pwc-archive/papers-with-abstracts huggingface dataset:
Infinite-Horizon Gaussian Processes
Authors: Arno Solin, James Hensman, Richard E. Turner
Date: 2018-11-15 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Gaussian Processes

Gaussian processes provide a flexible framework for forecasting, removing
noise, and interpreting long temporal datasets. State space modelling (Kalman
filtering) enables these non-parametric models to be deployed on long datasets
by reducing the complexity to linear in the number of data points. The
complexity is still cubic in the state dimension $m$ which is an impediment to
practical application. In certain special cases (Gaussian likelihood, regular
spacing) the GP posterior will reach a steady posterior state when the data are
very long. We leverage this and formulate an inference scheme for GPs with
general likelihoods, where inference is based on single-sweep EP (assumed
density filtering). The infinite-horizon model tackles the cubic cost in the
state dimensionality and reduces the cost in the state dimension $m$ to
$\mathcal{O}(m^2)$ per data point. The model is extended to online-learning of
hyperparameters. We show examples for large finite-length modelling problems,
and present how the method runs in real-time on a smartphone on a continuous
data stream updated at 100~Hz.
CAN: Composite Appearance Network for Person Tracking and How to Model Errors in a Tracking System
Authors: Neeti Narayan, Nishant Sankaran, Srirangaraj Setlur, Venu Govindaraju
Date: 2018-11-15 00:00:00
Tasks: Person Re-Identification

Tracking multiple people across multiple cameras is an open problem. It is typically divided into two tasks: (i) single-camera tracking (SCT) - identify trajectories in the same scene, and (ii) inter-camera tracking (ICT) - identify trajectories across cameras for real surveillance scenes. Many methods cater to SCT, while ICT still remains a challenge. In this paper, we propose a tracking method which uses motion cues and a feature aggregation network for template-based person re-identification by incorporating metadata such as person bounding box and camera information. We present a feature aggregation architecture called Composite Appearance Network (CAN) to address the above problem. The key structure of this architecture is called EvalNet that pays attention to each feature vector and learns to weight them based on gradients it receives for the overall template for optimal re-identification performance. We demonstrate the efficiency of our approach with experiments on the challenging multi-camera tracking dataset, DukeMTMC. We also survey existing tracking measures and present an online error metric called "Inference Error" (IE) that provides a better estimate of tracking/re-identification error, by treating SCT and ICT errors uniformly.
Subspace Clustering through Sub-Clusters
Authors: Weiwei Li, Jan Hannig, Sayan Mukherjee
Date: 2018-11-15 00:00:00
Tasks: Clustering, Dimensionality Reduction

The problem of dimension reduction is of increasing importance in modern data analysis. In this paper, we consider modeling the collection of points in a high dimensional space as a union of low dimensional subspaces. In particular we propose a highly scalable sampling based algorithm that clusters the entire data via first spectral clustering of a small random sample followed by classifying or labeling the remaining out of sample points. The key idea is that this random subset borrows information across the entire data set and that the problem of clustering points can be replaced with the more efficient and robust problem of "clustering sub-clusters". We provide theoretical guarantees for our procedure. The numerical results indicate we outperform other state-of-the-art subspace clustering algorithms with respect to accuracy and speed.
Stable Tensor Neural Networks for Rapid Deep Learning
Authors: Elizabeth Newman, Lior Horesh, Haim Avron, Misha Kilmer
Date: 2018-11-15 00:00:00
Tasks: Deep Learning

