pwc-archive/papers-with-abstracts huggingface dataset:
Informed Group-Sparse Representation for Singing Voice Separation
Authors: Tak-Shing T. Chan, Yi-Hsuan Yang
Date: 2018-01-09 00:00:00
Tasks: Information Retrieval, Music Information Retrieval, Retrieval

Singing voice separation attempts to separate the vocal and instrumental
parts of a music recording, which is a fundamental problem in music information
retrieval. Recent work on singing voice separation has shown that the low-rank
representation and informed separation approaches are both able to improve
separation quality. However, low-rank optimizations are computationally
inefficient due to the use of singular value decompositions. Therefore, in this
paper, we propose a new linear-time algorithm called informed group-sparse
representation, and use it to separate the vocals from music using pitch
annotations as side information. Experimental results on the iKala dataset
confirm the efficacy of our approach, suggesting that the music accompaniment
follows a group-sparse structure given a pre-trained instrumental dictionary.
We also show how our work can be easily extended to accommodate multiple
dictionaries using the DSD100 dataset.
Polar $n$-Complex and $n$-Bicomplex Singular Value Decomposition and Principal Component Pursuit
Authors: Tak-Shing T. Chan, Yi-Hsuan Yang
Date: 2018-01-09 00:00:00

Informed by recent work on tensor singular value decomposition and circulant
algebra matrices, this paper presents a new theoretical bridge that unifies the
hypercomplex and tensor-based approaches to singular value decomposition and
robust principal component analysis. We begin our work by extending the
principal component pursuit to Olariu's polar $n$-complex numbers as well as
their bicomplex counterparts. In so doing, we have derived the polar
$n$-complex and $n$-bicomplex proximity operators for both the $\ell_1$- and
trace-norm regularizers, which can be used by proximal optimization methods
such as the alternating direction method of multipliers. Experimental results
on two sets of audio data show that our algebraically-informed formulation
outperforms tensor robust principal component analysis. We conclude with the
message that an informed definition of the trace norm can bridge the gap
between the hypercomplex and tensor-based approaches. Our approach can be seen
as a general methodology for generating other principal component pursuit
algorithms with proper algebraic structures.
Complex and Quaternionic Principal Component Pursuit and Its Application to Audio Separation
Authors: Tak-Shing T. Chan, Yi-Hsuan Yang
Date: 2018-01-09 00:00:00

Recently, the principal component pursuit has received increasing attention
in signal processing research ranging from source separation to video
surveillance. So far, all existing formulations are real-valued and lack the
concept of phase, which is inherent in inputs such as complex spectrograms or
color images. Thus, in this letter, we extend principal component pursuit to
the complex and quaternionic cases to account for the missing phase
information. Specifically, we present both complex and quaternionic proximity
operators for the $\ell_1$- and trace-norm regularizers. These operators can be
used in conjunction with proximal minimization methods such as the inexact
augmented Lagrange multiplier algorithm. The new algorithms are then applied to
the singing voice separation problem, which aims to separate the singing voice
from the instrumental accompaniment. Results on the iKala and MSD100 datasets
confirmed the usefulness of phase information in principal component pursuit.
Data Augmentation for Brain-Computer Interfaces: Analysis on Event-Related Potentials Data
Authors: Mario Michael Krell, Anett Seeland, Su Kyoung Kim
Date: 2018-01-09 00:00:00
Tasks: Data Augmentation

On image data, data augmentation is becoming less relevant due to the large
amount of available training data and regularization techniques. Common
approaches are moving windows (cropping), scaling, affine distortions, random
noise, and elastic deformations. For electroencephalographic data, the lack of
sufficient training data is still a major issue. We suggest and evaluate
different approaches to generate augmented data using temporal and
spatial/rotational distortions. Our results on the perception of rare stimuli
(P300 data) and movement prediction (MRCP data) show that these approaches are
feasible and can significantly increase the performance of signal processing
chains for brain-computer interfaces by 1% to 6%.
Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks
Authors: Yuhua Chen, Yibin Xie, Zhengwei Zhou, Feng Shi, Anthony G. Christodoulou, Debiao Li
Date: 2018-01-08 00:00:00
Tasks: Deep Learning, Image Super-Resolution, Super-Resolution

