pwc-archive/papers-with-abstracts huggingface dataset:
Conformal k-NN Anomaly Detector for Univariate Data Streams
Authors: Vladislav Ishimtsev, Ivan Nazarov, Alexander Bernstein, Evgeny Burnaev
Date: 2017-06-11 00:00:00
Tasks: Anomaly Detection, Conformal Prediction, Prediction, Time Series, Time Series Analysis

Anomalies in time-series data give essential and often actionable information
in many applications. In this paper we consider a model-free anomaly detection
method for univariate time-series which adapts to non-stationarity in the data
stream and provides probabilistic abnormality scores based on the conformal
prediction paradigm. Despite its simplicity the method performs on par with
complex prediction-based models on the Numenta Anomaly Detection benchmark and
the Yahoo! S5 dataset.
Learning from Untrusted Data
Authors: Moses Charikar, Jacob Steinhardt, Gregory Valiant
Date: 2016-11-07 00:00:00
Tasks: Stochastic Optimization

The vast majority of theoretical results in machine learning and statistics
assume that the available training data is a reasonably reliable reflection of
the phenomena to be learned or estimated. Similarly, the majority of machine
learning and statistical techniques used in practice are brittle to the
presence of large amounts of biased or malicious data. In this work we consider
two frameworks in which to study estimation, learning, and optimization in the
presence of significant fractions of arbitrary data.
  The first framework, list-decodable learning, asks whether it is possible to
return a list of answers, with the guarantee that at least one of them is
accurate. For example, given a dataset of $n$ points for which an unknown
subset of $\alpha n$ points are drawn from a distribution of interest, and no
assumptions are made about the remaining $(1-\alpha)n$ points, is it possible
to return a list of $\operatorname{poly}(1/\alpha)$ answers, one of which is
correct? The second framework, which we term the semi-verified learning model,
considers the extent to which a small dataset of trusted data (drawn from the
distribution in question) can be leveraged to enable the accurate extraction of
information from a much larger but untrusted dataset (of which only an
$\alpha$-fraction is drawn from the distribution).
  We show strong positive results in both settings, and provide an algorithm
for robust learning in a very general stochastic optimization setting. This
general result has immediate implications for robust estimation in a number of
settings, including for robustly estimating the mean of distributions with
bounded second moments, robustly learning mixtures of such distributions, and
robustly finding planted partitions in random graphs in which significant
portions of the graph have been perturbed by an adversary.
Robust features for facial action recognition
Authors: Nadav Israel, Lior Wolf, Ran Barzilay, Gal Shoval
Date: 2017-02-05 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Automatic recognition of facial gestures is becoming increasingly important
as real world AI agents become a reality. In this paper, we present an
automated system that recognizes facial gestures by capturing local changes and
encoding the motion into a histogram of frequencies. We evaluate the proposed
method by demonstrating its effectiveness on spontaneous face action
benchmarks: the FEEDTUM dataset, the Pain dataset and the HMDB51 dataset. The
results show that, compared to known methods, the new encoding methods
significantly improve the recognition accuracy and the robustness of analysis
for a variety of applications.
Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)
Authors: Hu Chen, Yi Zhang, Mannudeep K. Kalra, Feng Lin, Yang Chen, Peixi Liao, Jiliu Zhou, Ge Wang
Date: 2017-02-01 00:00:00
Tasks: Decoder, Lesion Detection

Given the potential X-ray radiation risk to the patient, low-dose CT has
attracted a considerable interest in the medical imaging field. The current
main stream low-dose CT methods include vendor-specific sinogram domain
filtration and iterative reconstruction, but they need to access original raw
data whose formats are not transparent to most users. Due to the difficulty of
modeling the statistical characteristics in the image domain, the existing
methods for directly processing reconstructed images cannot eliminate image
noise very well while keeping structural details. Inspired by the idea of deep
learning, here we combine the autoencoder, the deconvolution network, and
shortcut connections into the residual encoder-decoder convolutional neural
network (RED-CNN) for low-dose CT imaging. After patch-based training, the
proposed RED-CNN achieves a competitive performance relative to
the-state-of-art methods in both simulated and clinical cases. Especially, our
method has been favorably evaluated in terms of noise suppression, structural
preservation and lesion detection.
Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms
Authors: Changzhe Jiao, Bo-Yu Su, Princess Lyons, Alina Zare, K. C. Ho, Marjorie Skubic
Date: 2017-06-11 00:00:00
Tasks: Dictionary Learning, Heart rate estimation, Multiple Instance Learning

