pwc-archive/papers-with-abstracts huggingface dataset:
Batch-normalized Recurrent Highway Networks
Authors: Chi Zhang, Thang Nguyen, Shagan Sah, Raymond Ptucha, Alexander Loui, Carl Salvaggio
Date: 2018-09-26 00:00:00
Tasks: Image Captioning

Gradient control plays an important role in feed-forward networks applied to
various computer vision tasks. Previous work has shown that Recurrent Highway
Networks minimize the problem of vanishing or exploding gradients. They achieve
this by setting the eigenvalues of the temporal Jacobian to 1 across the time
steps. In this work, batch normalized recurrent highway networks are proposed
to control the gradient flow in an improved way for network convergence.
Specifically, the introduced model can be formed by batch normalizing the
inputs at each recurrence loop. The proposed model is tested on an image
captioning task using MSCOCO dataset. Experimental results indicate that the
batch normalized recurrent highway networks converge faster and performs better
compared with the traditional LSTM and RHN based models.
Semantic Sentence Embeddings for Paraphrasing and Text Summarization
Authors: Chi Zhang, Shagan Sah, Thang Nguyen, Dheeraj Peri, Alexander Loui, Carl Salvaggio, Raymond Ptucha
Date: 2018-09-26 00:00:00
Tasks: Decoder, Sentence, Sentence Embeddings, Text Summarization

This paper introduces a sentence to vector encoding framework suitable for
advanced natural language processing. Our latent representation is shown to
encode sentences with common semantic information with similar vector
representations. The vector representation is extracted from an encoder-decoder
model which is trained on sentence paraphrase pairs. We demonstrate the
application of the sentence representations for two different tasks -- sentence
paraphrasing and paragraph summarization, making it attractive for commonly
used recurrent frameworks that process text. Experimental results help gain
insight how vector representations are suitable for advanced language
embedding.
Scaling simulation-to-real transfer by learning composable robot skills
Authors: Ryan Julian, Eric Heiden, Zhanpeng He, Hejia Zhang, Stefan Schaal, Joseph J. Lim, Gaurav Sukhatme, Karol Hausman
Date: 2018-09-26 00:00:00

We present a novel solution to the problem of simulation-to-real transfer,
which builds on recent advances in robot skill decomposition. Rather than
focusing on minimizing the simulation-reality gap, we learn a set of diverse
policies that are parameterized in a way that makes them easily reusable. This
diversity and parameterization of low-level skills allows us to find a
transferable policy that is able to use combinations and variations of
different skills to solve more complex, high-level tasks. In particular, we
first use simulation to jointly learn a policy for a set of low-level skills,
and a "skill embedding" parameterization which can be used to compose them.
Later, we learn high-level policies which actuate the low-level policies via
this skill embedding parameterization. The high-level policies encode how and
when to reuse the low-level skills together to achieve specific high-level
tasks. Importantly, our method learns to control a real robot in joint-space to
achieve these high-level tasks with little or no on-robot time, despite the
fact that the low-level policies may not be perfectly transferable from
simulation to real, and that the low-level skills were not trained on any
examples of high-level tasks. We illustrate the principles of our method using
informative simulation experiments. We then verify its usefulness for real
robotics problems by learning, transferring, and composing free-space and
contact motion skills on a Sawyer robot using only joint-space control. We
experiment with several techniques for composing pre-learned skills, and find
that our method allows us to use both learning-based approaches and efficient
search-based planning to achieve high-level tasks using only pre-learned
skills.
Deeply Informed Neural Sampling for Robot Motion Planning
Authors: Ahmed H. Qureshi, Michael C. Yip
Date: 2018-09-26 00:00:00
Tasks: Motion Planning