Magnetic resonance image (MRI) in high spatial resolution provides detailed
anatomical information and is often necessary for accurate quantitative
analysis. However, high spatial resolution typically comes at the expense of
longer scan time, less spatial coverage, and lower signal to noise ratio (SNR).
Single Image Super-Resolution (SISR), a technique aimed to restore
high-resolution (HR) details from one single low-resolution (LR) input image,
has been improved dramatically by recent breakthroughs in deep learning. In
this paper, we introduce a new neural network architecture, 3D Densely
Connected Super-Resolution Networks (DCSRN) to restore HR features of
structural brain MR images. Through experiments on a dataset with 1,113
subjects, we demonstrate that our network outperforms bicubic interpolation as
well as other deep learning methods in restoring 4x resolution-reduced images.
Near Maximum Likelihood Decoding with Deep Learning
Authors: Eliya Nachmani, Yaron Bachar, Elad Marciano, David Burshtein, Yair Be'ery
Date: 2018-01-08 00:00:00
Tasks: Decoder, Deep Learning

A novel and efficient neural decoder algorithm is proposed. The proposed
decoder is based on the neural Belief Propagation algorithm and the
Automorphism Group. By combining neural belief propagation with permutations
from the Automorphism Group we achieve near maximum likelihood performance for
High Density Parity Check codes. Moreover, the proposed decoder significantly
improves the decoding complexity, compared to our earlier work on the topic. We
also investigate the training process and show how it can be accelerated.
Simulations of the hessian and the condition number show why the learning
process is accelerated. We demonstrate the decoding algorithm for various
linear block codes of length up to 63 bits.
End-to-end detection-segmentation network with ROI convolution
Authors: Zichen Zhang, Min Tang, Dana Cobzas, Dornoosh Zonoobi, Martin Jagersand, Jacob L. Jaremko
Date: 2018-01-08 00:00:00
Tasks: Object Localization, Segmentation

We propose an end-to-end neural network that improves the segmentation accuracy of fully convolutional networks by incorporating a localization unit. This network performs object localization first, which is then used as a cue to guide the training of the segmentation network. We test the proposed method on a segmentation task of small objects on a clinical dataset of ultrasound images. We show that by jointly learning for detection and segmentation, the proposed network is able to improve the segmentation accuracy compared to only learning for segmentation. Code is publicly available at https://github.com/vincentzhang/roi-fcn.
DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
Authors: Wenhan Xiong, Thien Hoang, William Yang Wang
Date: 2017-07-20 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Diversity, Graph Embedding, Knowledge Graph Embedding, Knowledge Graph Embeddings, Knowledge Graphs, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We study the problem of learning to reason in large scale knowledge graphs
(KGs). More specifically, we describe a novel reinforcement learning framework
for learning multi-hop relational paths: we use a policy-based agent with
continuous states based on knowledge graph embeddings, which reasons in a KG
vector space by sampling the most promising relation to extend its path. In
contrast to prior work, our approach includes a reward function that takes the
accuracy, diversity, and efficiency into consideration. Experimentally, we show
that our proposed method outperforms a path-ranking based algorithm and
knowledge graph embedding methods on Freebase and Never-Ending Language
Learning datasets.
End-to-end Adversarial Learning for Generative Conversational Agents
Authors: Oswaldo Ludwig
Date: 2017-11-28 00:00:00
Tasks: Dialogue Generation, Diversity

