pwc-archive/papers-with-abstracts huggingface dataset:
Mitigating Adversarial Effects Through Randomization
Authors: Cihang Xie, Jian-Yu Wang, Zhishuai Zhang, Zhou Ren, Alan Yuille
Date: 2017-11-06 00:00:00
Proceeding: ICLR 2018 1
Tasks: Adversarial Defense, Image Classification

Convolutional neural networks have demonstrated high accuracy on various
tasks in recent years. However, they are extremely vulnerable to adversarial
examples. For example, imperceptible perturbations added to clean images can
cause convolutional neural networks to fail. In this paper, we propose to
utilize randomization at inference time to mitigate adversarial effects.
Specifically, we use two randomization operations: random resizing, which
resizes the input images to a random size, and random padding, which pads zeros
around the input images in a random manner. Extensive experiments demonstrate
that the proposed randomization method is very effective at defending against
both single-step and iterative attacks. Our method provides the following
advantages: 1) no additional training or fine-tuning, 2) very few additional
computations, 3) compatible with other adversarial defense methods. By
combining the proposed randomization method with an adversarially trained
model, it achieves a normalized score of 0.924 (ranked No.2 among 107 defense
teams) in the NIPS 2017 adversarial examples defense challenge, which is far
better than using adversarial training alone with a normalized score of 0.773
(ranked No.56). The code is public available at
https://github.com/cihangxie/NIPS2017_adv_challenge_defense.
HBST: A Hamming Distance embedding Binary Search Tree for Visual Place Recognition
Authors: Dominik Schlegel, Giorgio Grisetti
Date: 2018-02-26 00:00:00
Tasks: Image Retrieval, Retrieval, Visual Place Recognition

Reliable and efficient Visual Place Recognition is a major building block of
modern SLAM systems. Leveraging on our prior work, in this paper we present a
Hamming Distance embedding Binary Search Tree (HBST) approach for binary
Descriptor Matching and Image Retrieval. HBST allows for descriptor Search and
Insertion in logarithmic time by exploiting particular properties of binary
Feature descriptors. We support the idea behind our search structure with a
thorough analysis on the exploited descriptor properties and their effects on
completeness and complexity of search and insertion. To validate our claims we
conducted comparative experiments for HBST and several state-of-the-art methods
on a broad range of publicly available datasets. HBST is available as a compact
open-source C++ header-only library.
SQL-Rank: A Listwise Approach to Collaborative Ranking
Authors: Liwei Wu, Cho-Jui Hsieh, James Sharpnack
Date: 2018-02-28 00:00:00
Proceeding: ICML 2018 7
Tasks: Collaborative Ranking, Recommendation Systems

In this paper, we propose a listwise approach for constructing user-specific
rankings in recommendation systems in a collaborative fashion. We contrast the
listwise approach to previous pointwise and pairwise approaches, which are
based on treating either each rating or each pairwise comparison as an
independent instance respectively. By extending the work of (Cao et al. 2007),
we cast listwise collaborative ranking as maximum likelihood under a
permutation model which applies probability mass to permutations based on a low
rank latent score matrix. We present a novel algorithm called SQL-Rank, which
can accommodate ties and missing data and can run in linear time. We develop a
theoretical framework for analyzing listwise ranking methods based on a novel
representation theory for the permutation model. Applying this framework to
collaborative ranking, we derive asymptotic statistical rates as the number of
users and items grow together. We conclude by demonstrating that our SQL-Rank
method often outperforms current state-of-the-art algorithms for implicit
feedback such as Weighted-MF and BPR and achieve favorable results when
compared to explicit feedback algorithms such as matrix factorization and
collaborative ranking.
Approximate Inference for Constructing Astronomical Catalogs from Images
Authors: Jeffrey Regier, Andrew C. Miller, David Schlegel, Ryan P. Adams, Jon D. McAuliffe, Prabhat
Date: 2018-02-28 00:00:00
Tasks: CPU, Variational Inference