Sampling-based Motion Planners (SMPs) have become increasingly popular as
they provide collision-free path solutions regardless of obstacle geometry in a
given environment. However, their computational complexity increases
significantly with the dimensionality of the motion planning problem. Adaptive
sampling is one of the ways to speed up SMPs by sampling a particular region of
a configuration space that is more likely to contain an optimal path solution.
Although there are a wide variety of algorithms for adaptive sampling, they
rely on hand-crafted heuristics; furthermore, their performance decreases
significantly in high-dimensional spaces. In this paper, we present a neural
network-based adaptive sampler for motion planning called Deep Sampling-based
Motion Planner (DeepSMP). DeepSMP generates samples for SMPs and enhances their
overall speed significantly while exhibiting efficient scalability to
higher-dimensional problems. DeepSMP's neural architecture comprises of a
Contractive AutoEncoder which encodes given workspaces directly from a raw
point cloud data, and a Dropout-based stochastic deep feedforward neural
network which takes the workspace encoding, start and goal configuration, and
iteratively generates feasible samples for SMPs to compute end-to-end
collision-free optimal paths. DeepSMP is not only consistently computationally
efficient in all tested environments but has also shown remarkable
generalization to completely unseen environments. We evaluate DeepSMP on
multiple planning problems including planning of a point-mass robot,
rigid-body, 6-link robotic manipulator in various 2D and 3D environments. The
results show that on average our method is at least 7 times faster in
point-mass and rigid-body case and about 28 times faster in 6-link robot case
than the existing state-of-the-art.
Preconditioner on Matrix Lie Group for SGD
Authors: Xi-Lin Li
Date: 2018-09-26 00:00:00
Proceeding: ICLR 2019 5
Tasks: Vocal Bursts Type Prediction

We study two types of preconditioners and preconditioned stochastic gradient
descent (SGD) methods in a unified framework. We call the first one the Newton
type due to its close relationship to the Newton method, and the second one the
Fisher type as its preconditioner is closely related to the inverse of Fisher
information matrix. Both preconditioners can be derived from one framework, and
efficiently estimated on any matrix Lie groups designated by the user using
natural or relative gradient descent minimizing certain preconditioner
estimation criteria. Many existing preconditioners and methods, e.g., RMSProp,
Adam, KFAC, equilibrated SGD, batch normalization, etc., are special cases of
or closely related to either the Newton type or the Fisher type ones.
Experimental results on relatively large scale machine learning problems are
reported for performance study.
A Kernel for Multi-Parameter Persistent Homology
Authors: René Corbet, Ulderico Fugacci, Michael Kerber, Claudia Landi, Bei Wang
Date: 2018-09-26 00:00:00
Tasks: BIG-bench Machine Learning, Topological Data Analysis

Topological data analysis and its main method, persistent homology, provide a toolkit for computing topological information of high-dimensional and noisy data sets. Kernels for one-parameter persistent homology have been established to connect persistent homology with machine learning techniques. We contribute a kernel construction for multi-parameter persistence by integrating a one-parameter kernel weighted along straight lines. We prove that our kernel is stable and efficiently computable, which establishes a theoretical connection between topological data analysis and machine learning for multivariate data analysis.
Automatic Dataset Annotation to Learn CNN Pore Description for Fingerprint Recognition
Authors: Gabriel Dahia, Maurício Pamplona Segundo
Date: 2018-09-26 00:00:00

High-resolution fingerprint recognition often relies on sophisticated
matching algorithms based on hand-crafted keypoint descriptors, with pores
being the most common keypoint choice. Our method is the opposite of the
prevalent approach: we use instead a simple matching algorithm based on robust
local pore descriptors that are learned from the data using a CNN. In order to
train this CNN in a fully supervised manner, we describe how the automatic
alignment of fingerprint images can be used to obtain the required training
annotations, which are otherwise missing in all publicly available datasets.
This improves the state-of-the-art recognition results for both partial and
full fingerprints in a public benchmark. To confirm that the observed
improvement is due to the adoption of learned descriptors, we conduct an
ablation study using the most successful pore descriptors previously used in
the literature. All our code is available at
https://github.com/gdahia/high-res-fingerprint-recognition
Optimal Noise-Adding Mechanism in Additive Differential Privacy
Authors: Quan Geng, Wei Ding, Ruiqi Guo, Sanjiv Kumar
Date: 2018-09-26 00:00:00