A multiple instance dictionary learning approach, Dictionary Learning using
Functions of Multiple Instances (DL-FUMI), is used to perform beat-to-beat
heart rate estimation and to characterize heartbeat signatures from
ballistocardiogram (BCG) signals collected with a hydraulic bed sensor. DL-FUMI
estimates a "heartbeat concept" that represents an individual's personal
ballistocardiogram heartbeat pattern. DL-FUMI formulates heartbeat detection
and heartbeat characterization as a multiple instance learning problem to
address the uncertainty inherent in aligning BCG signals with ground truth
during training. Experimental results show that the estimated heartbeat concept
found by DL-FUMI is an effective heartbeat prototype and achieves superior
performance over comparison algorithms.
A dynamic graph-cuts method with integrated multiple feature maps for segmenting kidneys in ultrasound images
Authors: Qiang Zheng, Steven Warner, Gregory Tasian, Yong Fan
Date: 2017-06-11 00:00:00
Tasks: Computational Efficiency, Image Segmentation, Segmentation, Semantic Segmentation

Purpose: To improve kidney segmentation in clinical ultrasound (US) images,
we develop a new graph cuts based method to segment kidney US images by
integrating original image intensity information and texture feature maps
extracted using Gabor filters. Methods: To handle large appearance variation
within kidney images and improve computational efficiency, we build a graph of
image pixels close to kidney boundary instead of building a graph of the whole
image. To make the kidney segmentation robust to weak boundaries, we adopt
localized regional information to measure similarity between image pixels for
computing edge weights to build the graph of image pixels. The localized graph
is dynamically updated and the GC based segmentation iteratively progresses
until convergence. The proposed method has been evaluated and compared with
state of the art image segmentation methods based on clinical kidney US images
of 85 subjects. We randomly selected US images of 20 subjects as training data
for tuning the parameters, and validated the methods based on US images of the
remaining 65 subjects. The segmentation results have been quantitatively
analyzed using 3 metrics, including Dice Index, Jaccard Index, and Mean
Distance. Results: Experiment results demonstrated that the proposed method
obtained segmentation results for bilateral kidneys of 65 subjects with average
Dice index of 0.9581, Jaccard index of 0.9204, and Mean Distance of 1.7166,
better than other methods under comparison (p<10-19, paired Wilcoxon rank sum
tests). Conclusions: The proposed method achieved promising performance for
segmenting kidneys in US images, better than segmentation methods that built on
any single channel of image information. This method will facilitate extraction
of kidney characteristics that may predict important clinical outcomes such
progression chronic kidney disease.
On the Sampling Problem for Kernel Quadrature
Authors: Francois-Xavier Briol, Chris. J. Oates, Jon Cockayne, Wilson Ye Chen, Mark Girolami
Date: 2017-06-11 00:00:00
Proceeding: ICML 2017 8
Tasks: Numerical Integration

The standard Kernel Quadrature method for numerical integration with random
point sets (also called Bayesian Monte Carlo) is known to converge in root mean
square error at a rate determined by the ratio $s/d$, where $s$ and $d$ encode
the smoothness and dimension of the integrand. However, an empirical
investigation reveals that the rate constant $C$ is highly sensitive to the
distribution of the random points. In contrast to standard Monte Carlo
integration, for which optimal importance sampling is well-understood, the
sampling distribution that minimises $C$ for Kernel Quadrature does not admit a
closed form. This paper argues that the practical choice of sampling
distribution is an important open problem. One solution is considered; a novel
automatic approach based on adaptive tempering and sequential Monte Carlo.
Empirical results demonstrate a dramatic reduction in integration error of up
to 4 orders of magnitude can be achieved with the proposed method.
A Full Non-Monotonic Transition System for Unrestricted Non-Projective Parsing
Authors: Daniel Fernández-González, Carlos Gómez-Rodríguez
Date: 2017-06-11 00:00:00
Tasks: ARC