We present a new, fully generative model for constructing astronomical
catalogs from optical telescope image sets. Each pixel intensity is treated as
a random variable with parameters that depend on the latent properties of stars
and galaxies. These latent properties are themselves modeled as random. We
compare two procedures for posterior inference. One procedure is based on
Markov chain Monte Carlo (MCMC) while the other is based on variational
inference (VI). The MCMC procedure excels at quantifying uncertainty, while the
VI procedure is 1000 times faster. On a supercomputer, the VI procedure
efficiently uses 665,000 CPU cores to construct an astronomical catalog from 50
terabytes of images in 14.6 minutes, demonstrating the scaling characteristics
necessary to construct catalogs for upcoming astronomical surveys.
Predicting Recall Probability to Adaptively Prioritize Study
Authors: Shane Mooney, Karen Sun, Eric Bomgardner
Date: 2018-02-28 00:00:00
Tasks: Scheduling

Students have a limited time to study and are typically ineffective at
allocating study time. Machine-directed study strategies that identify which
items need reinforcement and dictate the spacing of repetition have been shown
to help students optimize mastery (Mozer & Lindsey 2017). The large volume of
research on this matter is typically conducted in constructed experimental
settings with fixed instruction, content, and scheduling; in contrast, we aim
to develop methods that can address any demographic, subject matter, or study
schedule. We show two methods that model item-specific recall probability for
use in a discrepancy-reduction instruction strategy. The first model predicts
item recall probability using a multiple logistic regression (MLR) model based
on previous answer correctness and temporal spacing of study. Prompted by
literature suggesting that forgetting is better modeled by the power law than
an exponential decay (Wickelgren 1974), we compare the MLR approach with a
Recurrent Power Law (RPL) model which adaptively fits a forgetting curve. We
then discuss the performance of these models against study datasets comprised
of millions of answers and show that the RPL approach is more accurate and
flexible than the MLR model. Finally, we give an overview of promising future
approaches to knowledge modeling.
Complex-Valued Gaussian Processes for Regression
Authors: Rafael Boloix-Tortosa, Eva Arias-de-Reyna, F. Javier Payan-Somet, Juan J. Murillo-Fuentes
Date: 2015-11-18 00:00:00
Tasks: Gaussian Processes, regression

In this paper we propose a novel Bayesian solution for nonlinear regression
in complex fields. Previous solutions for kernels methods usually assume a
complexification approach, where the real-valued kernel is replaced by a
complex-valued one. This approach is limited. Based on results in
complex-valued linear theory and Gaussian random processes we show that a
pseudo-kernel must be included. This is the starting point to develop the new
complex-valued formulation for Gaussian process for regression (CGPR). We face
the design of the covariance and pseudo-covariance based on a convolution
approach and for several scenarios. Just in the particular case where the
outputs are proper, the pseudo-kernel cancels. Also, the hyperparameters of the
covariance {can be learnt} maximizing the marginal likelihood using Wirtinger's
calculus and patterned complex-valued matrix derivatives. In the experiments
included, we show how CGPR successfully solve systems where real and imaginary
parts are correlated. Besides, we successfully solve the nonlinear channel
equalization problem by developing a recursive solution with basis removal. We
report remarkable improvements compared to previous solutions: a 2-4 dB
reduction of the MSE with {just a quarter} of the training samples used by
previous approaches.
Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning
Authors: Vladimir Feinberg, Alvin Wan, Ion Stoica, Michael. I. Jordan, Joseph E. Gonzalez, Sergey Levine
Date: 2018-02-28 00:00:00
Tasks: continuous-control, Continuous Control, model, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent model-free reinforcement learning algorithms have proposed
incorporating learned dynamics models as a source of additional data with the
intention of reducing sample complexity. Such methods hold the promise of
incorporating imagined data coupled with a notion of model uncertainty to
accelerate the learning of continuous control tasks. Unfortunately, they rely
on heuristics that limit usage of the dynamics model. We present model-based
value expansion, which controls for uncertainty in the model by only allowing
imagination to fixed depth. By enabling wider use of learned dynamics models
within a model-free reinforcement learning algorithm, we improve value
estimation, which, in turn, reduces the sample complexity of learning.
NETT: Solving Inverse Problems with Deep Neural Networks
Authors: Housen Li, Johannes Schwab, Stephan Antholzer, Markus Haltmeier
Date: 2018-02-28 00:00:00

