pwc-archive/papers-with-abstracts huggingface dataset:
Discovering Heterogeneous Subsequences for Trajectory Classification
Authors: Carlos Andres Ferrero, Lucas May Petry, Luis Otavio Alvares, Willian Zalewski, Vania Bogorny
Date: 2019-03-18 00:00:00
Tasks: Classification, General Classification

In this paper we propose a new parameter-free method for trajectory
classification which finds the best trajectory partition and dimension
combination for robust trajectory classification. Preliminary experiments show
that our approach is very promising.
A RAD approach to deep mixture models
Authors: Laurent Dinh, Jascha Sohl-Dickstein, Hugo Larochelle, Razvan Pascanu
Date: 2019-03-18 00:00:00
Proceeding: ICLR Workshop DeepGenStruct 2019
Tasks: Density Estimation

Flow based models such as Real NVP are an extremely powerful approach to density estimation. However, existing flow based models are restricted to transforming continuous densities over a continuous input space into similarly continuous distributions over continuous latent variables. This makes them poorly suited for modeling and representing discrete structures in data distributions, for example class membership or discrete symmetries. To address this difficulty, we present a normalizing flow architecture which relies on domain partitioning using locally invertible functions, and possesses both real and discrete valued latent variables. This Real and Discrete (RAD) approach retains the desirable normalizing flow properties of exact sampling, exact inference, and analytically computable probabilities, while at the same time allowing simultaneous modeling of both continuous and discrete structure in a data distribution.
Direct Object Recognition Without Line-of-Sight Using Optical Coherence
Authors: Xin Lei, Liangyu He, Yixuan Tan, Ken Xingze Wang, Xinggang Wang, Yihan Du, Shanhui Fan, Zongfu Yu
Date: 2019-03-18 00:00:00
Proceeding: CVPR 2019 6
Tasks: Object, Object Recognition

Visual object recognition under situations in which the direct line-of-sight
is blocked, such as when it is occluded around the corner, is of practical
importance in a wide range of applications. With coherent illumination, the
light scattered from diffusive walls forms speckle patterns that contain
information of the hidden object. It is possible to realize non-line-of-sight
(NLOS) recognition with these speckle patterns. We introduce a novel approach
based on speckle pattern recognition with deep neural network, which is simpler
and more robust than other NLOS recognition methods. Simulations and
experiments are performed to verify the feasibility and performance of this
approach.
Deep Fundamental Factor Models
Authors: Matthew F. Dixon, Nicholas G. Polson
Date: 2019-03-18 00:00:00
Tasks: Uncertainty Quantification

Deep fundamental factor models are developed to automatically capture non-linearity and interaction effects in factor modeling. Uncertainty quantification provides interpretability with interval estimation, ranking of factor importances and estimation of interaction effects. With no hidden layers we recover a linear factor model and for one or more hidden layers, uncertainty bands for the sensitivity to each input naturally arise from the network weights. Using 3290 assets in the Russell 1000 index over a period of December 1989 to January 2018, we assess a 49 factor model and generate information ratios that are approximately 1.5x greater than the OLS factor model. Furthermore, we compare our deep fundamental factor model with a quadratic LASSO model and demonstrate the superior performance and robustness to outliers. The Python source code and the data used for this study are provided.
Software-defined Design Space Exploration for an Efficient DNN Accelerator Architecture
Authors: Ye Yu, Yingmin Li, Shuai Che, Niraj K. Jha, Weifeng Zhang
Date: 2019-03-18 00:00:00
Tasks: object-detection, Object Detection

