pwc-archive/papers-with-abstracts huggingface dataset:
Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios
Authors: Hiroaki Sasaki, Takafumi Kanamori, Aapo Hyvärinen, Gang Niu, Masashi Sugiyama
Date: 2017-07-06 00:00:00
Tasks: Clustering, Density Estimation

Modes and ridges of the probability density function behind observed data are
useful geometric features. Mode-seeking clustering assigns cluster labels by
associating data samples with the nearest modes, and estimation of density
ridges enables us to find lower-dimensional structures hidden in data. A key
technical challenge both in mode-seeking clustering and density ridge
estimation is accurate estimation of the ratios of the first- and second-order
density derivatives to the density. A naive approach takes a three-step
approach of first estimating the data density, then computing its derivatives,
and finally taking their ratios. However, this three-step approach can be
unreliable because a good density estimator does not necessarily mean a good
density derivative estimator, and division by the estimated density could
significantly magnify the estimation error. To cope with these problems, we
propose a novel estimator for the \emph{density-derivative-ratios}. The
proposed estimator does not involve density estimation, but rather
\emph{directly} approximates the ratios of density derivatives of any order.
Moreover, we establish a convergence rate of the proposed estimator. Based on
the proposed estimator, novel methods both for mode-seeking clustering and
density ridge estimation are developed, and the respective convergence rates to
the mode and ridge of the underlying density are also established. Finally, we
experimentally demonstrate that the developed methods significantly outperform
existing methods, particularly for relatively high-dimensional data.
Engineering a Simplified 0-Bit Consistent Weighted Sampling
Authors: Edward Raff, Jared Sylvester, Charles Nicholas
Date: 2018-03-30 00:00:00
Tasks: General Classification

The Min-Hashing approach to sketching has become an important tool in data
analysis, information retrial, and classification. To apply it to real-valued
datasets, the ICWS algorithm has become a seminal approach that is widely used,
and provides state-of-the-art performance for this problem space. However, ICWS
suffers a computational burden as the sketch size K increases. We develop a new
Simplified approach to the ICWS algorithm, that enables us to obtain over 20x
speedups compared to the standard algorithm. The veracity of our approach is
demonstrated empirically on multiple datasets and scenarios, showing that our
new Simplified CWS obtains the same quality of results while being an order of
magnitude faster.
Learning Beyond Human Expertise with Generative Models for Dental Restorations
Authors: Jyh-Jing Hwang, Sergei Azernikov, Alexei A. Efros, Stella X. Yu
Date: 2018-03-30 00:00:00
Tasks: Object Recognition

Computer vision has advanced significantly that many discriminative
approaches such as object recognition are now widely used in real applications.
We present another exciting development that utilizes generative models for the
mass customization of medical products such as dental crowns. In the dental
industry, it takes a technician years of training to design synthetic crowns
that restore the function and integrity of missing teeth. Each crown must be
customized to individual patients, and it requires human expertise in a
time-consuming and labor-intensive process, even with computer-assisted design
software. We develop a fully automatic approach that learns not only from human
designs of dental crowns, but also from natural spatial profiles between
opposing teeth. The latter is hard to account for by technicians but important
for proper biting and chewing functions. Built upon a Generative Adversar-ial
Network architecture (GAN), our deep learning model predicts the customized
crown-filled depth scan from the crown-missing depth scan and opposing depth
scan. We propose to incorporate additional space constraints and statistical
compatibility into learning. Our automatic designs exceed human technicians'
standards for good morphology and functionality, and our algorithm is being
tested for production use.
Visual Robot Task Planning
Authors: Chris Paxton, Yotam Barnoy, Kapil Katyal, Raman Arora, Gregory D. Hager
Date: 2018-03-30 00:00:00
Tasks: Imitation Learning, Robot Task Planning, Task Planning