We derive the optimal $(0, \delta)$-differentially private query-output
independent noise-adding mechanism for single real-valued query function under
a general cost-minimization framework. Under a mild technical condition, we
show that the optimal noise probability distribution is a uniform distribution
with a probability mass at the origin. We explicitly derive the optimal noise
distribution for general $\ell^p$ cost functions, including $\ell^1$ (for noise
magnitude) and $\ell^2$ (for noise power) cost functions, and show that the
probability concentration on the origin occurs when $\delta > \frac{p}{p+1}$.
Our result demonstrates an improvement over the existing Gaussian mechanisms by
a factor of two and three for $(0,\delta)$-differential privacy in the high
privacy regime in the context of minimizing the noise magnitude and noise
power, and the gain is more pronounced in the low privacy regime. Our result is
consistent with the existing result for $(0,\delta)$-differential privacy in
the discrete setting, and identifies a probability concentration phenomenon in
the continuous setting.
Left Ventricle Segmentation and Quantification from Cardiac Cine MR Images via Multi-task Learning
Authors: Shusil Dangi, Ziv Yaniv, Cristian A. Linte
Date: 2018-09-26 00:00:00
Tasks: Left Ventricle Segmentation, LV Segmentation, Multi-Task Learning, Myocardium Segmentation, Segmentation

Segmentation of the left ventricle and quantification of various cardiac
contractile functions is crucial for the timely diagnosis and treatment of
cardiovascular diseases. Traditionally, the two tasks have been tackled
independently. Here we propose a convolutional neural network based multi-task
learning approach to perform both tasks simultaneously, such that, the network
learns better representation of the data with improved generalization
performance. Probabilistic formulation of the problem enables learning the task
uncertainties during the training, which are used to automatically compute the
weights for the tasks. We performed a five fold cross-validation of the
myocardium segmentation obtained from the proposed multi-task network on 97
patient 4-dimensional cardiac cine-MRI datasets available through the STACOM LV
segmentation challenge against the provided gold-standard myocardium
segmentation, obtaining a Dice overlap of $0.849 \pm 0.036$ and mean surface
distance of $0.274 \pm 0.083$ mm, while simultaneously estimating the
myocardial area with mean absolute difference error of $205\pm198$ mm$^2$.
Predicting computational reproducibility of data analysis pipelines in large population studies using collaborative filtering
Authors: Soudabeh Barghi, Lalet Scaria, Ali Salari, Tristan Glatard
Date: 2018-09-26 00:00:00
Tasks: Collaborative Filtering

Evaluating the computational reproducibility of data analysis pipelines has
become a critical issue. It is, however, a cumbersome process for analyses that
involve data from large populations of subjects, due to their computational and
storage requirements. We present a method to predict the computational
reproducibility of data analysis pipelines in large population studies. We
formulate the problem as a collaborative filtering process, with constraints on
the construction of the training set. We propose 6 different strategies to
build the training set, which we evaluate on 2 datasets, a synthetic one
modeling a population with a growing number of subject types, and a real one
obtained with neuroinformatics pipelines. Results show that one sampling
method, "Random File Numbers (Uniform)" is able to predict computational
reproducibility with a good accuracy. We also analyze the relevance of
including file and subject biases in the collaborative filtering model. We
conclude that the proposed method is able to speedup reproducibility
evaluations substantially, with a reduced accuracy loss.
A Machine Learning Approach to Shipping Box Design
Authors: Guang Yang, Cun Mu
Date: 2018-09-26 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Descriptive

Having the right assortment of shipping boxes in the fulfillment warehouse to
pack and ship customer's online orders is an indispensable and integral part of
nowadays eCommerce business, as it will not only help maintain a profitable
business but also create great experiences for customers. However, it is an
extremely challenging operations task to strategically select the best
combination of tens of box sizes from thousands of feasible ones to be
responsible for hundreds of thousands of orders daily placed on millions of
inventory products. In this paper, we present a machine learning approach to
tackle the task by formulating the box design problem prescriptively as a
generalized version of weighted $k$-medoids clustering problem, where the
parameters are estimated through a variety of descriptive analytics. We test
this machine learning approach on fulfillment data collected from Walmart U.S.
eCommerce, and our approach is shown to be capable of improving the box
utilization rate by more than $10\%$.
Content Based Image Retrieval from AWiFS Images Repository of IRS Resourcesat-2 Satellite Based on Water Bodies and Burnt Areas
Authors: Suraj Kothawade, Kunjan Mhaske, Sahil Sharma, Furkhan Shaikh
Date: 2018-09-26 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval, Semantic Segmentation