Deep neural networks (DNNs) have been shown to outperform conventional machine learning algorithms across a wide range of applications, e.g., image recognition, object detection, robotics, and natural language processing. However, the high computational complexity of DNNs often necessitates extremely fast and efficient hardware. The problem gets worse as the size of neural networks grows exponentially. As a result, customized hardware accelerators have been developed to accelerate DNN processing without sacrificing model accuracy. However, previous accelerator design studies have not fully considered the characteristics of the target applications, which may lead to sub-optimal architecture designs. On the other hand, new DNN models have been developed for better accuracy, but their compatibility with the underlying hardware accelerator is often overlooked. In this article, we propose an application-driven framework for architectural design space exploration of DNN accelerators. This framework is based on a hardware analytical model of individual DNN operations. It models the accelerator design task as a multi-dimensional optimization problem. We demonstrate that it can be efficaciously used in application-driven accelerator architecture design. Given a target DNN, the framework can generate efficient accelerator design solutions with optimized performance and area. Furthermore, we explore the opportunity to use the framework for accelerator configuration optimization under simultaneous diverse DNN applications. The framework is also capable of improving neural network models to best fit the underlying hardware resources.
Neural Sequential Phrase Grounding (SeqGROUND)
Authors: Pelin Dogan, Leonid Sigal, Markus Gross
Date: 2019-03-18 00:00:00
Proceeding: CVPR 2019 6
Tasks: Phrase Grounding

We propose an end-to-end approach for phrase grounding in images. Unlike
prior methods that typically attempt to ground each phrase independently by
building an image-text embedding, our architecture formulates grounding of
multiple phrases as a sequential and contextual process. Specifically, we
encode region proposals and all phrases into two stacks of LSTM cells, along
with so-far grounded phrase-region pairs. These LSTM stacks collectively
capture context for grounding of the next phrase. The resulting architecture,
which we call SeqGROUND, supports many-to-many matching by allowing an image
region to be matched to multiple phrases and vice versa. We show competitive
performance on the Flickr30K benchmark dataset and, through ablation studies,
validate the efficacy of sequential grounding as well as individual design
choices in our model architecture.
An Updated Duet Model for Passage Re-ranking
Authors: Bhaskar Mitra, Nick Craswell
Date: 2019-03-18 00:00:00
Tasks: model, Passage Ranking, Passage Re-Ranking, Re-Ranking

We propose several small modifications to Duet---a deep neural ranking
model---and evaluate the updated model on the MS MARCO passage ranking task. We
report significant improvements from the proposed changes based on an ablation
study.
Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI
Authors: Endre Grøvik, Darvin Yi, Michael Iv, Elisabeth Tong, Daniel L. Rubin, Greg Zaharchuk
Date: 2019-03-18 00:00:00

Detecting and segmenting brain metastases is a tedious and time-consuming
task for many radiologists, particularly with the growing use of multi-sequence
3D imaging. This study demonstrates automated detection and segmentation of
brain metastases on multi-sequence MRI using a deep learning approach based on
a fully convolution neural network (CNN). In this retrospective study, a total
of 156 patients with brain metastases from several primary cancers were
included. Pre-therapy MR images (1.5T and 3T) included pre- and post-gadolinium
T1-weighted 3D fast spin echo, post-gadolinium T1-weighted 3D axial IR-prepped
FSPGR, and 3D fluid attenuated inversion recovery. The ground truth was
established by manual delineation by two experienced neuroradiologists. CNN
training/development was performed using 100 and 5 patients, respectively, with
a 2.5D network based on a GoogLeNet architecture. The results were evaluated in
51 patients, equally separated into those with few (1-3), multiple (4-10), and
many (>10) lesions. Network performance was evaluated using precision, recall,
Dice/F1 score, and ROC-curve statistics. For an optimal probability threshold,
detection and segmentation performance was assessed on a per metastasis basis.
The area under the ROC-curve (AUC), averaged across all patients, was 0.98. The
AUC in the subgroups was 0.99, 0.97, and 0.97 for patients having 1-3, 4-10,
and >10 metastases, respectively. Using an average optimal probability
threshold determined by the development set, precision, recall, and Dice-score
were 0.79, 0.53, and 0.79, respectively. At the same probability threshold, the
network showed an average false positive rate of 8.3/patient (no lesion-size
limit) and 3.4/patient (10 mm3 lesion size limit). In conclusion, a deep
learning approach using multi-sequence MRI can aid in the detection and
segmentation of brain metastases.
SCNN: A General Distribution based Statistical Convolutional Neural Network with Application to Video Object Detection
Authors: Tianchen Wang, JinJun Xiong, Xiaowei Xu, Yiyu Shi
Date: 2019-03-15 00:00:00
Tasks: object-detection, Object Detection, Video Object Detection