We propose a tensor neural network ($t$-NN) framework that offers an exciting
new paradigm for designing neural networks with multidimensional (tensor) data.
Our network architecture is based on the $t$-product (Kilmer and Martin, 2011),
an algebraic formulation to multiply tensors via circulant convolution. In this
$t$-product algebra, we interpret tensors as $t$-linear operators analogous to
matrices as linear operators, and hence our framework inherits mimetic matrix
properties. To exemplify the elegant, matrix-mimetic algebraic structure of our
$t$-NNs, we expand on recent work (Haber and Ruthotto, 2017) which interprets
deep neural networks as discretizations of non-linear differential equations
and introduces stable neural networks which promote superior generalization.
Motivated by this dynamic framework, we introduce a stable $t$-NN which
facilitates more rapid learning because of its reduced, more powerful
parameterization. Through our high-dimensional design, we create a more compact
parameter space and extract multidimensional correlations otherwise latent in
traditional algorithms. We further generalize our $t$-NN framework to a family
of tensor-tensor products (Kernfeld, Kilmer, and Aeron, 2015) which still
induce a matrix-mimetic algebraic structure. Through numerical experiments on
the MNIST and CIFAR-10 datasets, we demonstrate the more powerful
parameterizations and improved generalizability of stable $t$-NNs.
Automatic Text Document Summarization using Semantic-based Analysis
Authors: Chandra Shekhar Yadav
Date: 2018-11-15 00:00:00
Tasks: Document Summarization, Extractive Summarization

Since the advent of the web, the amount of data on wen has been increased
several million folds. In recent years web data generated is more than data
stored for years. One important data format is text. To answer user queries
over the internet, and to overcome the problem of information overload one
possible solution is text document summarization. This not only reduces query
access time, but also optimize the document results according to specific users
requirements. Summarization of text document can be categorized as abstractive
and extractive. Most of the work has been done in the direction of Extractive
summarization. Extractive summarized result is a subset of original documents
with the objective of more content coverage and lea redundancy. Our work is
based on Extractive approaches. In the first approach, we are using some
statistical features and semantic-based features. To include sentiment as a
feature is an idea cached from a view that emotion plays an important role. It
effectively conveys a message. So, it may play a vital role in text document
summarization.
Seq2Seq Mimic Games: A Signaling Perspective
Authors: Juan Leni, John Levine, John Quigley
Date: 2018-11-15 00:00:00

We study the emergence of communication in multiagent adversarial settings
inspired by the classic Imitation game. A class of three player games is used
to explore how agents based on sequence to sequence (Seq2Seq) models can learn
to communicate information in adversarial settings. We propose a modeling
approach, an initial set of experiments and use signaling theory to support our
analysis. In addition, we describe how we operationalize the learning process
of actor-critic Seq2Seq based agents in these communicational games.
High Granular Operator Spaces, and Less-Contaminated General Rough Mereologies
Authors: A. Mani
Date: 2018-11-15 00:00:00
Tasks: Decision Making, Vocal Bursts Intensity Prediction

Granular operator spaces and variants had been introduced and used in theoretical investigations on the foundations of general rough sets by the present author over the last few years. In this research, higher order versions of these are presented uniformly as partial algebraic systems. They are also adapted for practical applications when the data is representable by data table-like structures according to a minimalist schema for avoiding contamination. Issues relating to valuations used in information systems or tables are also addressed. The concept of contamination introduced and studied by the present author across a number of her papers, concerns mixing up of information across semantic domains (or domains of discourse). Rough inclusion functions (\textsf{RIF}s), variants, and numeric functions often have a direct or indirect role in contaminating algorithms. Some solutions that seek to replace or avoid them have been proposed and investigated by the present author in some of her earlier papers. Because multiple kinds of solution are of interest to the contamination problem, granular generalizations of RIFs are proposed, and investigated. Interesting representation results are proved and a core algebraic strategy for generalizing Skowron-Polkowski style of rough mereology (though for a very different purpose) is formulated. A number of examples have been added to illustrate key parts of the proposal in higher order variants of granular operator spaces. Further algorithms grounded in mereological nearness, suited for decision-making in human-machine interaction contexts, are proposed by the present author. Applications of granular \textsf{RIF}s to partial/soft solutions of the inverse problem are also invented in this paper.
The Trace Criterion for Kernel Bandwidth Selection for Support Vector Data Description
Authors: Arin Chaudhuri, Carol Sadek, Deovrat Kakde, Wenhao Hu, Hansi Jiang, Seunghyun Kong, Yuewei Liao, Sergiy Peredriy, Haoyu Wang
Date: 2018-11-15 00:00:00
Tasks: Anomaly Detection