This paper presents a new adversarial learning method for generative
conversational agents (GCA) besides a new model of GCA. Similar to previous
works on adversarial learning for dialogue generation, our method assumes the
GCA as a generator that aims at fooling a discriminator that labels dialogues
as human-generated or machine-generated; however, in our approach, the
discriminator performs token-level classification, i.e. it indicates whether
the current token was generated by humans or machines. To do so, the
discriminator also receives the context utterances (the dialogue history) and
the incomplete answer up to the current token as input. This new approach makes
possible the end-to-end training by backpropagation. A self-conversation
process enables to produce a set of generated data with more diversity for the
adversarial training. This approach improves the performance on questions not
related to the training data. Experimental results with human and adversarial
evaluations show that the adversarial method yields significant performance
gains over the usual teacher forcing training.
Modeling urbanization patterns with generative adversarial networks
Authors: Adrian Albert, Emanuele Strano, Jasleen Kaur, Marta Gonzalez
Date: 2018-01-08 00:00:00

In this study we propose a new method to simulate hyper-realistic urban
patterns using Generative Adversarial Networks trained with a global urban
land-use inventory. We generated a synthetic urban "universe" that
qualitatively reproduces the complex spatial organization observed in global
urban patterns, while being able to quantitatively recover certain key
high-level urban spatial metrics.
Removal of Batch Effects using Distribution-Matching Residual Networks
Authors: Uri Shaham, Kelly P. Stanton, Jun Zhao, Huamin Li, Khadir Raddassi, Ruth Montgomery, Yuval Kluger
Date: 2016-10-13 00:00:00

Sources of variability in experimentally derived data include measurement
error in addition to the physical phenomena of interest. This measurement error
is a combination of systematic components, originating from the measuring
instrument, and random measurement errors. Several novel biological
technologies, such as mass cytometry and single-cell RNA-seq, are plagued with
systematic errors that may severely affect statistical analysis if the data is
not properly calibrated. We propose a novel deep learning approach for removing
systematic batch effects. Our method is based on a residual network, trained to
minimize the Maximum Mean Discrepancy (MMD) between the multivariate
distributions of two replicates, measured in different batches. We apply our
method to mass cytometry and single-cell RNA-seq datasets, and demonstrate that
it effectively attenuates batch effects.
Intelligent Device Discovery in the Internet of Things - Enabling the Robot Society
Authors: James Sunthonlap, Phuoc Nguyen, Zilong Ye
Date: 2017-12-22 00:00:00
Tasks: Clustering, Sand

The Internet of Things (IoT) is continuously growing to connect billions of
smart devices anywhere and anytime in an Internet-like structure, which enables
a variety of applications, services and interactions between human and objects.
In the future, the smart devices are supposed to be able to autonomously
discover a target device with desired features and generate a set of entirely
new services and applications that are not supervised or even imagined by human
beings. The pervasiveness of smart devices, as well as the heterogeneity of
their design and functionalities, raise a major concern: How can a smart device
efficiently discover a desired target device? In this paper, we propose a
Social-Aware and Distributed (SAND) scheme that achieves a fast, scalable and
efficient device discovery in the IoT. The proposed SAND scheme adopts a novel
device ranking criteria that measures the device's degree, social relationship
diversity, clustering coefficient and betweenness. Based on the device ranking
criteria, the discovery request can be guided to travel through critical
devices that stand at the major intersections of the network, and thus quickly
reach the desired target device by contacting only a limited number of
intermediate devices. With the help of such an intelligent device discovery as
SAND, the IoT devices, as well as other computing facilities, software and data
on the Internet, can autonomously establish new social connections with each
other as human being do. They can formulate self-organized computing groups to
perform required computing tasks, facilitate a fusion of a variety of computing
service, network service and data to generate novel applications and services,
evolve from the individual aritificial intelligence to the collaborative
intelligence, and eventually enable the birth of a robot society.
Deep Supervision with Intermediate Concepts
Authors: Chi Li, M. Zeeshan Zia, Quoc-Huy Tran, Xiang Yu, Gregory D. Hager, Manmohan Chandraker
Date: 2018-01-08 00:00:00
Tasks: image-classification, Image Classification

