations. Additionally, we
propose compact convolution, a novel method of convolution that when
incorporated in conventional CNNs improves their robustness. Compact
convolution ensures feature compactness at every layer such that they are
bounded and close to each other. Extensive experiments show that Compact
Convolutional Networks (CCNs) neutralize multiple types of attacks, and perform
better than existing methods in defending adversarial attacks, without
incurring any additional training overhead compared to CNNs.
Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates
Authors: Yining Wang, Sivaraman Balakrishnan, Aarti Singh
Date: 2018-03-22 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: global-optimization

We consider the problem of global optimization of an unknown non-convex
smooth function with zeroth-order feedback. In this setup, an algorithm is
allowed to adaptively query the underlying function at different locations and
receives noisy evaluations of function values at the queried points (i.e. the
algorithm has access to zeroth-order information). Optimization performance is
evaluated by the expected difference of function values at the estimated
optimum and the true optimum. In contrast to the classical optimization setup,
first-order information like gradients are not directly accessible to the
optimization algorithm. We show that the classical minimax framework of
analysis, which roughly characterizes the worst-case query complexity of an
optimization algorithm in this setting, leads to excessively pessimistic
results. We propose a local minimax framework to study the fundamental
difficulty of optimizing smooth functions with adaptive function evaluations,
which provides a refined picture of the intrinsic difficulty of zeroth-order
optimization. We show that for functions with fast level set growth around the
global minimum, carefully designed optimization algorithms can identify a near
global minimizer with many fewer queries. For the special case of strongly
convex and smooth functions, our implied convergence rates match the ones
developed for zeroth-order convex optimization problems. At the other end of
the spectrum, for worst-case smooth functions no algorithm can converge faster
than the minimax rate of estimating the entire unknown function in the
$\ell_\infty$-norm. We provide an intuitive and efficient algorithm that
attains the derived upper error bounds.
Curvature of Hypergraphs via Multi-Marginal Optimal Transport
Authors: Shahab Asoodeh, Tingran Gao, James Evans
Date: 2018-03-22 00:00:00

We introduce a novel definition of curvature for hypergraphs, a natural
generalization of graphs, by introducing a multi-marginal optimal transport
problem for a naturally defined random walk on the hypergraph. This curvature,
termed \emph{coarse scalar curvature}, generalizes a recent definition of Ricci
curvature for Markov chains on metric spaces by Ollivier [Journal of Functional
Analysis 256 (2009) 810-864], and is related to the scalar curvature when the
hypergraph arises naturally from a Riemannian manifold. We investigate basic
properties of the coarse scalar curvature and obtain several bounds. Empirical
experiments indicate that coarse scalar curvatures are capable of detecting
"bridges" across connected components in hypergraphs, suggesting it is an
appropriate generalization of curvature on simple graphs.
Unbiased scalable softmax optimization
Authors: Francois Fagan, Garud Iyengar
Date: 2018-03-22 00:00:00
Proceeding: ICLR 2018 1

Recent neural network and language models rely on softmax distributions with
an extremely large number of categories. Since calculating the softmax
normalizing constant in this context is prohibitively expensive, there is a
growing literature of efficiently computable but biased estimates of the
softmax. In this paper we propose the first unbiased algorithms for maximizing
the softmax likelihood whose work per iteration is independent of the number of
classes and datapoints (and no extra work is required at the end of each
epoch). We show that our proposed unbiased methods comprehensively outperform
the state-of-the-art on seven real world datasets.