Restricted non-monotonicity has been shown beneficial for the projective
arc-eager dependency parser in previous research, as posterior decisions can
repair mistakes made in previous states due to the lack of information. In this
paper, we propose a novel, fully non-monotonic transition system based on the
non-projective Covington algorithm. As a non-monotonic system requires
exploration of erroneous actions during the training process, we develop
several non-monotonic variants of the recently defined dynamic oracle for the
Covington parser, based on tight approximations of the loss. Experiments on
datasets from the CoNLL-X and CoNLL-XI shared tasks show that a non-monotonic
dynamic oracle outperforms the monotonic version in the majority of languages.
Deep Tensor Convolution on Multicores
Authors: David Budden, Alexander Matveev, Shibani Santurkar, Shraman Ray Chaudhuri, Nir Shavit
Date: 2016-11-20 00:00:00
Proceeding: ICML 2017 8
Tasks: CPU, GPU

Deep convolutional neural networks (ConvNets) of 3-dimensional kernels allow
joint modeling of spatiotemporal features. These networks have improved
performance of video and volumetric image analysis, but have been limited in
size due to the low memory ceiling of GPU hardware. Existing CPU
implementations overcome this constraint but are impractically slow. Here we
extend and optimize the faster Winograd-class of convolutional algorithms to
the $N$-dimensional case and specifically for CPU hardware. First, we remove
the need to manually hand-craft algorithms by exploiting the relaxed
constraints and cheap sparse access of CPU memory. Second, we maximize CPU
utilization and multicore scalability by transforming data matrices to be
cache-aware, integer multiples of AVX vector widths. Treating 2-dimensional
ConvNets as a special (and the least beneficial) case of our approach, we
demonstrate a 5 to 25-fold improvement in throughput compared to previous
state-of-the-art.
Generic Axiomatization of Families of Noncrossing Graphs in Dependency Parsing
Authors: Anssi Yli-Jyrä, Carlos Gómez-Rodríguez
Date: 2017-06-11 00:00:00
Tasks: Dependency Parsing, Semantic Parsing

We present a simple encoding for unlabeled noncrossing graphs and show how
its latent counterpart helps us to represent several families of directed and
undirected graphs used in syntactic and semantic parsing of natural language as
context-free languages. The families are separated purely on the basis of
forbidden patterns in latent encoding, eliminating the need to differentiate
the families of non-crossing graphs in inference algorithms: one algorithm
works for all when the search space can be controlled in parser input.
Meta learning Framework for Automated Driving
Authors: Ahmad El Sallab, Mahmoud Saeed, Omar Abdel Tawab, Mohammed Abdou
Date: 2017-06-11 00:00:00
Tasks: Imitation Learning, Meta-Learning, Reinforcement Learning

The success of automated driving deployment is highly depending on the
ability to develop an efficient and safe driving policy. The problem is well
formulated under the framework of optimal control as a cost optimization
problem. Model based solutions using traditional planning are efficient, but
require the knowledge of the environment model. On the other hand, model free
solutions suffer sample inefficiency and require too many interactions with the
environment, which is infeasible in practice. Methods under the Reinforcement
Learning framework usually require the notion of a reward function, which is
not available in the real world. Imitation learning helps in improving sample
efficiency by introducing prior knowledge obtained from the demonstrated
behavior, on the risk of exact behavior cloning without generalizing to unseen
environments. In this paper we propose a Meta learning framework, based on data
set aggregation, to improve generalization of imitation learning algorithms.
Under the proposed framework, we propose MetaDAgger, a novel algorithm which
tackles the generalization issues in traditional imitation learning. We use The
Open Race Car Simulator (TORCS) to test our algorithm. Results on unseen test
tracks show significant improvement over traditional imitation learning
algorithms, improving the learning time and sample efficiency in the same time.
The results are also supported by visualization of the learnt features to prove
generalization of the captured details.
SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine
Authors: Matthew Dunn, Levent Sagun, Mike Higgins, V. Ugur Guney, Volkan Cirik, Kyunghyun Cho
Date: 2017-04-18 00:00:00
Tasks: Open-Domain Question Answering, Question Answering, Reading Comprehension