Recent data-driven approaches to scene interpretation predominantly pose
inference as an end-to-end black-box mapping, commonly performed by a
Convolutional Neural Network (CNN). However, decades of work on perceptual
organization in both human and machine vision suggests that there are often
intermediate representations that are intrinsic to an inference task, and which
provide essential structure to improve generalization. In this work, we explore
an approach for injecting prior domain structure into neural network training
by supervising hidden layers of a CNN with intermediate concepts that normally
are not observed in practice. We formulate a probabilistic framework which
formalizes these notions and predicts improved generalization via this deep
supervision method. One advantage of this approach is that we are able to train
only from synthetic CAD renderings of cluttered scenes, where concept values
can be extracted, but apply the results to real images. Our implementation
achieves the state-of-the-art performance of 2D/3D keypoint localization and
image classification on real image benchmarks, including KITTI, PASCAL VOC,
PASCAL3D+, IKEA, and CIFAR100. We provide additional evidence that our approach
outperforms alternative forms of supervision, such as multi-task networks.
AudioPairBank: Towards A Large-Scale Tag-Pair-Based Audio Content Analysis
Authors: Sebastian Sager, Benjamin Elizalde, Damian Borth, Christian Schulze, Bhiksha Raj, Ian Lane
Date: 2016-07-13 00:00:00
Tasks: TAG

Recently, sound recognition has been used to identify sounds, such as car and
river. However, sounds have nuances that may be better described by
adjective-noun pairs such as slow car, and verb-noun pairs such as flying
insects, which are under explored. Therefore, in this work we investigate the
relation between audio content and both adjective-noun pairs and verb-noun
pairs. Due to the lack of datasets with these kinds of annotations, we
collected and processed the AudioPairBank corpus consisting of a combined total
of 1,123 pairs and over 33,000 audio files. One contribution is the previously
unavailable documentation of the challenges and implications of collecting
audio recordings with these type of labels. A second contribution is to show
the degree of correlation between the audio content and the labels through
sound recognition experiments, which yielded results of 70% accuracy, hence
also providing a performance benchmark. The results and study in this paper
encourage further exploration of the nuances in audio and are meant to
complement similar research performed on images and text in multimedia
analysis.
Pileup Mitigation with Machine Learning (PUMML)
Authors: Patrick T. Komiske, Eric M. Metodiev, Benjamin Nachman, Matthew D. Schwartz
Date: 2017-07-26 00:00:00
Tasks: BIG-bench Machine Learning

Pileup involves the contamination of the energy distribution arising from the
primary collision of interest (leading vertex) by radiation from soft
collisions (pileup). We develop a new technique for removing this contamination
using machine learning and convolutional neural networks. The network takes as
input the energy distribution of charged leading vertex particles, charged
pileup particles, and all neutral particles and outputs the energy distribution
of particles coming from leading vertex alone. The PUMML algorithm performs
remarkably well at eliminating pileup distortion on a wide range of simple and
complex jet observables. We test the robustness of the algorithm in a number of
ways and discuss how the network can be trained directly on data.
Multilayered Model of Speech
Authors: Andrey Chistyakov
Date: 2018-01-08 00:00:00
Tasks: model

Human speech is the most important part of General Artificial Intelligence and subject of much research. The hypothesis proposed in this article provides explanation of difficulties that modern science tackles in the field of human brain simulation. The hypothesis is based on the author's conviction that the brain of any given person has different ability to process and store information. Therefore, the approaches that are currently used to create General Artificial Intelligence have to be altered.
Real-time 3D Shape Instantiation from Single Fluoroscopy Projection for Fenestrated Stent Graft Deployment
Authors: Xiao-Yun Zhou, Jianyu Lin, Celia Riga, Guang-Zhong Yang, Su-Lin Lee
Date: 2017-09-22 00:00:00