Various convolutional neural networks (CNNs) were developed recently that
achieved accuracy comparable with that of human beings in computer vision tasks
such as image recognition, object detection and tracking, etc. Most of these
networks, however, process one single frame of image at a time, and may not
fully utilize the temporal and contextual correlation typically present in
multiple channels of the same image or adjacent frames from a video, thus
limiting the achievable throughput. This limitation stems from the fact that
existing CNNs operate on deterministic numbers. In this paper, we propose a
novel statistical convolutional neural network (SCNN), which extends existing
CNN architectures but operates directly on correlated distributions rather than
deterministic numbers. By introducing a parameterized canonical model to model
correlated data and defining corresponding operations as required for CNN
training and inference, we show that SCNN can process multiple frames of
correlated images effectively, hence achieving significant speedup over
existing CNN models. We use a CNN based video object detection as an example to
illustrate the usefulness of the proposed SCNN as a general network model.
Experimental results show that even a non-optimized implementation of SCNN can
still achieve 178% speedup over existing CNNs with slight accuracy degradation.
HexaShrink, an exact scalable framework for hexahedral meshes with attributes and discontinuities: multiresolution rendering and storage of geoscience models
Authors: Jean-Luc Peyrot, Laurent Duval, Frédéric Payan, Lauriane Bouard, Lénaïc Chizat, Sébastien Schneider, Marc Antonini
Date: 2019-03-16 00:00:00
Tasks: Attribute

With huge data acquisition progresses realized in the past decades and acquisition systems now able to produce high resolution grids and point clouds, the digitization of physical terrains becomes increasingly more precise. Such extreme quantities of generated and modeled data greatly impact computational performances on many levels of high-performance computing (HPC): storage media, memory requirements, transfer capability, and finally simulation interactivity, necessary to exploit this instance of big data. Efficient representations and storage are thus becoming "enabling technologies'' in HPC experimental and simulation science. We propose HexaShrink, an original decomposition scheme for structured hexahedral volume meshes. The latter are used for instance in biomedical engineering, materials science, or geosciences. HexaShrink provides a comprehensive framework allowing efficient mesh visualization and storage. Its exactly reversible multiresolution decomposition yields a hierarchy of meshes of increasing levels of details, in terms of either geometry, continuous or categorical properties of cells. Starting with an overview of volume meshes compression techniques, our contribution blends coherently different multiresolution wavelet schemes in different dimensions. It results in a global framework preserving discontinuities (faults) across scales, implemented as a fully reversible upscaling at different resolutions. Experimental results are provided on meshes of varying size and complexity. They emphasize the consistency of the proposed representation, in terms of visualization, attribute downsampling and distribution at different resolutions. Finally, HexaShrink yields gains in storage space when combined to lossless compression techniques.
Tackling Initial Centroid of K-Means with Distance Part (DP-KMeans)
Authors: Ahmad Ilham, Danny Ibrahim, Luqman Assaffat, Achmad Solichan
Date: 2019-03-15 00:00:00
Tasks: Clustering

The initial centroid is a fairly challenging problem in the k-means method
because it can affect the clustering results. In addition, choosing the
starting centroid of the cluster is not always appropriate, especially, when
the number of groups increases.
Twins Recognition with Multi Biometric System: Handcrafted-Deep Learning Based Multi Algorithm with Voice-Ear Recognition Based Multi Modal
Authors: Cihan Akin, Umit Kacar, Murvet Kirci
Date: 2019-03-15 00:00:00

With the development of technology, the usage areas and importance of
biometric systems have started to increase. Since the characteristics of each
person are different from each other, a single model biometric system can yield
successful results. However, because the characteristics of twin people are
very close to each other, multiple biometric systems including multiple
characteristics of individuals will be more appropriate and will increase the
recognition rate. In this study, a multiple biometric recognition system
consisting of a combination of multiple algorithms and multiple models was
developed to distinguish people from other people and their twins. Ear and
voice biometric data were used for the multimodal model and 38 pair of twin ear
images and sound recordings were used in the data set. Sound and ear
recognition rates were obtained using classical (hand-crafted) and deep
learning algorithms. The results obtained were combined with the score level
fusion method to achieve a success rate of 94.74% in rank-1 and 100% in rank
-2.
Wikipedia Edit Number Prediction based on Temporal Dynamics Only
Authors: Dell Zhang
Date: 2011-10-23 00:00:00
Tasks: Prediction, Self-Supervised Learning