Prospection, the act of predicting the consequences of many possible futures,
is intrinsic to human planning and action, and may even be at the root of
consciousness. Surprisingly, this idea has been explored comparatively little
in robotics. In this work, we propose a neural network architecture and
associated planning algorithm that (1) learns a representation of the world
useful for generating prospective futures after the application of high-level
actions, (2) uses this generative model to simulate the result of sequences of
high-level actions in a variety of environments, and (3) uses this same
representation to evaluate these actions and perform tree search to find a
sequence of high-level actions in a new environment. Models are trained via
imitation learning on a variety of domains, including navigation,
pick-and-place, and a surgical robotics task. Our approach allows us to
visualize intermediate motion goals and learn to plan complex activity from
visual information.
Class Subset Selection for Transfer Learning using Submodularity
Authors: Varun Manjunatha, Srikumar Ramalingam, Tim K. Marks, Larry Davis
Date: 2018-03-30 00:00:00
Tasks: image-classification, Image Classification, Transfer Learning

In recent years, it is common practice to extract fully-connected layer (fc)
features that were learned while performing image classification on a source
dataset, such as ImageNet, and apply them generally to a wide range of other
tasks. The general usefulness of some large training datasets for transfer
learning is not yet well understood, and raises a number of questions. For
example, in the context of transfer learning, what is the role of a specific
class in the source dataset, and how is the transferability of fc features
affected when they are trained using various subsets of the set of all classes
in the source dataset? In this paper, we address the question of how to select
an optimal subset of the set of classes, subject to a budget constraint, that
will more likely generate good features for other tasks. To accomplish this, we
use a submodular set function to model the accuracy achievable on a new task
when the features have been learned on a given subset of classes of the source
dataset. An optimal subset is identified as the set that maximizes this
submodular function. The maximization can be accomplished using an efficient
greedy algorithm that comes with guarantees on the optimality of the solution.
We empirically validate our submodular model by successfully identifying
subsets of classes that produce good features for new tasks.
Understanding Autoencoders with Information Theoretic Concepts
Authors: Shujian Yu, Jose C. Principe
Date: 2018-03-30 00:00:00
Tasks: Information Plane

Despite their great success in practical applications, there is still a lack of theoretical and systematic methods to analyze deep neural networks. In this paper, we illustrate an advanced information theoretic methodology to understand the dynamics of learning and the design of autoencoders, a special type of deep learning architectures that resembles a communication channel. By generalizing the information plane to any cost function, and inspecting the roles and dynamics of different layers using layer-wise information quantities, we emphasize the role that mutual information plays in quantifying learning from data. We further suggest and also experimentally validate, for mean square error training, three fundamental properties regarding the layer-wise flow of information and intrinsic dimensionality of the bottleneck layer, using respectively the data processing inequality and the identification of a bifurcation point in the information plane that is controlled by the given data. Our observations have a direct impact on the optimal design of autoencoders, the design of alternative feedforward training methods, and even in the problem of generalization.
Deflecting Adversarial Attacks with Pixel Deflection
Authors: Aaditya Prakash, Nick Moran, Solomon Garber, Antonella DiLillo, James Storer
Date: 2018-01-26 00:00:00
Proceeding: CVPR 2018 6
Tasks: Adversarial Attack

CNNs are poised to become integral parts of many critical systems. Despite
their robustness to natural variations, image pixel values can be manipulated,
via small, carefully crafted, imperceptible perturbations, to cause a model to
misclassify images. We present an algorithm to process an image so that
classification accuracy is significantly preserved in the presence of such
adversarial manipulations. Image classifiers tend to be robust to natural
noise, and adversarial attacks tend to be agnostic to object location. These
observations motivate our strategy, which leverages model robustness to defend
against adversarial perturbations by forcing the image to match natural image
statistics. Our algorithm locally corrupts the image by redistributing pixel
values via a process we term pixel deflection. A subsequent wavelet-based
denoising operation softens this corruption, as well as some of the adversarial
changes. We demonstrate experimentally that the combination of these techniques
enables the effective recovery of the true class, against a variety of robust
attacks. Our results compare favorably with current state-of-the-art defenses,
without requiring retraining or modifying the CNN.
Overview: A Hierarchical Framework for Plan Generation and Execution in Multi-Robot Systems
Authors: Hang Ma, Wolfgang Hönig, Liron Cohen, Tansel Uras, Hong Xu, T. K. Satish Kumar, Nora Ayanian, Sven Koenig
Date: 2018-03-30 00:00:00