Support vector data description (SVDD) is a popular anomaly detection technique. The SVDD classifier partitions the whole data space into an inlier region, which consists of the region near the training data, and an outlier region, which consists of points away from the training data. The computation of the SVDD classifier requires a kernel function, for which the Gaussian kernel is a common choice. The Gaussian kernel has a bandwidth parameter, and it is important to set the value of this parameter correctly for good results. A small bandwidth leads to overfitting such that the resulting SVDD classifier overestimates the number of anomalies, whereas a large bandwidth leads to underfitting and an inability to detect many anomalies. In this paper, we present a new unsupervised method for selecting the Gaussian kernel bandwidth. Our method exploits a low-rank representation of the kernel matrix to suggest a kernel bandwidth value. Our new technique is competitive with the current state of the art for low-dimensional data and performs extremely well for many classes of high-dimensional data. Because the mathematical formulation of SVDD is identical with the mathematical formulation of one-class support vector machines (OCSVM) when the Gaussian kernel is used, our method is equally applicable to Gaussian kernel bandwidth tuning for OCSVM.
Improving Fingerprint Pore Detection with a Small FCN
Authors: Gabriel Dahia, Maurício Pamplona Segundo
Date: 2018-11-14 00:00:00

In this work, we investigate if previously proposed CNNs for fingerprint pore
detection overestimate the number of required model parameters for this task.
We show that this is indeed the case by proposing a fully convolutional neural
network that has significantly fewer parameters. We evaluate this model using a
rigorous and reproducible protocol, which was, prior to our work, not available
to the community. Using our protocol, we show that the proposed model, when
combined with post-processing, performs better than previous methods, albeit
being much more efficient. All our code is available at
https://github.com/gdahia/fingerprint-pore-detection
Adversarial Unsupervised Representation Learning for Activity Time-Series
Authors: Karan Aggarwal, Shafiq Joty, Luis Fernandez-Luque, Jaideep Srivastava
Date: 2018-11-14 00:00:00
Tasks: Representation Learning, Time Series, Time Series Analysis

Sufficient physical activity and restful sleep play a major role in the
prevention and cure of many chronic conditions. Being able to proactively
screen and monitor such chronic conditions would be a big step forward for
overall health. The rapid increase in the popularity of wearable devices
provides a significant new source, making it possible to track the user's
lifestyle real-time. In this paper, we propose a novel unsupervised
representation learning technique called activity2vec that learns and
"summarizes" the discrete-valued activity time-series. It learns the
representations with three components: (i) the co-occurrence and magnitude of
the activity levels in a time-segment, (ii) neighboring context of the
time-segment, and (iii) promoting subject-invariance with adversarial training.
We evaluate our method on four disorder prediction tasks using linear
classifiers. Empirical evaluation demonstrates that our proposed method scales
and performs better than many strong baselines. The adversarial regime helps
improve the generalizability of our representations by promoting subject
invariant features. We also show that using the representations at the level of
a day works the best since human activity is structured in terms of daily
routines
Probabilistic Random Forest: A machine learning algorithm for noisy datasets
Authors: Itamar Reis, Dalya Baron, Sahar Shahaf
Date: 2018-11-14 00:00:00
Tasks: BIG-bench Machine Learning, Missing Values, Transfer Learning

