pwc-archive/papers-with-abstracts huggingface dataset:
Trifocal Relative Pose from Lines at Points and its Efficient Solution
Authors: Ricardo Fabbri, Timothy Duff, Hongyi Fan, Margaret Regan, David da Costa de Pinho, Elias Tsigaridas, Charles Wampler, Jonathan Hauenstein, Benjamin Kimia, Anton Leykin, Tomas Pajdla
Date: 2019-03-23 00:00:00
Tasks: Camera Pose Estimation, Pose Estimation

We present a method for solving two minimal problems for relative camera pose estimation from three views, which are based on three view correspondences of i) three points and one line and the novel case of ii) three points and two lines through two of the points. These problems are too difficult to be efficiently solved by the state of the art Groebner basis methods. Our method is based on a new efficient homotopy continuation (HC) solver framework MINUS, which dramatically speeds up previous HC solving by specializing HC methods to generic cases of our problems. We characterize their number of solutions and show with simulated experiments that our solvers are numerically robust and stable under image noise, a key contribution given the borderline intractable degree of nonlinearity of trinocular constraints. We show in real experiments that i) SIFT feature location and orientation provide good enough point-and-line correspondences for three-view reconstruction and ii) that we can solve difficult cases with too few or too noisy tentative matches, where the state of the art structure from motion initialization fails.
Residual Pyramid Learning for Single-Shot Semantic Segmentation
Authors: Xiaoyu Chen, Xiaotian Lou, Lianfa Bai, Jing Han
Date: 2019-03-23 00:00:00
Tasks: Decoder, Segmentation, Semantic Segmentation

Pixel-level semantic segmentation is a challenging task with a huge amount of
computation, especially if the size of input is large. In the segmentation
model, apart from the feature extraction, the extra decoder structure is often
employed to recover spatial information. In this paper, we put forward a method
for single-shot segmentation in a feature residual pyramid network (RPNet),
which learns the main and residuals of segmentation by decomposing the label at
different levels of residual blocks. Specifically speaking, we use the residual
features to learn the edges and details, and the identity features to learn the
main part of targets. At testing time, the predicted residuals are used to
enhance the details of the top-level prediction. Residual learning blocks split
the network into several shallow sub-networks which facilitates the training of
the RPNet. We then evaluate the proposed method and compare it with recent
state-of-the-art methods on CamVid and Cityscapes. The proposed single-shot
segmentation based on RPNet achieves impressive results with high efficiency on
pixel-level segmentation.
Fast LLMMSE filter for low-dose CT imaging
Authors: Fengling Wang, Bowen Lin, Shujun Fu, Shiling Xie, Zhigang Zhao, Yuliang Li
Date: 2019-03-23 00:00:00

Low-dose X-ray CT technology is one of important directions of current
research and development of medical imaging equipment. A fast algorithm of
blockwise sinogram filtering is presented for realtime low-dose CT imaging. A
nonstationary Gaussian noise model of low-dose sinogram data is proposed in the
low-mA (tube current) CT protocol. Then, according to the linear minimum mean
square error principle, an adaptive blockwise algorithm is built to filter
contaminated sinogram data caused by photon starvation. A moving sum technique
is used to speed the algorithm into a linear time one, regardless of the block
size and thedata range. The proposedfast filtering givesa better performance in
noise reduction and detail preservation in the reconstructed images,which is
verified in experiments on simulated and real data compared with some related
filtering methods.
Regularized Learning for Domain Adaptation under Label Shifts
Authors: Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang, Animashree Anandkumar
Date: 2019-03-22 00:00:00
Proceeding: ICLR 2019 5
Tasks: Domain Adaptation

We propose Regularized Learning under Label shifts (RLLS), a principled and a
practical domain-adaptation algorithm to correct for shifts in the label
distribution between a source and a target domain. We first estimate importance
weights using labeled source data and unlabeled target data, and then train a
classifier on the weighted source samples. We derive a generalization bound for
the classifier on the target domain which is independent of the (ambient) data
dimensions, and instead only depends on the complexity of the function class.
To the best of our knowledge, this is the first generalization bound for the
label-shift problem where the labels in the target domain are not available.
Based on this bound, we propose a regularized estimator for the small-sample
regime which accounts for the uncertainty in the estimated weights. Experiments
on the CIFAR-10 and MNIST datasets show that RLLS improves classification
accuracy, especially in the low sample and large-shift regimes, compared to
previous methods.
Time Series Imputation
Authors: Samuel Arcadinho, Paulo Mateus
Date: 2019-03-22 00:00:00
Tasks: BIG-bench Machine Learning, Imputation, Missing Values, Time Series, Time Series Analysis