In this paper, we describe our approach to the Wikipedia Participation
Challenge which aims to predict the number of edits a Wikipedia editor will
make in the next 5 months. The best submission from our team, "zeditor",
achieved 41.7% improvement over WMF's baseline predictive model and the final
rank of 3rd place among 96 teams. An interesting characteristic of our approach
is that only temporal dynamics features (i.e., how the number of edits changes
in recent periods, etc.) are used in a self-supervised learning framework,
which makes it easy to be generalised to other application domains.
Photometric Mesh Optimization for Video-Aligned 3D Object Reconstruction
Authors: Chen-Hsuan Lin, Oliver Wang, Bryan C. Russell, Eli Shechtman, Vladimir G. Kim, Matthew Fisher, Simon Lucey
Date: 2019-03-20 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Object Reconstruction, 3D Reconstruction, Object, Object Reconstruction, Surface Reconstruction

In this paper, we address the problem of 3D object mesh reconstruction from
RGB videos. Our approach combines the best of multi-view geometric and
data-driven methods for 3D reconstruction by optimizing object meshes for
multi-view photometric consistency while constraining mesh deformations with a
shape prior. We pose this as a piecewise image alignment problem for each mesh
face projection. Our approach allows us to update shape parameters from the
photometric error without any depth or mask information. Moreover, we show how
to avoid a degeneracy of zero photometric gradients via rasterizing from a
virtual viewpoint. We demonstrate 3D object mesh reconstruction results from
both synthetic and real-world videos with our photometric mesh optimization,
which is unachievable with either na\"ive mesh generation networks or
traditional pipelines of surface reconstruction without heavy manual
post-processing.
Single Image Deraining: A Comprehensive Benchmark Analysis
Authors: Siyuan Li, Iago Breno Araujo, Wenqi Ren, Zhangyang Wang, Eric K. Tokuda, Roberto Hirata Junior, Roberto Cesar-Junior, Jiawan Zhang, Xiaojie Guo, Xiaochun Cao
Date: 2019-03-20 00:00:00
Proceeding: CVPR 2019 6
Tasks: Rain Removal, Single Image Deraining

We present a comprehensive study and evaluation of existing single image
deraining algorithms, using a new large-scale benchmark consisting of both
synthetic and real-world rainy images.This dataset highlights diverse data
sources and image contents, and is divided into three subsets (rain streak,
rain drop, rain and mist), each serving different training or evaluation
purposes. We further provide a rich variety of criteria for dehazing algorithm
evaluation, ranging from full-reference metrics, to no-reference metrics, to
subjective evaluation and the novel task-driven evaluation. Experiments on the
dataset shed light on the comparisons and limitations of state-of-the-art
deraining algorithms, and suggest promising future directions.
TATi-Thermodynamic Analytics ToolkIt: TensorFlow-based software for posterior sampling in machine learning applications
Authors: Frederik Heber, Zofia Trstanova, Benedict Leimkuhler
Date: 2019-03-20 00:00:00
Tasks: BIG-bench Machine Learning, GPU

With the advent of GPU-assisted hardware and maturing high-efficiency software platforms such as TensorFlow and PyTorch, Bayesian posterior sampling for neural networks becomes plausible. In this article we discuss Bayesian parametrization in machine learning based on Markov Chain Monte Carlo methods, specifically discretized stochastic differential equations such as Langevin dynamics and extended system methods in which an ensemble of walkers is employed to enhance sampling. We provide a glimpse of the potential of the sampling-intensive approach by studying (and visualizing) the loss landscape of a neural network applied to the MNIST data set. Moreover, we investigate how the sampling efficiency itself can be significantly enhanced through an ensemble quasi-Newton preconditioning method. This article accompanies the release of a new TensorFlow software package, the Thermodynamic Analytics ToolkIt, which is used in the computational experiments.
An Efficient Schmidt-EKF for 3D Visual-Inertial SLAM
Authors: Patrick Geneva, James Maley, Guoquan Huang
Date: 2019-03-20 00:00:00
Proceeding: CVPR 2019 6