Machine learning (ML) algorithms become increasingly important in the
analysis of astronomical data. However, since most ML algorithms are not
designed to take data uncertainties into account, ML based studies are mostly
restricted to data with high signal-to-noise ratio. Astronomical datasets of
such high-quality are uncommon. In this work we modify the long-established
Random Forest (RF) algorithm to take into account uncertainties in the
measurements (i.e., features) as well as in the assigned classes (i.e.,
labels). To do so, the Probabilistic Random Forest (PRF) algorithm treats the
features and labels as probability distribution functions, rather than
deterministic quantities. We perform a variety of experiments where we inject
different types of noise to a dataset, and compare the accuracy of the PRF to
that of RF. The PRF outperforms RF in all cases, with a moderate increase in
running time. We find an improvement in classification accuracy of up to 10% in
the case of noisy features, and up to 30% in the case of noisy labels. The PRF
accuracy decreased by less then 5% for a dataset with as many as 45%
misclassified objects, compared to a clean dataset. Apart from improving the
prediction accuracy in noisy datasets, the PRF naturally copes with missing
values in the data, and outperforms RF when applied to a dataset with different
noise characteristics in the training and test sets, suggesting that it can be
used for Transfer Learning.
A Grammar-Based Structural CNN Decoder for Code Generation
Authors: Zeyu Sun, Qihao Zhu, Lili Mou, Yingfei Xiong, Ge Li, Lu Zhang
Date: 2018-11-14 00:00:00
Tasks: Code Generation, Decoder, Semantic Parsing, Sentence

Code generation maps a program description to executable source code in a
programming language. Existing approaches mainly rely on a recurrent neural
network (RNN) as the decoder. However, we find that a program contains
significantly more tokens than a natural language sentence, and thus it may be
inappropriate for RNN to capture such a long sequence. In this paper, we
propose a grammar-based structural convolutional neural network (CNN) for code
generation. Our model generates a program by predicting the grammar rules of
the programming language; we design several CNN modules, including the
tree-based convolution and pre-order convolution, whose information is further
aggregated by dedicated attentive pooling layers. Experimental results on the
HearthStone benchmark dataset show that our CNN code generator significantly
outperforms the previous state-of-the-art method by 5 percentage points;
additional experiments on several semantic parsing tasks demonstrate the
robustness of our model. We also conduct in-depth ablation test to better
understand each component of our model.
SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects
Authors: Xue Yang, Jirui Yang, Junchi Yan, Yue Zhang, Tengfei Zhang, Zhi Guo, Sun Xian, Kun fu
Date: 2018-11-17 00:00:00
Proceeding: ICCV 2019 10
Tasks: object-detection, Object Detection, Object Detection In Aerial Images

Object detection has been a building block in computer vision. Though considerable progress has been made, there still exist challenges for objects with small size, arbitrary direction, and dense distribution. Apart from natural images, such issues are especially pronounced for aerial images of great importance. This paper presents a novel multi-category rotation detector for small, cluttered and rotated objects, namely SCRDet. Specifically, a sampling fusion network is devised which fuses multi-layer feature with effective anchor sampling, to improve the sensitivity to small objects. Meanwhile, the supervised pixel attention network and the channel attention network are jointly explored for small and cluttered object detection by suppressing the noise and highlighting the objects feature. For more accurate rotation estimation, the IoU constant factor is added to the smooth L1 loss to address the boundary problem for the rotating bounding box. Extensive experiments on two remote sensing public datasets DOTA, NWPU VHR-10 as well as natural image datasets COCO, VOC2007 and scene text data ICDAR2015 show the state-of-the-art performance of our detector. The code and models will be available at https://github.com/DetectionTeamUCAS.
Multi-Period Trading via Convex Optimization
Authors: Stephen Boyd, Enzo Busseti, Steven Diamond, Ronald N. Kahn, Kwangmoo Koh, Peter Nystrup, Jan Speth
Date: 2017-04-29 00:00:00
Tasks: Model Predictive Control

We consider a basic model of multi-period trading, which can be used to
evaluate the performance of a trading strategy. We describe a framework for
single-period optimization, where the trades in each period are found by
solving a convex optimization problem that trades off expected return, risk,
transaction cost and holding cost such as the borrowing cost for shorting
assets. We then describe a multi-period version of the trading method, where
optimization is used to plan a sequence of trades, with only the first one
executed, using estimates of future quantities that are unknown when the trades
are chosen. The single-period method traces back to Markowitz; the multi-period
methods trace back to model predictive control. Our contribution is to describe
the single-period and multi-period methods in one simple framework, giving a
clear description of the development and the approximations made. In this paper
we do not address a critical component in a trading algorithm, the predictions
or forecasts of future quantities. The methods we describe in this paper can be
thought of as good ways to exploit predictions, no matter how they are made. We
have also developed a companion open-source software library that implements
many of the ideas and methods described in the paper.
Deep Learning for Limit Order Books
Authors: Justin Sirignano
Date: 2016-01-08 00:00:00
Tasks: Deep Learning, Management, regression

