pwc-archive/papers-with-abstracts huggingface dataset:
Prolongation of SMAP to Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning Neural Network
Authors: Kuai Fang, Chaopeng Shen, Daniel Kifer, Xiao Yang
Date: 2017-07-20 00:00:00
Tasks: Time Series, Time Series Analysis, Weather Forecasting

The Soil Moisture Active Passive (SMAP) mission has delivered valuable
sensing of surface soil moisture since 2015. However, it has a short time span
and irregular revisit schedule. Utilizing a state-of-the-art time-series deep
learning neural network, Long Short-Term Memory (LSTM), we created a system
that predicts SMAP level-3 soil moisture data with atmospheric forcing,
model-simulated moisture, and static physiographic attributes as inputs. The
system removes most of the bias with model simulations and improves predicted
moisture climatology, achieving small test root-mean-squared error (<0.035) and
high correlation coefficient >0.87 for over 75\% of Continental United States,
including the forested Southeast. As the first application of LSTM in
hydrology, we show the proposed network avoids overfitting and is robust for
both temporal and spatial extrapolation tests. LSTM generalizes well across
regions with distinct climates and physiography. With high fidelity to SMAP,
LSTM shows great potential for hindcasting, data assimilation, and weather
forecasting.
Training a Fully Convolutional Neural Network to Route Integrated Circuits
Authors: Sambhav R. Jain, Kye Okabe
Date: 2017-06-27 00:00:00
Tasks: Layout Design

We present a deep, fully convolutional neural network that learns to route a
circuit layout net with appropriate choice of metal tracks and wire class
combinations. Inputs to the network are the encoded layouts containing spatial
location of pins to be routed. After 15 fully convolutional stages followed by
a score comparator, the network outputs 8 layout layers (corresponding to 4
route layers, 3 via layers and an identity-mapped pin layer) which are then
decoded to obtain the routed layouts. We formulate this as a binary
segmentation problem on a per-pixel per-layer basis, where the network is
trained to correctly classify pixels in each layout layer to be 'on' or 'off'.
To demonstrate learnability of layout design rules, we train the network on a
dataset of 50,000 train and 10,000 validation samples that we generate based on
certain pre-defined layout constraints. Precision, recall and $F_1$ score
metrics are used to track the training progress. Our network achieves
$F_1\approx97\%$ on the train set and $F_1\approx92\%$ on the validation set.
We use PyTorch for implementing our model. Code is made publicly available at
https://github.com/sjain-stanford/deep-route .
Recovering Homography from Camera Captured Documents using Convolutional Neural Networks
Authors: Syed Ammar Abbas, Sibt Ul Hussain
Date: 2017-09-11 00:00:00

Removing perspective distortion from hand held camera captured document
images is one of the primitive tasks in document analysis, but unfortunately,
no such method exists that can reliably remove the perspective distortion from
document images automatically. In this paper, we propose a convolutional neural
network based method for recovering homography from hand-held camera captured
documents.
  Our proposed method works independent of document's underlying content and is
trained end-to-end in a fully automatic way. Specifically, this paper makes
following three contributions: Firstly, we introduce a large scale synthetic
dataset for recovering homography from documents images captured under
different geometric and photometric transformations; secondly, we show that a
generic convolutional neural network based architecture can be successfully
used for regressing the corners positions of documents captured under wild
settings; thirdly, we show that L1 loss can be reliably used for corners
regression. Our proposed method gives state-of-the-art performance on the
tested datasets, and has potential to become an integral part of document
analysis pipeline.
Deep Generative Filter for Motion Deblurring
Authors: Sainandan Ramakrishnan, Shubham Pachori. Aalok Gangopadhyay, Shanmuganathan Raman
Date: 2017-09-11 00:00:00
Tasks: Deblurring, Generative Adversarial Network