It holds great implications for practical applications to enable
centimeter-accuracy positioning for mobile and wearable sensor systems. In this
paper, we propose a novel, high-precision, efficient visual-inertial (VI)-SLAM
algorithm, termed Schmidt-EKF VI-SLAM (SEVIS), which optimally fuses IMU
measurements and monocular images in a tightly-coupled manner to provide 3D
motion tracking with bounded error. In particular, we adapt the Schmidt Kalman
filter formulation to selectively include informative features in the state
vector while treating them as nuisance parameters (or Schmidt states) once they
become matured. This change in modeling allows for significant computational
savings by no longer needing to constantly update the Schmidt states (or their
covariance), while still allowing the EKF to correctly account for their
cross-correlations with the active states. As a result, we achieve linear
computational complexity in terms of map size, instead of quadratic as in the
standard SLAM systems. In order to fully exploit the map information to bound
navigation drifts, we advocate efficient keyframe-aided 2D-to-2D feature
matching to find reliable correspondences between current 2D visual
measurements and 3D map features. The proposed SEVIS is extensively validated
in both simulations and experiments.
Column2Vec: Structural Understanding via Distributed Representations of Database Schemas
Authors: Michael J. Mior, Alexander G. Ororbia II
Date: 2019-03-20 00:00:00

We present Column2Vec, a distributed representation of database columns based
on column metadata. Our distributed representation has several applications.
Using known names for groups of columns (i.e., a table name), we train a model
to generate an appropriate name for columns in an unnamed table. We demonstrate
the viability of our approach using schema information collected from open
source applications on GitHub.
The importance of better models in stochastic optimization
Authors: Hilal Asi, John C. Duchi
Date: 2019-03-20 00:00:00
Tasks: Stochastic Optimization

Standard stochastic optimization methods are brittle, sensitive to stepsize
choices and other algorithmic parameters, and they exhibit instability outside
of well-behaved families of objectives. To address these challenges, we
investigate models for stochastic minimization and learning problems that
exhibit better robustness to problem families and algorithmic parameters. With
appropriately accurate models---which we call the aProx family---stochastic
methods can be made stable, provably convergent and asymptotically optimal;
even modeling that the objective is nonnegative is sufficient for this
stability. We extend these results beyond convexity to weakly convex
objectives, which include compositions of convex losses with smooth functions
common in modern machine learning applications. We highlight the importance of
robustness and accurate modeling with a careful experimental evaluation of
convergence time and algorithm sensitivity.
Plug and play methods for magnetic resonance imaging (long version)
Authors: Rizwan Ahmad, Charles A. Bouman, Gregery T. Buzzard, Stanley Chan, Sizhou Liu, Edward T. Reehorst, Philip Schniter
Date: 2019-03-20 00:00:00
Tasks: Denoising, Diagnostic, Image Denoising, Image Reconstruction

Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic tool that provides excellent soft-tissue contrast without the use of ionizing radiation. Compared to other clinical imaging modalities (e.g., CT or ultrasound), however, the data acquisition process for MRI is inherently slow, which motivates undersampling and thus drives the need for accurate, efficient reconstruction methods from undersampled datasets. In this article, we describe the use of "plug-and-play" (PnP) algorithms for MRI image recovery. We first describe the linearly approximated inverse problem encountered in MRI. Then we review several PnP methods, where the unifying commonality is to iteratively call a denoising subroutine as one step of a larger optimization-inspired algorithm. Next, we describe how the result of the PnP method can be interpreted as a solution to an equilibrium equation, allowing convergence analysis from the equilibrium perspective. Finally, we present illustrative examples of PnP methods applied to MRI image recovery.
Single-step Options for Adversary Driving
Authors: Nazmus Sakib, Hengshuai Yao, Hong Zhang, Shangling Jui
Date: 2019-03-20 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we use reinforcement learning for safety driving in adversary settings. In our work, the knowledge in state-of-art planning methods is reused by single-step options whose action suggestions are compared in parallel with primitive actions. We show two advantages by doing so. First, training this reinforcement learning agent is easier and faster than training the primitive-action agent. Second, our new agent outperforms the primitive-action reinforcement learning agent, human testers as well as the state-of-art planning methods that our agent queries as skill options.