This paper develops a new neural network architecture for modeling spatial
distributions (i.e., distributions on R^d) which is computationally efficient
and specifically designed to take advantage of the spatial structure of limit
order books. The new architecture yields a low-dimensional model of price
movements deep into the limit order book, allowing more effective use of
information from deep in the limit order book (i.e., many levels beyond the
best bid and best ask). This "spatial neural network" models the joint
distribution of the state of the limit order book at a future time conditional
on the current state of the limit order book. The spatial neural network
outperforms other models such as the naive empirical model, logistic regression
(with nonlinear features), and a standard neural network architecture. Both
neural networks strongly outperform the logistic regression model. Due to its
more effective use of information deep in the limit order book, the spatial
neural network especially outperforms the standard neural network in the tail
of the distribution, which is important for risk management applications. The
models are trained and tested on nearly 500 stocks. Techniques from deep
learning such as dropout are employed to improve performance. Due to the
significant computational challenges associated with the large amount of data,
models are trained with a cluster of 50 GPUs.
BAMBI: blind accelerated multimodal Bayesian inference
Authors: Philip Graff, Farhan Feroz, Michael P. Hobson, Anthony Lasenby
Date: 2011-10-13 00:00:00
Tasks: Bayesian Inference

In this paper we present an algorithm for rapid Bayesian analysis that
combines the benefits of nested sampling and artificial neural networks. The
blind accelerated multimodal Bayesian inference (BAMBI) algorithm implements
the MultiNest package for nested sampling as well as the training of an
artificial neural network (NN) to learn the likelihood function. In the case of
computationally expensive likelihoods, this allows the substitution of a much
more rapid approximation in order to increase significantly the speed of the
analysis. We begin by demonstrating, with a few toy examples, the ability of a
NN to learn complicated likelihood surfaces. BAMBI's ability to decrease
running time for Bayesian inference is then demonstrated in the context of
estimating cosmological parameters from Wilkinson Microwave Anisotropy Probe
and other observations. We show that valuable speed increases are achieved in
addition to obtaining NNs trained on the likelihood functions for the different
model and data combinations. These NNs can then be used for an even faster
follow-up analysis using the same likelihood and different priors. This is a
fully general algorithm that can be applied, without any pre-processing, to
other problems with computationally expensive likelihood functions.
Towards Better Understanding of Artifacts in Variant Calling from High-Coverage Samples
Authors: Heng Li
Date: 2014-04-03 00:00:00
Tasks: Articles

Motivation: Whole-genome high-coverage sequencing has been widely used for
personal and cancer genomics as well as in various research areas. However, in
the lack of an unbiased whole-genome truth set, the global error rate of
variant calls and the leading causal artifacts still remain unclear even given
the great efforts in the evaluation of variant calling methods.
  Results: We made ten SNP and INDEL call sets with two read mappers and five
variant callers, both on a haploid human genome and a diploid genome at a
similar coverage. By investigating false heterozygous calls in the haploid
genome, we identified the erroneous realignment in low-complexity regions and
the incomplete reference genome with respect to the sample as the two major
sources of errors, which press for continued improvements in these two areas.
We estimated that the error rate of raw genotype calls is as high as 1 in
10-15kb, but the error rate of post-filtered calls is reduced to 1 in 100-200kb
without significant compromise on the sensitivity.
  Availability: BWA-MEM alignment: http://bit.ly/1g8XqRt; Scripts:
https://github.com/lh3/varcmp; Additional data:
https://figshare.com/articles/Towards_better_understanding_of_artifacts_in_variating_calling_from_high_coverage_samples/981073