Removing blur caused by camera shake in images has always been a challenging
problem in computer vision literature due to its ill-posed nature. Motion blur
caused due to the relative motion between the camera and the object in 3D space
induces a spatially varying blurring effect over the entire image. In this
paper, we propose a novel deep filter based on Generative Adversarial Network
(GAN) architecture integrated with global skip connection and dense
architecture in order to tackle this problem. Our model, while bypassing the
process of blur kernel estimation, significantly reduces the test time which is
necessary for practical applications. The experiments on the benchmark datasets
prove the effectiveness of the proposed method which outperforms the
state-of-the-art blind deblurring algorithms both quantitatively and
qualitatively.
Combining Strategic Learning and Tactical Search in Real-Time Strategy Games
Authors: Nicolas A. Barriga, Marius Stanescu, Michael Buro
Date: 2017-09-11 00:00:00
Tasks: Decision Making, Real-Time Strategy Games

A commonly used technique for managing AI complexity in real-time strategy
(RTS) games is to use action and/or state abstractions. High-level abstractions
can often lead to good strategic decision making, but tactical decision quality
may suffer due to lost details. A competing method is to sample the search
space which often leads to good tactical performance in simple scenarios, but
poor high-level planning.
  We propose to use a deep convolutional neural network (CNN) to select among a
limited set of abstract action choices, and to utilize the remaining
computation time for game tree search to improve low level tactics. The CNN is
trained by supervised learning on game states labelled by Puppet Search, a
strategic search algorithm that uses action abstractions. The network is then
used to select a script --- an abstract action --- to produce low level actions
for all units. Subsequently, the game tree search algorithm improves the
tactical actions of a subset of units using a limited view of the game state
only considering units close to opponent units.
  Experiments in the microRTS game show that the combined algorithm results in
higher win-rates than either of its two independent components and other
state-of-the-art microRTS agents.
  To the best of our knowledge, this is the first successful application of a
convolutional network to play a full RTS game on standard game maps, as
previous work has focused on sub-problems, such as combat, or on very small
maps.
Audio-Visual Speech Enhancement Using Multimodal Deep Convolutional Neural Networks
Authors: Jen-Cheng Hou, Syu-Siang Wang, Ying-Hui Lai, Yu Tsao, Hsiu-Wen Chang, Hsin-Min Wang
Date: 2017-09-01 00:00:00
Tasks: Decoder, Multi-Task Learning, Speech Enhancement

Speech enhancement (SE) aims to reduce noise in speech signals. Most SE techniques focus only on addressing audio information. In this work, inspired by multimodal learning, which utilizes data from different modalities, and the recent success of convolutional neural networks (CNNs) in SE, we propose an audio-visual deep CNNs (AVDCNN) SE model, which incorporates audio and visual streams into a unified network model. We also propose a multi-task learning framework for reconstructing audio and visual signals at the output layer. Precisely speaking, the proposed AVDCNN model is structured as an audio-visual encoder-decoder network, in which audio and visual data are first processed using individual CNNs, and then fused into a joint network to generate enhanced speech (the primary task) and reconstructed images (the secondary task) at the output layer. The model is trained in an end-to-end manner, and parameters are jointly learned through back-propagation. We evaluate enhanced speech using five instrumental criteria. Results show that the AVDCNN model yields a notably superior performance compared with an audio-only CNN-based SE model and two conventional SE approaches, confirming the effectiveness of integrating visual information into the SE process. In addition, the AVDCNN model also outperforms an existing audio-visual SE model, confirming its capability of effectively combining audio and visual information in SE.
UI-Net: Interactive Artificial Neural Networks for Iterative Image Segmentation Based on a User Model
Authors: Mario Amrehn, Sven Gaube, Mathias Unberath, Frank Schebesch, Tim Horz, Maddalena Strumia, Stefan Steidl, Markus Kowarschik, Andreas Maier
Date: 2017-09-11 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