Recovering a function or high-dimensional parameter vector from indirect measurements is a central task in various scientific areas. Several methods for solving such inverse problems are well developed and well understood. Recently, novel algorithms using deep learning and neural networks for inverse problems appeared. While still in their infancy, these techniques show astonishing performance for applications like low-dose CT or various sparse data problems. However, there are few theoretical results for deep learning in inverse problems. In this paper, we establish a complete convergence analysis for the proposed NETT (Network Tikhonov) approach to inverse problems. NETT considers data consistent solutions having small value of a regularizer defined by a trained neural network. We derive well-posedness results and quantitative error estimates, and propose a possible strategy for training the regularizer. Our theoretical results and framework are different from any previous work using neural networks for solving inverse problems. A possible data driven regularizer is proposed. Numerical results are presented for a tomographic sparse data problem, which demonstrate good performance of NETT even for unknowns of different type from the training data. To derive the convergence and convergence rates results we introduce a new framework based on the absolute Bregman distance generalizing the standard Bregman distance from the convex to the non-convex case.
Verification of Markov Decision Processes with Risk-Sensitive Measures
Authors: Murat Cubuktepe, Ufuk Topcu
Date: 2018-02-28 00:00:00

We develop a method for computing policies in Markov decision processes with risk-sensitive measures subject to temporal logic constraints. Specifically, we use a particular risk-sensitive measure from cumulative prospect theory, which has been previously adopted in psychology and economics. The nonlinear transformation of the probabilities and utility functions yields a nonlinear programming problem, which makes computation of optimal policies typically challenging. We show that this nonlinear weighting function can be accurately approximated by the difference of two convex functions. This observation enables efficient policy computation using convex-concave programming. We demonstrate the effectiveness of the approach on several scenarios.
Chinese Text in the Wild
Authors: Tai-Ling Yuan, Zhe Zhu, Kun Xu, Cheng-Jun Li, Shi-Min Hu
Date: 2018-02-28 00:00:00
Tasks: Optical Character Recognition (OCR)

[python3.6] 运用tf实现自然场景文字检测,keras/pytorch实现ctpn+crnn+ctc实现不定长场景文字OCR识别
Gotta Adapt 'Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the Wild
Authors: Luan Tran, Kihyuk Sohn, Xiang Yu, Xiaoming Liu, Manmohan Chandraker
Date: 2018-02-28 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D geometry, All, Attribute, Domain Adaptation, Image Generation, Transfer Learning

Recent developments in deep domain adaptation have allowed knowledge transfer from a labeled source domain to an unlabeled target domain at the level of intermediate features or input pixels. We propose that advantages may be derived by combining them, in the form of different insights that lead to a novel design and complementary properties that result in better performance. At the feature level, inspired by insights from semi-supervised learning, we propose a classification-aware domain adversarial neural network that brings target examples into more classifiable regions of source domain. Next, we posit that computer vision insights are more amenable to injection at the pixel level. In particular, we use 3D geometry and image synthesis based on a generalized appearance flow to preserve identity across pose transformations, while using an attribute-conditioned CycleGAN to translate a single source into multiple target images that differ in lower-level properties such as lighting. Besides standard UDA benchmark, we validate on a novel and apt problem of car recognition in unlabeled surveillance images using labeled images from the web, handling explicitly specified, nameable factors of variation through pixel-level and implicit, unspecified factors through feature-level adaptation.
Constrained Classification and Ranking via Quantiles
Authors: Alan Mackey, Xiyang Luo, Elad Eban
Date: 2018-02-28 00:00:00
Tasks: Classification, General Classification