Multivariate time series is a very active topic in the research community and
many machine learning tasks are being used in order to extract information from
this type of data. However, in real-world problems data has missing values,
which may difficult the application of machine learning techniques to extract
information. In this paper we focus on the task of imputation of time series.
Many imputation methods for time series are based on regression methods.
Unfortunately, these methods perform poorly when the variables are categorical.
To address this case, we propose a new imputation method based on Expectation
Maximization over dynamic Bayesian networks. The approach is assessed with
synthetic and real data, and it outperforms several state-of-the art methods.
Expert-Augmented Machine Learning
Authors: E. D. Gennatas, J. H. Friedman, L. H. Ungar, R. Pirracchio, E. Eaton, L. Reichman, Y. Interian, C. B. Simone, A. Auerbach, E. Delgado, M. J. Van der Laan, T. D. Solberg, G. Valdes
Date: 2019-03-22 00:00:00
Tasks: BIG-bench Machine Learning

Machine Learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption by the level of trust that models afford users. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of man and machine. Here we present Expert-Augmented Machine Learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We use a large dataset of intensive care patient data to predict mortality and show that we can extract expert knowledge using an online platform, help reveal hidden confounders, improve generalizability on a different population and learn using less data. EAML presents a novel framework for high performance and dependable machine learning in critical applications.
Generative Adversarial Minority Oversampling
Authors: Sankha Subhra Mullick, Shounak Datta, Swagatam Das
Date: 2019-03-22 00:00:00
Proceeding: ICCV 2019 10
Tasks: Deep Learning

Class imbalance is a long-standing problem relevant to a number of real-world applications of deep learning. Oversampling techniques, which are effective for handling class imbalance in classical learning systems, can not be directly applied to end-to-end deep learning systems. We propose a three-player adversarial game between a convex generator, a multi-class classifier network, and a real/fake discriminator to perform oversampling in deep learning systems. The convex generator generates new samples from the minority classes as convex combinations of existing instances, aiming to fool both the discriminator as well as the classifier into misclassifying the generated samples. Consequently, the artificial samples are generated at critical locations near the peripheries of the classes. This, in turn, adjusts the classifier induced boundaries in a way which is more likely to reduce misclassification from the minority classes. Extensive experiments on multiple class imbalanced image datasets establish the efficacy of our proposal.
Pre-trained Language Model Representations for Language Generation
Authors: Sergey Edunov, Alexei Baevski, Michael Auli
Date: 2019-03-22 00:00:00
Proceeding: NAACL 2019 6
Tasks: Abstractive Text Summarization, Language Modeling, Language Modelling, Machine Translation, model, Sentence, Text Generation, Translation

Pre-trained language model representations have been successful in a wide
range of language understanding tasks. In this paper, we examine different
strategies to integrate pre-trained representations into sequence to sequence
models and apply it to neural machine translation and abstractive
summarization. We find that pre-trained representations are most effective when
added to the encoder network which slows inference by only 14%. Our experiments
in machine translation show gains of up to 5.3 BLEU in a simulated
resource-poor setup. While returns diminish with more labeled data, we still
observe improvements when millions of sentence-pairs are available. Finally, on
abstractive summarization we achieve a new state of the art on the full text
version of CNN/DailyMail.
An Interaction Framework for Studying Co-Creative AI
Authors: Matthew Guzdial, Mark Riedl
Date: 2019-03-22 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning has been applied to a number of creative, design-oriented
tasks. However, it remains unclear how to best empower human users with these
machine learning approaches, particularly those users without technical
expertise. In this paper we propose a general framework for turn-based
interaction between human users and AI agents designed to support human
creativity, called {co-creative systems}. The framework can be used to better
understand the space of possible designs of co-creative systems and reveal
future research directions. We demonstrate how to apply this framework in
conjunction with a pair of recent human subject studies, comparing between the
four human-AI systems employed in these studies and generating hypotheses
towards future studies.
Explaining Reinforcement Learning to Mere Mortals: An Empirical Study
Authors: Andrew Anderson, Jonathan Dodge, Amrita Sadarangani, Zoe Juozapaitis, Evan Newman, Jed Irvine, Souti Chattopadhyay, Alan Fern, Margaret Burnett
Date: 2019-03-22 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present a user study to investigate the impact of explanations on non-experts' understanding of reinforcement learning (RL) agents. We investigate both a common RL visualization, saliency maps (the focus of attention), and a more recent explanation type, reward-decomposition bars (predictions of future types of rewards). We designed a 124 participant, four-treatment experiment to compare participants' mental models of an RL agent in a simple Real-Time Strategy (RTS) game. Our results show that the combination of both saliency and reward bars were needed to achieve a statistically significant improvement in mental model score over the control. In addition, our qualitative analysis of the data reveals a number of effects for further study.
CUR Decompositions, Approximations, and Perturbations
Authors: Keaton Hamm, Longxiu Huang
Date: 2019-03-22 00:00:00
Tasks: Dimensionality Reduction