Satellite Remote Sensing Technology is becoming a major milestone in the
prediction of weather anomalies, natural disasters as well as finding
alternative resources in proximity using multiple multi-spectral sensors
emitting electromagnetic waves at distinct wavelengths. Hence, it is imperative
to extract water bodies and burnt areas from orthorectified tiles and
correspondingly rank them using similarity measures. Different objects in all
the spheres of the earth have the inherent capability of absorbing
electromagnetic waves of distant wavelengths. This creates various unique masks
in terms of reflectance on the receptor. We propose Dynamic Semantic
Segmentation (DSS) algorithms that utilized the mentioned capability to extract
and rank Advanced Wide Field Sensor (AWiFS) images according to various
features. This system stores data intelligently in the form of a sparse feature
vector which drastically mitigates the computational and spatial costs incurred
for further analysis. The compressed source image is divided into chunks and
stored in the database for quicker retrieval. This work is intended to utilize
readily available and cost effective resources like AWiFS dataset instead of
depending on advanced technologies like Moderate Resolution Imaging
Spectroradiometer (MODIS) for data which is scarce.
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction
Authors: Yuhao Zhang, Peng Qi, Christopher D. Manning
Date: 2018-09-26 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Negation, Relation, Relation Classification, Relation Extraction

Dependency trees help relation extraction models capture long-range relations
between words. However, existing dependency-based models either neglect crucial
information (e.g., negation) by pruning the dependency trees too aggressively,
or are computationally inefficient because it is difficult to parallelize over
different tree structures. We propose an extension of graph convolutional
networks that is tailored for relation extraction, which pools information over
arbitrary dependency structures efficiently in parallel. To incorporate
relevant information while maximally removing irrelevant content, we further
apply a novel pruning strategy to the input trees by keeping words immediately
around the shortest path between the two entities among which a relation might
hold. The resulting model achieves state-of-the-art performance on the
large-scale TACRED dataset, outperforming existing sequence and
dependency-based neural models. We also show through detailed analysis that
this model has complementary strengths to sequence models, and combining them
further improves the state of the art.
Open Source Presentation Attack Detection Baseline for Iris Recognition
Authors: Joseph McGrath, Kevin W. Bowyer, Adam Czajka
Date: 2018-09-26 00:00:00
Tasks: Image Segmentation, Iris Recognition, Semantic Segmentation

This paper proposes the first, known to us, open source presentation attack detection (PAD) solution to distinguish between authentic iris images (possibly wearing clear contact lenses) and irises with textured contact lenses. This software can serve as a baseline in various PAD evaluations, and also as an open-source platform with an up-to-date reference method for iris PAD. The software is written in C++ and Python and uses only open source resources, such as OpenCV. This method does not incorporate iris image segmentation, which may be problematic for unknown fake samples. Instead, it makes a best guess to localize the rough position of the iris. The PAD-related features are extracted with the Binary Statistical Image Features (BSIF), which are classified by an ensemble of classifiers incorporating support vector machine, random forest and multilayer perceptron. The models attached to the current software have been trained with the NDCLD'15 database and evaluated on the independent datasets included in the LivDet-Iris 2017 competition. The software implements the functionality of retraining the classifiers with any database of authentic and attack images. The accuracy of the current version offered with this paper exceeds 99% when tested on subject-disjoint subsets of NDCLD'15, and oscillates around 85% when tested on the LivDet-Iris 2017 benchmarks, which is on par with the results obtained by the LivDet-Iris 2017 winner.
Bayesian inference for PCA and MUSIC algorithms with unknown number of sources
Authors: Viet Hung Tran, Wenwu Wang
Date: 2018-09-26 00:00:00
Tasks: Bayesian Inference