In most machine learning applications, classification accuracy is not the
primary metric of interest. Binary classifiers which face class imbalance are
often evaluated by the $F_\beta$ score, area under the precision-recall curve,
Precision at K, and more. The maximization of many of these metrics can be
expressed as a constrained optimization problem, where the constraint is a
function of the classifier's predictions.
  In this paper we propose a novel framework for learning with constraints that
can be expressed as a predicted positive rate (or negative rate) on a subset of
the training data. We explicitly model the threshold at which a classifier must
operate to satisfy the constraint, yielding a surrogate loss function which
avoids the complexity of constrained optimization. The method is model-agnostic
and only marginally more expensive than minimization of the unconstrained loss.
Experiments on a variety of benchmarks show competitive performance relative to
existing baselines.
A Semi-Lagrangian two-level preconditioned Newton-Krylov solver for constrained diffeomorphic image registration
Authors: Andreas Mang, George Biros
Date: 2016-04-07 00:00:00
Tasks: Computational Efficiency, Constrained Diffeomorphic Image Registration, Image Registration, Medical Image Registration

We propose an efficient numerical algorithm for the solution of diffeomorphic
image registration problems. We use a variational formulation constrained by a
partial differential equation (PDE), where the constraints are a scalar
transport equation.
  We use a pseudospectral discretization in space and second-order accurate
semi-Lagrangian time stepping scheme for the transport equations. We solve for
a stationary velocity field using a preconditioned, globalized, matrix-free
Newton-Krylov scheme. We propose and test a two-level Hessian preconditioner.
We consider two strategies for inverting the preconditioner on the coarse grid:
a nested preconditioned conjugate gradient method (exact solve) and a nested
Chebyshev iterative method (inexact solve) with a fixed number of iterations.
  We test the performance of our solver in different synthetic and real-world
two-dimensional application scenarios. We study grid convergence and
computational efficiency of our new scheme. We compare the performance of our
solver against our initial implementation that uses the same spatial
discretization but a standard, explicit, second-order Runge-Kutta scheme for
the numerical time integration of the transport equations and a single-level
preconditioner. Our improved scheme delivers significant speedups over our
original implementation. As a highlight, we observe a 20$\times$ speedup for a
two dimensional, real world multi-subject medical image registration problem.
PDE-constrained optimization in medical image analysis
Authors: Andreas Mang, Amir Gholami, Christos Davatzikos, George Biros
Date: 2018-02-28 00:00:00
Tasks: Decision Making, Image Registration, Medical Image Analysis

PDE-constrained optimization problems find many applications in medical image
analysis, for example, neuroimaging, cardiovascular imaging, and oncological
imaging. We review related literature and give examples on the formulation,
discretization, and numerical solution of PDE-constrained optimization problems
for medical imaging. We discuss three examples. The first one is image
registration. The second one is data assimilation for brain tumor patients, and
the third one data assimilation in cardiovascular imaging. The image
registration problem is a classical task in medical image analysis and seeks to
find pointwise correspondences between two or more images. The data
assimilation problems use a PDE-constrained formulation to link a biophysical
model to patient-specific data obtained from medical images. The associated
optimality systems turn out to be sets of nonlinear, multicomponent PDEs that
are challenging to solve in an efficient way.
  The ultimate goal of our work is the design of inversion methods that
integrate complementary data, and rigorously follow mathematical and physical
principles, in an attempt to support clinical decision making. This requires
reliable, high-fidelity algorithms with a short time-to-solution. This task is
complicated by model and data uncertainties, and by the fact that
PDE-constrained optimization problems are ill-posed in nature, and in general
yield high-dimensional, severely ill-conditioned systems after discretization.
These features make regularization, effective preconditioners, and iterative
solvers that, in many cases, have to be implemented on distributed-memory
architectures to be practical, a prerequisite. We showcase state-of-the-art
techniques in scientific computing to tackle these challenges.
An Efficient, Expressive and Local Minima-free Method for Learning Controlled Dynamical Systems
Authors: Ahmed Hefny, Carlton Downey, Geoffrey J. Gordon
Date: 2017-02-12 00:00:00