This article discusses a useful tool in dimensionality reduction and low-rank
matrix approximation called the CUR decomposition. Various viewpoints of this
method in the literature are synergized and are compared and contrasted;
included in this is a new characterization of exact CUR decompositions. A novel
perturbation analysis is performed on CUR approximations of noisy versions of
low-rank matrices, which compares them with the putative CUR decomposition of
the underlying low-rank part. Additionally, we give new column and row sampling
results which allow one to conclude that a CUR decomposition of a low-rank
matrix is attained with high probability. We then illustrate the stability of
these sampling methods under the perturbations studied before, and provide
numerical illustrations of the methods and bounds discussed.
Symbolic Regression Methods for Reinforcement Learning
Authors: Jiří Kubalík, Erik Derner, Jan Žegklitz, Robert Babuška
Date: 2019-03-22 00:00:00
Tasks: Decision Making, Friction, regression, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Symbolic Regression

Reinforcement learning algorithms can solve dynamic decision-making and optimal control problems. With continuous-valued state and input variables, reinforcement learning algorithms must rely on function approximators to represent the value function and policy mappings. Commonly used numerical approximators, such as neural networks or basis function expansions, have two main drawbacks: they are black-box models offering little insight into the mappings learned, and they require extensive trial and error tuning of their hyper-parameters. In this paper, we propose a new approach to constructing smooth value functions in the form of analytic expressions by using symbolic regression. We introduce three off-line methods for finding value functions based on a state-transition model: symbolic value iteration, symbolic policy iteration, and a direct solution of the Bellman equation. The methods are illustrated on four nonlinear control problems: velocity control under friction, one-link and two-link pendulum swing-up, and magnetic manipulation. The results show that the value functions yield well-performing policies and are compact, mathematically tractable, and easy to plug into other algorithms. This makes them potentially suitable for further analysis of the closed-loop system. A comparison with an alternative approach using neural networks shows that our method outperforms the neural network-based one.
BshapeNet: Object Detection and Instance Segmentation with Bounding Shape Masks
Authors: Ba Rom Kang, Ha Young Kim
Date: 2018-10-15 00:00:00
Tasks: Instance Segmentation, Object, object-detection, Object Detection, Semantic Segmentation

Recent object detectors use four-coordinate bounding box (bbox) regression to predict object locations. Providing additional information indicating the object positions and coordinates will improve detection performance. Thus, we propose two types of masks: a bbox mask and a bounding shape (bshape) mask, to represent the object's bbox and boundary shape, respectively. For each of these types, we consider two variants: the Thick model and the Scored model, both of which have the same morphology but differ in ways to make their boundaries thicker. To evaluate the proposed masks, we design extended frameworks by adding a bshape mask (or a bbox mask) branch to a Faster R-CNN framework, and call this BshapeNet (or BboxNet). Further, we propose BshapeNet+, a network that combines a bshape mask branch with a Mask R-CNN to improve instance segmentation as well as detection. Among our proposed models, BshapeNet+ demonstrates the best performance in both tasks and achieves highly competitive results with state of the art (SOTA) models. Particularly, it improves the detection results over Faster R-CNN+RoIAlign (37.3% and 28.9%) with a detection AP of 42.4% and 32.3% on MS COCO test-dev and Cityscapes val, respectively. Furthermore, for small objects, it achieves 24.9% AP on COCO test-dev, a significant improvement over previous SOTA models. For instance segmentation, it is substantially superior to Mask R-CNN on both test datasets.
Axonal Conduction Velocity Impacts Neuronal Network Oscillations
Authors: Vladimir A. Ivanov, Ioannis E. Polykretis, Konstantinos P. Michmizos
Date: 2019-03-22 00:00:00

Increasing experimental evidence suggests that axonal action potential
conduction velocity is a highly adaptive parameter in the adult central nervous
system. Yet, the effects of this newfound plasticity on global brain dynamics
is poorly understood. In this work, we analyzed oscillations in biologically
plausible neuronal networks with different conduction velocity distributions.
Changes of 1-2 (ms) in network mean signal transmission time resulted in
substantial network oscillation frequency changes ranging in 0-120 (Hz). Our
results suggest that changes in axonal conduction velocity may significantly
affect both the frequency and synchrony of brain rhythms, which have well
established connections to learning, memory, and other cognitive processes.
Data Augmentation for Bayesian Deep Learning
Authors: Yuexi Wang, Nicholas G. Polson, Vadim O. Sokolov
Date: 2019-03-22 00:00:00
Tasks: Data Augmentation, Deep Learning, Uncertainty Quantification