For complex segmentation tasks, fully automatic systems are inherently
limited in their achievable accuracy for extracting relevant objects.
Especially in cases where only few data sets need to be processed for a highly
accurate result, semi-automatic segmentation techniques exhibit a clear benefit
for the user. One area of application is medical image processing during an
intervention for a single patient. We propose a learning-based cooperative
segmentation approach which includes the computing entity as well as the user
into the task. Our system builds upon a state-of-the-art fully convolutional
artificial neural network (FCN) as well as an active user model for training.
During the segmentation process, a user of the trained system can iteratively
add additional hints in form of pictorial scribbles as seed points into the FCN
system to achieve an interactive and precise segmentation result. The
segmentation quality of interactive FCNs is evaluated. Iterative FCN approaches
can yield superior results compared to networks without the user input channel
component, due to a consistent improvement in segmentation quality after each
interaction.
Why Do Deep Neural Networks Still Not Recognize These Images?: A Qualitative Analysis on Failure Cases of ImageNet Classification
Authors: Han S. Lee, Alex A. Agarwal, Junmo Kim
Date: 2017-09-11 00:00:00
Tasks: Deep Learning, General Classification

In a recent decade, ImageNet has become the most notable and powerful
benchmark database in computer vision and machine learning community. As
ImageNet has emerged as a representative benchmark for evaluating the
performance of novel deep learning models, its evaluation tends to include only
quantitative measures such as error rate, rather than qualitative analysis.
Thus, there are few studies that analyze the failure cases of deep learning
models in ImageNet, though there are numerous works analyzing the networks
themselves and visualizing them. In this abstract, we qualitatively analyze the
failure cases of ImageNet classification results from recent deep learning
model, and categorize these cases according to the certain image patterns.
Through this failure analysis, we believe that it can be discovered what the
final challenges are in ImageNet database, which the current deep learning
model is still vulnerable to.
How Does Knowledge of the AUC Constrain the Set of Possible Ground-truth Labelings?
Authors: Jacob Whitehill
Date: 2017-09-07 00:00:00
Tasks: Binary Classification, Privacy Preserving

Recent work on privacy-preserving machine learning has considered how
data-mining competitions such as Kaggle could potentially be "hacked", either
intentionally or inadvertently, by using information from an oracle that
reports a classifier's accuracy on the test set. For binary classification
tasks in particular, one of the most common accuracy metrics is the Area Under
the ROC Curve (AUC), and in this paper we explore the mathematical structure of
how the AUC is computed from an n-vector of real-valued "guesses" with respect
to the ground-truth labels. We show how knowledge of a classifier's AUC on the
test set can constrain the set of possible ground-truth labelings, and we
derive an algorithm both to compute the exact number of such labelings and to
enumerate efficiently over them. Finally, we provide empirical evidence that,
surprisingly, the number of compatible labelings can actually decrease as n
grows, until a test set-dependent threshold is reached.
On the Use of Sparse Filtering for Covariate Shift Adaptation
Authors: Fabio Massimo Zennaro, Ke Chen
Date: 2016-07-22 00:00:00

In this paper we formally analyse the use of sparse filtering algorithms to
perform covariate shift adaptation. We provide a theoretical analysis of sparse
filtering by evaluating the conditions required to perform covariate shift
adaptation. We prove that sparse filtering can perform adaptation only if the
conditional distribution of the labels has a structure explained by a cosine
metric. To overcome this limitation, we propose a new algorithm, named periodic
sparse filtering, and carry out the same theoretical analysis regarding
covariate shift adaptation. We show that periodic sparse filtering can perform
adaptation under the looser and more realistic requirement that the conditional
distribution of the labels has a periodic structure, which may be satisfied,
for instance, by user-dependent data sets. We experimentally validate our
theoretical results on synthetic data. Moreover, we apply periodic sparse
filtering to real-world data sets to demonstrate that this simple and
computationally efficient algorithm is able to achieve competitive
performances.
Statistical Inference for Data-adaptive Doubly Robust Estimators with Survival Outcomes
Authors: Iván Díaz
Date: 2017-09-01 00:00:00
Tasks: regression, Survival Analysis