The authors present an overview of a hierarchical framework for coordinating
task- and motion-level operations in multirobot systems. Their framework is
based on the idea of using simple temporal networks to simultaneously reason
about precedence/causal constraints required for task-level coordination and
simple temporal constraints required to take some kinematic constraints of
robots into account. In the plan-generation phase, the framework provides a
computationally scalable method for generating plans that achieve high-level
tasks for groups of robots and take some of their kinematic constraints into
account. In the plan-execution phase, the framework provides a method for
absorbing an imperfect plan execution to avoid time-consuming re-planning in
many cases. The authors use the multirobot path-planning problem as a case
study to present the key ideas behind their framework for the long-term
autonomy of multirobot systems.
Improving Sentiment Analysis in Arabic Using Word Representation
Authors: Abdulaziz M. Alayba, Vasile Palade, Matthew England, Rahat Iqbal
Date: 2018-02-28 00:00:00
Tasks: General Classification, Sentiment Analysis, Sentiment Classification

The complexities of Arabic language in morphology, orthography and dialects
makes sentiment analysis for Arabic more challenging. Also, text feature
extraction from short messages like tweets, in order to gauge the sentiment,
makes this task even more difficult. In recent years, deep neural networks were
often employed and showed very good results in sentiment classification and
natural language processing applications. Word embedding, or word distributing
approach, is a current and powerful tool to capture together the closest words
from a contextual text. In this paper, we describe how we construct Word2Vec
models from a large Arabic corpus obtained from ten newspapers in different
Arab countries. By applying different machine learning algorithms and
convolutional neural networks with different text feature selections, we report
improved accuracy of sentiment classification (91%-95%) on our publicly
available Arabic language health sentiment dataset [1]
ESPnet: End-to-End Speech Processing Toolkit
Authors: Shinji Watanabe, Takaaki Hori, Shigeki Karita, Tomoki Hayashi, Jiro Nishitoba, Yuya Unno, Nelson Enrique Yalta Soplin, Jahn Heymann, Matthew Wiesner, Nanxin Chen, Adithya Renduchintala, Tsubasa Ochiai
Date: 2018-03-30 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This paper introduces a new open source platform for end-to-end speech
processing named ESPnet. ESPnet mainly focuses on end-to-end automatic speech
recognition (ASR), and adopts widely-used dynamic neural network toolkits,
Chainer and PyTorch, as a main deep learning engine. ESPnet also follows the
Kaldi ASR toolkit style for data processing, feature extraction/format, and
recipes to provide a complete setup for speech recognition and other speech
processing experiments. This paper explains a major architecture of this
software platform, several important functionalities, which differentiate
ESPnet from other open source ASR toolkits, and experimental results with major
ASR benchmarks.
Learning to Anonymize Faces for Privacy Preserving Action Detection
Authors: Zhongzheng Ren, Yong Jae Lee, Michael S. Ryoo
Date: 2018-03-30 00:00:00
Proceeding: ECCV 2018 9
Tasks: Action Detection, Privacy Preserving

There is an increasing concern in computer vision devices invading users'
privacy by recording unwanted videos. On the one hand, we want the camera
systems to recognize important events and assist human daily lives by
understanding its videos, but on the other hand we want to ensure that they do
not intrude people's privacy. In this paper, we propose a new principled
approach for learning a video \emph{face anonymizer}. We use an adversarial
training setting in which two competing systems fight: (1) a video anonymizer
that modifies the original video to remove privacy-sensitive information while
still trying to maximize spatial action detection performance, and (2) a
discriminator that tries to extract privacy-sensitive information from the
anonymized videos. The end result is a video anonymizer that performs
pixel-level modifications to anonymize each person's face, with minimal effect
on action detection performance. We experimentally confirm the benefits of our
approach compared to conventional hand-crafted anonymization methods including
masking, blurring, and noise adding. Code, demo, and more results can be found
on our project page https://jason718.github.io/project/privacy/main.html.
The eigenvalues of stochastic blockmodel graphs
Authors: Minh Tang
Date: 2018-03-30 00:00:00