Deep Learning (DL) methods have emerged as one of the most powerful tools for functional approximation and prediction. While the representation properties of DL have been well studied, uncertainty quantification remains challenging and largely unexplored. Data augmentation techniques are a natural approach to provide uncertainty quantification and to incorporate stochastic Monte Carlo search into stochastic gradient descent (SGD) methods. The purpose of our paper is to show that training DL architectures with data augmentation leads to efficiency gains. We use the theory of scale mixtures of normals to derive data augmentation strategies for deep learning. This allows variants of the expectation-maximization and MCMC algorithms to be brought to bear on these high dimensional nonlinear deep learning models. To demonstrate our methodology, we develop data augmentation algorithms for a variety of commonly used activation functions: logit, ReLU, leaky ReLU and SVM. Our methodology is compared to traditional stochastic gradient descent with back-propagation. Our optimization procedure leads to a version of iteratively re-weighted least squares and can be implemented at scale with accelerated linear algebra methods providing substantial improvement in speed. We illustrate our methodology on a number of standard datasets. Finally, we conclude with directions for future research.
Capsule Networks with Max-Min Normalization
Authors: Zhen Zhao, Ashley Kleinhans, Gursharan Sandhu, Ishan Patel, K. P. Unnikrishnan
Date: 2019-03-22 00:00:00

Capsule Networks (CapsNet) use the Softmax function to convert the logits of
the routing coefficients into a set of normalized values that signify the
assignment probabilities between capsules in adjacent layers. We show that the
use of Softmax prevents capsule layers from forming optimal couplings between
lower and higher-level capsules. Softmax constrains the dynamic range of the
routing coefficients and leads to probabilities that remain mostly uniform
after several routing iterations. Instead, we propose the use of Max-Min
normalization. Max-Min performs a scale-invariant normalization of the logits
that allows each lower-level capsule to take on an independent value,
constrained only by the bounds of normalization. Max-Min provides consistent
improvement in test accuracy across five datasets and allows more routing
iterations without a decrease in network performance. A single CapsNet trained
using Max-Min achieves an improved test error of 0.20% on the MNIST dataset.
With a simple 3-model majority vote, we achieve a test error of 0.17% on MNIST.
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition
Authors: Yao Qin, Nicholas Carlini, Ian Goodfellow, Garrison Cottrell, Colin Raffel
Date: 2019-03-22 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Sentence, speech-recognition, Speech Recognition

Adversarial examples are inputs to machine learning models designed by an adversary to cause an incorrect output. So far, adversarial examples have been studied most extensively in the image domain. In this domain, adversarial examples can be constructed by imperceptibly modifying images to cause misclassification, and are practical in the physical world. In contrast, current targeted adversarial examples applied to speech recognition systems have neither of these properties: humans can easily identify the adversarial perturbations, and they are not effective when played over-the-air. This paper makes advances on both of these fronts. First, we develop effectively imperceptible audio adversarial examples (verified through a human study) by leveraging the psychoacoustic principle of auditory masking, while retaining 100% targeted success rate on arbitrary full-sentence targets. Next, we make progress towards physical-world over-the-air audio adversarial examples by constructing perturbations which remain effective even after applying realistic simulated environmental distortions.
High-Dimensional Linear Regression via Implicit Regularization
Authors: Peng Zhao, Yun Yang, Qiao-Chu He
Date: 2019-03-22 00:00:00
Tasks: regression, Vocal Bursts Intensity Prediction

Many statistical estimators for high-dimensional linear regression are M-estimators, formed through minimizing a data-dependent square loss function plus a regularizer. This work considers a new class of estimators implicitly defined through a discretized gradient dynamic system under overparameterization. We show that under suitable restricted isometry conditions, overparameterization leads to implicit regularization: if we directly apply gradient descent to the residual sum of squares with sufficiently small initial values, then under some proper early stopping rule, the iterates converge to a nearly sparse rate-optimal solution that improves over explicitly regularized approaches. In particular, the resulting estimator does not suffer from extra bias due to explicit penalties, and can achieve the parametric root-n rate when the signal-to-noise ratio is sufficiently high. We also perform simulations to compare our methods with high dimensional linear regression with explicit regularization. Our results illustrate the advantages of using implicit regularization via gradient descent after overparameterization in sparse vector estimation.
Trace transform based method for color image domain identification
Authors: Igor G. Olaizola, Marco Quartulli, Julian Florez, Basilio Sierra
Date: 2012-08-19 00:00:00
Tasks: Dimensionality Reduction

Context categorization is a fundamental pre-requisite for multi-domain
multimedia content analysis applications in order to manage contextual
information in an efficient manner. In this paper, we introduce a new color
image context categorization method (DITEC) based on the trace transform. The
problem of dimensionality reduction of the obtained trace transform signal is
addressed through statistical descriptors that keep the underlying information.
These extracted features offer a highly discriminant behavior for content
categorization. The theoretical properties of the method are analyzed and
validated experimentally through two different datasets.