The consistency of doubly robust estimators relies on consistent estimation
of at least one of two nuisance regression parameters. In moderate to large
dimensions, the use of flexible data-adaptive regression estimators may aid in
achieving this consistency. However, $n^{1/2}$-consistency of doubly robust
estimators is not guaranteed if one of the nuisance estimators is inconsistent.
In this paper we present a doubly robust estimator for survival analysis with
the novel property that it converges to a Gaussian variable at $n^{1/2}$-rate
for a large class of data-adaptive estimators of the nuisance parameters, under
the only assumption that at least one of them is consistently estimated at a
$n^{1/4}$-rate. This result is achieved through adaptation of recent ideas in
semiparametric inference, which amount to: (i) Gaussianizing (i.e., making
asymptotically linear) a drift term that arises in the asymptotic analysis of
the doubly robust estimator, and (ii) using cross-fitting to avoid entropy
conditions on the nuisance estimators. We present the formula of the asymptotic
variance of the estimator, which allows computation of doubly robust confidence
intervals and p-values. We illustrate the finite-sample properties of the
estimator in simulation studies, and demonstrate its use in a phase III
clinical trial for estimating the effect of a novel therapy for the treatment
of HER2 positive breast cancer.
One-Shot Learning for Semantic Segmentation
Authors: Amirreza Shaban, Shray Bansal, Zhen Liu, Irfan Essa, Byron Boots
Date: 2017-09-11 00:00:00
Tasks: General Classification, image-classification, Image Classification, Image Segmentation, One-Shot Learning, One-Shot Segmentation, Segmentation, Semantic Segmentation

Low-shot learning methods for image classification support learning from
sparse data. We extend these techniques to support dense semantic image
segmentation. Specifically, we train a network that, given a small set of
annotated images, produces parameters for a Fully Convolutional Network (FCN).
We use this FCN to perform dense pixel-level prediction on a test image for the
new semantic class. Our architecture shows a 25% relative meanIoU improvement
compared to the best baseline methods for one-shot segmentation on unseen
classes in the PASCAL VOC 2012 dataset and is at least 3 times faster.
Deep Shape Matching
Authors: Filip Radenović, Giorgos Tolias, Ondřej Chum
Date: 2017-09-11 00:00:00
Proceeding: ECCV 2018 9
Tasks: Domain Generalization, Image Retrieval, Metric Learning, Retrieval, Sketch-Based Image Retrieval

We cast shape matching as metric learning with convolutional networks. We
break the end-to-end process of image representation into two parts. Firstly,
well established efficient methods are chosen to turn the images into edge
maps. Secondly, the network is trained with edge maps of landmark images, which
are automatically obtained by a structure-from-motion pipeline. The learned
representation is evaluated on a range of different tasks, providing
improvements on challenging cases of domain generalization, generic
sketch-based image retrieval or its fine-grained counterpart. In contrast to
other methods that learn a different model per task, object category, or
domain, we use the same network throughout all our experiments, achieving
state-of-the-art results in multiple benchmarks.
Social Media Text Processing and Semantic Analysis for Smart Cities
Authors: João Filipe Figueiredo Pereira
Date: 2017-09-11 00:00:00
Tasks: Data Visualization, Word Embeddings

With the rise of Social Media, people obtain and share information almost
instantly on a 24/7 basis. Many research areas have tried to gain valuable
insights from these large volumes of freely available user generated content.
  With the goal of extracting knowledge from social media streams that might be
useful in the context of intelligent transportation systems and smart cities,
we designed and developed a framework that provides functionalities for
parallel collection of geo-located tweets from multiple pre-defined bounding
boxes (cities or regions), including filtering of non-complying tweets, text
pre-processing for Portuguese and English language, topic modeling, and
transportation-specific text classifiers, as well as, aggregation and data
visualization.
  We performed an exploratory data analysis of geo-located tweets in 5
different cities: Rio de Janeiro, S\~ao Paulo, New York City, London and
Melbourne, comprising a total of more than 43 million tweets in a period of 3
months. Furthermore, we performed a large scale topic modelling comparison
between Rio de Janeiro and S\~ao Paulo. Interestingly, most of the topics are
shared between both cities which despite being in the same country are
considered very different regarding population, economy and lifestyle.
  We take advantage of recent developments in word embeddings and train such
representations from the collections of geo-located tweets. We then use a
combination of bag-of-embeddings and traditional bag-of-words to train
travel-related classifiers in both Portuguese and English to filter
travel-related content from non-related. We created specific gold-standard data
to perform empirical evaluation of the resulting classifiers. Results are in
line with research work in other application areas by showing the robustness of
using word embeddings to learn word similarities that bag-of-words is not able
to capture.
Hamiltonian Maker-Breaker games on small graphs
Authors: Miloš Stojaković, Nikola Trkulja
Date: 2017-08-25 00:00:00