Principal component analysis (PCA) is a popular method for projecting data
onto uncorrelated components in lower dimension, although the optimal number of
components is not specified. Likewise, multiple signal classification (MUSIC)
algorithm is a popular PCA-based method for estimating directions of arrival
(DOAs) of sinusoidal sources, yet it requires the number of sources to be known
a priori. The accurate estimation of the number of sources is hence a crucial
issue for performance of these algorithms. In this paper, we will show that
both PCA and MUSIC actually return the exact joint maximum-a-posteriori (MAP)
estimate for uncorrelated steering vectors, although they can only compute this
MAP estimate approximately in correlated case. We then use Bayesian method to,
for the first time, compute the MAP estimate for the number of sources in PCA
and MUSIC algorithms. Intuitively, this MAP estimate corresponds to the highest
probability that signal-plus-noise's variance still dominates projected noise's
variance on signal subspace. In simulations of overlapping multi-tone sources
for linear sensor array, our exact MAP estimate is far superior to the
asymptotic Akaike information criterion (AIC), which is a popular method for
estimating the number of components in PCA and MUSIC algorithms.
When is there a Representer Theorem? Reflexive Banach spaces
Authors: Kevin Schlegel
Date: 2018-09-26 00:00:00
Tasks: Learning Theory

We consider a general regularised interpolation problem for learning a parameter vector from data. The well known representer theorem says that under certain conditions on the regulariser there exists a solution in the linear span of the data points. This is at the core of kernel methods in machine learning as it makes the problem computationally tractable. Most literature deals only with sufficient conditions for representer theorems in Hilbert spaces. We prove necessary and sufficient conditions for the existence of representer theorems in reflexive Banach spaces and illustrate why in a sense reflexivity is the minimal requirement on the function space. We further show that if the learning relies on the linear representer theorem, then the solution is independent of the regulariser and in fact determined by the function space alone. This in particular shows the value of generalising Hilbert space learning theory to Banach spaces.
Adding Neural Network Controllers to Behavior Trees without Destroying Performance Guarantees
Authors: Christopher Iliffe Sprague, Petter Ögren
Date: 2018-09-26 00:00:00
Tasks: BIG-bench Machine Learning, Reinforcement Learning

In this paper, we show how Behavior Trees that have performance guarantees, in terms of safety and goal convergence, can be extended with components that were designed using machine learning, without destroying those performance guarantees. Machine learning approaches such as reinforcement learning or learning from demonstration can be very appealing to AI designers that want efficient and realistic behaviors in their agents. However, those algorithms seldom provide guarantees for solving the given task in all different situations while keeping the agent safe. Instead, such guarantees are often easier to find for manually designed model-based approaches. In this paper we exploit the modularity of behavior trees to extend a given design with an efficient, but possibly unreliable, machine learning component in a way that preserves the guarantees. The approach is illustrated with an inverted pendulum example.
Developmental Bayesian Optimization of Black-Box with Visual Similarity-Based Transfer Learning
Authors: Maxime Petit, Amaury Depierre, Xiaofang Wang, Emmanuel Dellandréa, Liming Chen
Date: 2018-09-26 00:00:00
Tasks: Bayesian Optimisation, Bayesian Optimization, Transfer Learning

We present a developmental framework based on a long-term memory and
reasoning mechanisms (Vision Similarity and Bayesian Optimisation). This
architecture allows a robot to optimize autonomously hyper-parameters that need
to be tuned from any action and/or vision module, treated as a black-box. The
learning can take advantage of past experiences (stored in the episodic and
procedural memories) in order to warm-start the exploration using a set of
hyper-parameters previously optimized from objects similar to the new unknown
one (stored in a semantic memory). As example, the system has been used to
optimized 9 continuous hyper-parameters of a professional software (Kamido)
both in simulation and with a real robot (industrial robotic arm Fanuc) with a
total of 13 different objects. The robot is able to find a good object-specific
optimization in 68 (simulation) or 40 (real) trials. In simulation, we
demonstrate the benefit of the transfer learning based on visual similarity, as
opposed to an amnesic learning (i.e. learning from scratch all the time).
Moreover, with the real robot, we show that the method consistently outperforms
the manual optimization from an expert with less than 2 hours of training time
to achieve more than 88% of success.