We derive the limiting distribution for the largest eigenvalues of the
adjacency matrix for a stochastic blockmodel graph when the number of vertices
tends to infinity. We show that, in the limit, these eigenvalues are jointly
multivariate normal with bounded covariances. Our result extends the classic
result of F\"{u}redi and Koml\'{o}s on the fluctuation of the largest
eigenvalue for Erd\H{o}s-R\'{e}nyi graphs.
Multi-modal Disease Classification in Incomplete Datasets Using Geometric Matrix Completion
Authors: Gerome Vivar, Andreas Zwergal, Nassir Navab, Seyed-Ahmad Ahmadi
Date: 2018-03-30 00:00:00
Tasks: Classification, General Classification, Imputation, Matrix Completion

In large population-based studies and in clinical routine, tasks like disease
diagnosis and progression prediction are inherently based on a rich set of
multi-modal data, including imaging and other sensor data, clinical scores,
phenotypes, labels and demographics. However, missing features, rater bias and
inaccurate measurements are typical ailments of real-life medical datasets.
Recently, it has been shown that deep learning with graph convolution neural
networks (GCN) can outperform traditional machine learning in disease
classification, but missing features remain an open problem. In this work, we
follow up on the idea of modeling multi-modal disease classification as a
matrix completion problem, with simultaneous classification and non-linear
imputation of features. Compared to methods before, we arrange subjects in a
graph-structure and solve classification through geometric matrix completion,
which simulates a heat diffusion process that is learned and solved with a
recurrent neural network. We demonstrate the potential of this method on the
ADNI-based TADPOLE dataset and on the task of predicting the transition from
MCI to Alzheimer's disease. With an AUC of 0.950 and classification accuracy of
87%, our approach outperforms standard linear and non-linear classifiers, as
well as several state-of-the-art results in related literature, including a
recently proposed GCN-based approach.
Learning Image-Conditioned Dynamics Models for Control of Under-actuated Legged Millirobots
Authors: Anusha Nagabandi, Guangzhao Yang, Thomas Asmar, Ravi Pandya, Gregory Kahn, Sergey Levine, Ronald S. Fearing
Date: 2017-11-14 00:00:00

Millirobots are a promising robotic platform for many applications due to
their small size and low manufacturing costs. Legged millirobots, in
particular, can provide increased mobility in complex environments and improved
scaling of obstacles. However, controlling these small, highly dynamic, and
underactuated legged systems is difficult. Hand-engineered controllers can
sometimes control these legged millirobots, but they have difficulties with
dynamic maneuvers and complex terrains. We present an approach for controlling
a real-world legged millirobot that is based on learned neural network models.
Using less than 17 minutes of data, our method can learn a predictive model of
the robot's dynamics that can enable effective gaits to be synthesized on the
fly for following user-specified waypoints on a given terrain. Furthermore, by
leveraging expressive, high-capacity neural network models, our approach allows
for these predictions to be directly conditioned on camera images, endowing the
robot with the ability to predict how different terrains might affect its
dynamics. This enables sample-efficient and effective learning for locomotion
of a dynamic legged millirobot on various terrains, including gravel, turf,
carpet, and styrofoam. Experiment videos can be found at
https://sites.google.com/view/imageconddyn
Guide Me: Interacting with Deep Networks
Authors: Christian Rupprecht, Iro Laina, Nassir Navab, Gregory D. Hager, Federico Tombari
Date: 2018-03-30 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Captioning, Image Generation