We look at the unbiased Maker-Breaker Hamiltonicity game played on the edge
set of a complete graph $K_n$, where Maker's goal is to claim a Hamiltonian
cycle. First, we prove that, independent of who starts, Maker can win the game
for $n = 8$ and $n = 9$. Then we use an inductive argument to show that,
independent of who starts, Maker can win the game if and only if $n \geq 8$.
This, in particular, resolves in the affirmative the long-standing conjecture
of Papaioannou.
  We also study two standard positional games related to Hamiltonicity game.
For Hamiltonian Path game, we show that Maker can claim a Hamiltonian path if
and only if $n \geq 5$, independent of who starts. Next, we look at Fixed
Hamiltonian Path game, where the goal of Maker is to claim a Hamiltonian path
between two predetermined vertices. We prove that if Maker starts the game, he
wins if and only if $n \geq 7$, and if Breaker starts, Maker wins if and only
if $n \geq 8$. Using this result, we are able to improve the previously best
upper bound on the smallest number of edges a graph on $n$ vertices can have,
knowing that Maker can win the Maker-Breaker Hamiltonicity game played on its
edges.
  To resolve the outcomes of the mentioned games on small (finite) boards, we
devise algorithms for efficiently searching game trees and then obtain our
results with the help of a computer.
Multivariate Regression with Gross Errors on Manifold-valued Data
Authors: Xiaowei Zhang, Xudong Shi, Yu Sun, Li Cheng
Date: 2017-03-26 00:00:00
Tasks: regression

We consider the topic of multivariate regression on manifold-valued output,
that is, for a multivariate observation, its output response lies on a
manifold. Moreover, we propose a new regression model to deal with the presence
of grossly corrupted manifold-valued responses, a bottleneck issue commonly
encountered in practical scenarios. Our model first takes a correction step on
the grossly corrupted responses via geodesic curves on the manifold, and then
performs multivariate linear regression on the corrected data. This results in
a nonconvex and nonsmooth optimization problem on manifolds. To this end, we
propose a dedicated approach named PALMR, by utilizing and extending the
proximal alternating linearized minimization techniques. Theoretically, we
investigate its convergence property, where it is shown to converge to a
critical point under mild conditions. Empirically, we test our model on both
synthetic and real diffusion tensor imaging data, and show that our model
outperforms other multivariate regression models when manifold-valued responses
contain gross errors, and is effective in identifying gross errors.
Automated Identification of Trampoline Skills Using Computer Vision Extracted Pose Estimation
Authors: Paul W. Connolly, Guenole C. Silvestre, Chris J. Bleakley
Date: 2017-09-11 00:00:00
Tasks: Pose Estimation

A novel method to identify trampoline skills using a single video camera is
proposed herein. Conventional computer vision techniques are used for
identification, estimation, and tracking of the gymnast's body in a video
recording of the routine. For each frame, an open source convolutional neural
network is used to estimate the pose of the athlete's body. Body orientation
and joint angle estimates are extracted from these pose estimates. The
trajectories of these angle estimates over time are compared with those of
labelled reference skills. A nearest neighbour classifier utilising a mean
squared error distance metric is used to identify the skill performed. A
dataset containing 714 skill examples with 20 distinct skills performed by
adult male and female gymnasts was recorded and used for evaluation of the
system. The system was found to achieve a skill identification accuracy of
80.7% for the dataset.