Robot-assisted deployment of fenestrated stent grafts in Fenestrated
Endovascular Aortic Repair (FEVAR) requires accurate geometrical alignment.
Currently, this process is guided by 2D fluoroscopy, which is uninformative and
error prone. In this paper, a real-time framework is proposed to instantiate
the 3D shape of a fenestrated stent graft based on only a single low-dose 2D
fluoroscopic image. Firstly, the fenestrated stent graft was placed with
markers. Secondly, the 3D pose of each stent segment was instantiated by the
RPnP (Robust Perspective-n-Point) method. Thirdly, the 3D shape of the whole
stent graft was instantiated via graft gap interpolation. Focal-Unet was
proposed to segment the markers from 2D fluoroscopic images to achieve
semi-automatic marker detection. The proposed framework was validated on five
patient-specific 3D printed phantoms of aortic aneurysms and three stent grafts
with new marker placements, showing an average distance error of 1-3mm and an
average angle error of 4 degree.
Generative Sensing: Transforming Unreliable Sensor Data for Reliable Recognition
Authors: Lina Karam, Tejas Borkar, Yu Cao, Junseok Chae
Date: 2018-01-08 00:00:00
Tasks: Image Generation

This paper introduces a deep learning enabled generative sensing framework
which integrates low-end sensors with computational intelligence to attain a
high recognition accuracy on par with that attained with high-end sensors. The
proposed generative sensing framework aims at transforming low-end, low-quality
sensor data into higher quality sensor data in terms of achieved classification
accuracy. The low-end data can be transformed into higher quality data of the
same modality or into data of another modality. Different from existing methods
for image generation, the proposed framework is based on discriminative models
and targets to maximize the recognition accuracy rather than a similarity
measure. This is achieved through the introduction of selective feature
regeneration in a deep neural network (DNN). The proposed generative sensing
will essentially transform low-quality sensor data into high-quality
information for robust perception. Results are presented to illustrate the
performance of the proposed framework.
Solutions to problems with deep learning
Authors: J. Gerard Wolff
Date: 2018-01-08 00:00:00
Tasks: Deep Learning

Despite the several successes of deep learning systems, there are concerns
about their limitations, discussed most recently by Gary Marcus. This paper
discusses Marcus's concerns and some others, together with solutions to several
of these problems provided by the "P theory of intelligence" and its
realisation in the "SP computer model". The main advantages of the SP system
are: relatively small requirements for data and the ability to learn from a
single experience; the ability to model both hierarchical and non-hierarchical
structures; strengths in several kinds of reasoning, including `commonsense'
reasoning; transparency in the representation of knowledge, and the provision
of an audit trail for all processing; the likelihood that the SP system could
not be fooled into bizarre or eccentric recognition of stimuli, as deep
learning systems can be; the SP system provides a robust solution to the
problem of `catastrophic forgetting' in deep learning systems; the SP system
provides a theoretically-coherent solution to the problems of correcting over-
and under-generalisations in learning, and learning correct structures despite
errors in data; unlike most research on deep learning, the SP programme of
research draws extensively on research on human learning, perception, and
cognition; and the SP programme of research has an overarching theory,
supported by evidence, something that is largely missing from research on deep
learning. In general, the SP system provides a much firmer foundation than deep
learning for the development of artificial general intelligence.
Synthetic Medical Images from Dual Generative Adversarial Networks
Authors: John T. Guibas, Tejpal S. Virdi, Peter S. Li
Date: 2017-09-06 00:00:00
Tasks: image-classification, Image Classification, Image Generation, Medical Image Classification, Medical Image Generation

Currently there is strong interest in data-driven approaches to medical image
classification. However, medical imaging data is scarce, expensive, and fraught
with legal concerns regarding patient privacy. Typical consent forms only allow
for patient data to be used in medical journals or education, meaning the
majority of medical data is inaccessible for general public research. We
propose a novel, two-stage pipeline for generating synthetic medical images
from a pair of generative adversarial networks, tested in practice on retinal
fundi images. We develop a hierarchical generation process to divide the
complex image generation task into two parts: geometry and photorealism. We
hope researchers will use our pipeline to bring private medical data into the
public domain, sparking growth in imaging tasks that have previously relied on
the hand-tuning of models. We have begun this initiative through the
development of SynthMed, an online repository for synthetic medical images.