We propose a framework for modeling and estimating the state of controlled
dynamical systems, where an agent can affect the system through actions and
receives partial observations. Based on this framework, we propose the
Predictive State Representation with Random Fourier Features (RFFPSR). A key
property in RFF-PSRs is that the state estimate is represented by a conditional
distribution of future observations given future actions. RFF-PSRs combine this
representation with moment-matching, kernel embedding and local optimization to
achieve a method that enjoys several favorable qualities: It can represent
controlled environments which can be affected by actions; it has an efficient
and theoretically justified learning algorithm; it uses a non-parametric
representation that has expressive power to represent continuous non-linear
dynamics. We provide a detailed formulation, a theoretical analysis and an
experimental evaluation that demonstrates the effectiveness of our method.
Super-Efficient Spatially Adaptive Contrast Enhancement Algorithm for Superficial Vein Imaging
Authors: A. M. R. R. Bandara, K. A. S. H. Kulathilake, P. W. G. R. M. P. B. Giragama
Date: 2018-02-28 00:00:00

This paper presents a super-efficient spatially adaptive contrast enhancement
algorithm for enhancing infrared (IR) radiation based superficial vein images
in real-time. The super-efficiency permits the algorithm to run in
consumer-grade handheld devices, which ultimately reduces the cost of vein
imaging equipment. The proposed method utilizes the response from the
low-frequency range of the IR image signal to adjust the boundaries of the
reference dynamic range in a linear contrast stretching process with a tunable
contrast enhancement parameter, as opposed to traditional approaches which use
costly adaptive histogram equalization based methods. The algorithm has been
implemented and deployed in a consumer grade Android-based mobile device to
evaluate the performance. The results revealed that the proposed algorithm can
process IR images of veins in real-time on low-performance computers. It was
compared with several well-performed traditional methods and the results
revealed that the new algorithm stands out with several beneficial features,
namely, the fastest processing, the ability to enhance the desired details, the
excellent illumination normalization capability and the ability to enhance
details where the traditional methods failed.
Invariant properties of a locally salient dither pattern with a spatial-chromatic histogram
Authors: A. M. R. R. Bandara, L. Ranathunga, N. A. Abdullah
Date: 2018-02-28 00:00:00

Compacted Dither Pattern Code (CDPC) is a recently found feature which is
successful in irregular shapes based visual depiction. Locally salient dither
pattern feature is an attempt to expand the capability of CDPC for both regular
and irregular shape based visual depiction. This paper presents an analysis of
rotational and scale invariance property of locally salient dither pattern
feature with a two dimensional spatialchromatic histogram, which expands the
applicability of the visual feature. Experiments were conducted to exhibit
rotational and scale invariance of the feature. These experiments were
conducted by combining linear Support Vector Machine (SVM) classifier to the
new feature. The experimental results revealed that the locally salient dither
pattern feature with the spatialchromatic histogram is rotationally and scale
invariant.
A Retinal Image Enhancement Technique for Blood Vessel Segmentation Algorithm
Authors: A. M. R. R. Bandara, P. W. G. R. M. P. B. Giragama
Date: 2018-02-28 00:00:00
Tasks: Image Enhancement, Segmentation

The morphology of blood vessels in retinal fundus images is an important
indicator of diseases like glaucoma, hypertension and diabetic retinopathy. The
accuracy of retinal blood vessels segmentation affects the quality of retinal
image analysis which is used in diagnosis methods in modern ophthalmology.
Contrast enhancement is one of the crucial steps in any of retinal blood vessel
segmentation approaches. The reliability of the segmentation depends on the
consistency of the contrast over the image. This paper presents an assessment
of the suitability of a recently invented spatially adaptive contrast
enhancement technique for enhancing retinal fundus images for blood vessel
segmentation. The enhancement technique was integrated with a variant of Tyler
Coye algorithm, which has been improved with Hough line transformation based
vessel reconstruction method. The proposed approach was evaluated on two public
datasets STARE and DRIVE. The assessment was done by comparing the segmentation
performance with five widely used contrast enhancement techniques based on
wavelet transform, contrast limited histogram equalization, local
normalization, linear un-sharp masking and contourlet transform. The results
revealed that the assessed enhancement technique is well suited for the
application and also outperforms all compared techniques.