Interaction and collaboration between humans and intelligent machines has
become increasingly important as machine learning methods move into real-world
applications that involve end users. While much prior work lies at the
intersection of natural language and vision, such as image captioning or image
generation from text descriptions, less focus has been placed on the use of
language to guide or improve the performance of a learned visual processing
algorithm. In this paper, we explore methods to flexibly guide a trained
convolutional neural network through user input to improve its performance
during inference. We do so by inserting a layer that acts as a spatio-semantic
guide into the network. This guide is trained to modify the network's
activations, either directly via an energy minimization scheme or indirectly
through a recurrent model that translates human language queries to interaction
weights. Learning the verbal interaction is fully automatic and does not
require manual text annotations. We evaluate the method on two datasets,
showing that guiding a pre-trained network can improve performance, and provide
extensive insights into the interaction between the guide and the CNN.
Towards Quantum Machine Learning with Tensor Networks
Authors: William Huggins, Piyush Patel, K. Birgitta Whaley, E. Miles Stoudenmire
Date: 2018-03-30 00:00:00
Tasks: BIG-bench Machine Learning, Handwriting Recognition, Quantum Machine Learning, Tensor Networks

Machine learning is a promising application of quantum computing, but
challenges remain as near-term devices will have a limited number of physical
qubits and high error rates. Motivated by the usefulness of tensor networks for
machine learning in the classical context, we propose quantum computing
approaches to both discriminative and generative learning, with circuits based
on tree and matrix product state tensor networks that could have benefits for
near-term devices. The result is a unified framework where classical and
quantum computing can benefit from the same theoretical and algorithmic
developments, and the same model can be trained classically then transferred to
the quantum setting for additional optimization. Tensor network circuits can
also provide qubit-efficient schemes where, depending on the architecture, the
number of physical qubits required scales only logarithmically with, or
independently of the input or output data sizes. We demonstrate our proposals
with numerical experiments, training a discriminative model to perform
handwriting recognition using a optimization procedure that could be carried
out on quantum hardware, and testing the noise resilience of the trained model.
An Extreme-Value Approach for Testing the Equality of Large U-Statistic Based Correlation Matrices
Authors: Cheng Zhou, Fang Han, Xinsheng Zhang, Han Liu
Date: 2015-02-11 00:00:00
Tasks: valid

There has been an increasing interest in testing the equality of large
Pearson's correlation matrices. However, in many applications it is more
important to test the equality of large rank-based correlation matrices since
they are more robust to outliers and nonlinearity. Unlike the Pearson's case,
testing the equality of large rank-based statistics has not been well explored
and requires us to develop new methods and theory. In this paper, we provide a
framework for testing the equality of two large U-statistic based correlation
matrices, which include the rank-based correlation matrices as special cases.
Our approach exploits extreme value statistics and the Jackknife estimator for
uncertainty assessment and is valid under a fully nonparametric model.
Theoretically, we develop a theory for testing the equality of U-statistic
based correlation matrices. We then apply this theory to study the problem of
testing large Kendall's tau correlation matrices and demonstrate its
optimality. For proving this optimality, a novel construction of least
favourable distributions is developed for the correlation matrix comparison.
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters
Authors: Yifan Xu, Tianqi Fan, Mingye Xu, Long Zeng, Yu Qiao
Date: 2018-03-30 00:00:00
Proceeding: ECCV 2018 9
Tasks: 3D Part Segmentation, 3D Point Cloud Classification, Deep Learning

Deep neural networks have enjoyed remarkable success for various vision
tasks, however it remains challenging to apply CNNs to domains lacking a
regular underlying structures such as 3D point clouds. Towards this we propose
a novel convolutional architecture, termed SpiderCNN, to efficiently extract
geometric features from point clouds. SpiderCNN is comprised of units called
SpiderConv, which extend convolutional operations from regular grids to
irregular point sets that can be embedded in R^n, by parametrizing a family of
convolutional filters. We design the filter as a product of a simple step
function that captures local geodesic information and a Taylor polynomial that
ensures the expressiveness. SpiderCNN inherits the multi-scale hierarchical
architecture from classical CNNs, which allows it to extract semantic deep
features. Experiments on ModelNet40 demonstrate that SpiderCNN achieves
state-of-the-art accuracy 92.4% on standard benchmarks, and shows competitive
performance on segmentation task.