We publicly release a new large-scale dataset, called SearchQA, for machine
comprehension, or question-answering. Unlike recently released datasets, such
as DeepMind CNN/DailyMail and SQuAD, the proposed SearchQA was constructed to
reflect a full pipeline of general question-answering. That is, we start not
from an existing article and generate a question-answer pair, but start from an
existing question-answer pair, crawled from J! Archive, and augment it with
text snippets retrieved by Google. Following this approach, we built SearchQA,
which consists of more than 140k question-answer pairs with each pair having
49.6 snippets on average. Each question-answer-context tuple of the SearchQA
comes with additional meta-data such as the snippet's URL, which we believe
will be valuable resources for future research. We conduct human evaluation as
well as test two baseline methods, one simple word selection and the other deep
learning based, on the SearchQA. We show that there is a meaningful gap between
the human and machine performances. This suggests that the proposed dataset
could well serve as a benchmark for question-answering.
Large-Scale Evolution of Image Classifiers
Authors: Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc Le, Alex Kurakin
Date: 2017-03-03 00:00:00
Proceeding: ICML 2017 8
Tasks: Evolutionary Algorithms, Hyperparameter Optimization, image-classification, Image Classification, Navigate, Neural Architecture Search

Neural networks have proven effective at solving difficult problems but
designing their architectures can be challenging, even for image classification
problems alone. Our goal is to minimize human participation, so we employ
evolutionary algorithms to discover such networks automatically. Despite
significant computational requirements, we show that it is now possible to
evolve models with accuracies within the range of those published in the last
year. Specifically, we employ simple evolutionary techniques at unprecedented
scales to discover models for the CIFAR-10 and CIFAR-100 datasets, starting
from trivial initial conditions and reaching accuracies of 94.6% (95.6% for
ensemble) and 77.0%, respectively. To do this, we use novel and intuitive
mutation operators that navigate large search spaces; we stress that no human
participation is required once evolution starts and that the output is a
fully-trained model. Throughout this work, we place special emphasis on the
repeatability of results, the variability in the outcomes and the computational
requirements.
Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning Possible
Authors: Kai Zheng, Wenlong Mou, Li-Wei Wang
Date: 2017-06-11 00:00:00
Proceeding: ICML 2017 8
Tasks: regression

Non-interactive Local Differential Privacy (LDP) requires data analysts to
collect data from users through noisy channel at once. In this paper, we extend
the frontiers of Non-interactive LDP learning and estimation from several
aspects. For learning with smooth generalized linear losses, we propose an
approximate stochastic gradient oracle estimated from non-interactive LDP
channel, using Chebyshev expansion. Combined with inexact gradient methods, we
obtain an efficient algorithm with quasi-polynomial sample complexity bound.
For the high-dimensional world, we discover that under $\ell_2$-norm assumption
on data points, high-dimensional sparse linear regression and mean estimation
can be achieved with logarithmic dependence on dimension, using random
projection and approximate recovery. We also extend our methods to Kernel Ridge
Regression. Our work is the first one that makes learning and estimation
possible for a broad range of learning tasks under non-interactive LDP model.
Bicycle Detection Based On Multi-feature and Multi-frame Fusion in low-resolution traffic videos
Authors: Yi-Cheng Zhang, Qiang Ling
Date: 2017-06-11 00:00:00

As a major type of transportation equipments, bicycles, including electrical
bicycles, are distributed almost everywhere in China. The accidents caused by
bicycles have become a serious threat to the public safety. So bicycle
detection is one major task of traffic video surveillance systems in China. In
this paper, a method based on multi-feature and multi-frame fusion is presented
for bicycle detection in low-resolution traffic videos. It first extracts some
geometric features of objects from each frame image, then concatenate multiple
features into a feature vector and use linear support vector machine (SVM) to
learn a classifier, or put these features into a cascade classifier, to yield a
preliminary detection result regarding whether an object is a bicycle. It
further fuses these preliminary detection results from multiple frames to
provide a more reliable detection decision, together with a confidence level of
that decision. Experimental results show that this method based on
multi-feature and multi-frame fusion can identify bicycles with high accuracy
and low computational complexity. It is, therefore, applicable for real-time
traffic video surveillance systems.
RSI-CB: A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data
Authors: Haifeng Li, Xin Dou, Chao Tao, Zhixiang Hou, Jie Chen, Jian Peng, Min Deng, Ling Zhao
Date: 2017-05-30 00:00:00
Tasks: Classification, General Classification, GPU, image-classification, Image Classification, Remote Sensing Image Classification

In recent years, deep convolutional neural network (DCNN) has seen a breakthrough progress in natural image recognition because of three points: universal approximation ability via DCNN, large-scale database (such as ImageNet), and supercomputing ability powered by GPU. The remote sensing field is still lacking a large-scale benchmark compared to ImageNet and Place2. In this paper, we propose a remote sensing image classification benchmark (RSI-CB) based on massive, scalable, and diverse crowdsource data. Using crowdsource data, such as Open Street Map (OSM) data, ground objects in remote sensing images can be annotated effectively by points of interest, vector data from OSM, or other crowdsource data. The annotated images can be used in remote sensing image classification tasks. Based on this method, we construct a worldwide large-scale benchmark for remote sensing image classification. This benchmark has two sub-datasets with 256 by 256 and 128 by 128 sizes because different DCNNs require different image sizes. The former contains 6 categories with 35 subclasses of more than 24,000 images. The latter contains 6 categories with 45 subclasses of more than 36,000 images. This classification system of ground objects is defined according to the national standard of land-use classification in China and is inspired by the hierarchy mechanism of ImageNet. Finally, we conduct many experiments to compare RSI-CB with the SAT-4, SAT-6, and UC-Merced datasets on handcrafted features, such as scale-invariant feature transform, color histogram, local binary patterns, and GIST, and classical DCNN models, such as AlexNet, VGGNet, GoogLeNet, and ResNet.
Deep Optimization for Spectrum Repacking
Authors: Neil Newman, Alexandre Fréchette, Kevin Leyton-Brown
Date: 2017-06-11 00:00:00

Over 13 months in 2016-17 the FCC conducted an "incentive auction" to
repurpose radio spectrum from broadcast television to wireless internet. In the
end, the auction yielded $19.8 billion, $10.05 billion of which was paid to 175
broadcasters for voluntarily relinquishing their licenses across 14 UHF
channels. Stations that continued broadcasting were assigned potentially new
channels to fit as densely as possible into the channels that remained. The
government netted more than $7 billion (used to pay down the national debt)
after covering costs. A crucial element of the auction design was the
construction of a solver, dubbed SATFC, that determined whether sets of
stations could be "repacked" in this way; it needed to run every time a station
was given a price quote. This paper describes the process by which we built
SATFC. We adopted an approach we dub "deep optimization", taking a data-driven,
highly parametric, and computationally intensive approach to solver design.
More specifically, to build SATFC we designed software that could pair both
complete and local-search SAT-encoded feasibility checking with a wide range of
domain-specific techniques. We then used automatic algorithm configuration
techniques to construct a portfolio of eight complementary algorithms to be run
in parallel, aiming to achieve good performance on instances that arose in
proprietary auction simulations. To evaluate the impact of our solver in this
paper, we built an open-source reverse auction simulator. We found that within
the short time budget required in practice, SATFC solved more than 95% of the
problems it encountered. Furthermore, the incentive auction paired with SATFC
produced nearly optimal allocations in a restricted setting and substantially
outperformed other alternatives at national scale.
Neural networks and rational functions
Authors: Matus Telgarsky
Date: 2017-06-11 00:00:00
Proceeding: ICML 2017 8

Neural networks and rational functions efficiently approximate each other. In
more detail, it is shown here that for any ReLU network, there exists a
rational function of degree $O(\text{polylog}(1/\epsilon))$ which is
$\epsilon$-close, and similarly for any rational function there exists a ReLU
network of size $O(\text{polylog}(1/\epsilon))$ which is $\epsilon$-close. By
contrast, polynomials need degree $\Omega(\text{poly}(1/\epsilon))$ to
approximate even a single ReLU. When converting a ReLU network to a rational
function as above, the hidden constants depend exponentially on the number of
layers, which is shown to be tight; in other words, a compositional
representation can be beneficial even for rational functions.