pwc-archive/papers-with-abstracts huggingface dataset:
ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks
Authors: Kunal Sankhe, Mauro Belgiovine, Fan Zhou, Shamnaz Riyaz, Stratis Ioannidis, Kaushik Chowdhury
Date: 2018-12-03 00:00:00
Tasks: Classification, General Classification

This paper describes the architecture and performance of ORACLE, an approach
for detecting a unique radio from a large pool of bit-similar devices (same
hardware, protocol, physical address, MAC ID) using only IQ samples at the
physical layer. ORACLE trains a convolutional neural network (CNN) that
balances computational time and accuracy, showing 99\% classification accuracy
for a 16-node USRP X310 SDR testbed and an external database of $>$100 COTS
WiFi devices. Our work makes the following contributions: (i) it studies the
hardware-centric features within the transmitter chain that causes IQ sample
variations; (ii) for an idealized static channel environment, it proposes a CNN
architecture requiring only raw IQ samples accessible at the front-end, without
channel estimation or prior knowledge of the communication protocol; (iii) for
dynamic channels, it demonstrates a principled method of feedback-driven
transmitter-side modifications that uses channel estimation at the receiver to
increase differentiability for the CNN classifier. The key innovation here is
to intentionally introduce controlled imperfections on the transmitter side
through software directives, while minimizing the change in bit error rate.
Unlike previous work that imposes constant environmental conditions, ORACLE
adopts the `train once deploy anywhere' paradigm with near-perfect device
classification accuracy.
On learning with shift-invariant structures
Authors: Cristian Rusu
Date: 2018-12-03 00:00:00
Tasks: Dictionary Learning, Retrieval

We describe new results and algorithms for two different, but related, problems which deal with circulant matrices: learning shift-invariant components from training data and calculating the shift (or alignment) between two given signals. In the first instance, we deal with the shift-invariant dictionary learning problem while the latter bears the name of (compressive) shift retrieval. We formulate these problems using circulant and convolutional matrices (including unions of such matrices), define optimization problems that describe our goals and propose efficient ways to solve them. Based on these findings, we also show how to learn a wavelet-like dictionary from training data. We connect our work with various previous results from the literature and we show the effectiveness of our proposed algorithms using synthetic, ECG signals and images.
Exploring galaxy evolution with generative models
Authors: Kevin Schawinski, M. Dennis Turp, Ce Zhang
Date: 2018-12-03 00:00:00

Context. Generative models open up the possibility to interrogate scientific
data in a more data-driven way. Aims: We propose a method that uses generative
models to explore hypotheses in astrophysics and other areas. We use a neural
network to show how we can independently manipulate physical attributes by
encoding objects in latent space. Methods: By learning a latent space
representation of the data, we can use this network to forward model and
explore hypotheses in a data-driven way. We train a neural network to generate
artificial data to test hypotheses for the underlying physical processes.
Results: We demonstrate this process using a well-studied process in
astrophysics, the quenching of star formation in galaxies as they move from
low-to high-density environments. This approach can help explore astrophysical
and other phenomena in a way that is different from current methods based on
simulations and observations.
LEAF: A Benchmark for Federated Settings
Authors: Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečný, H. Brendan McMahan, Virginia Smith, Ameet Talwalkar
Date: 2018-12-03 00:00:00
Tasks: Autonomous Vehicles, Benchmarking, Federated Learning, Meta-Learning, Multi-Task Learning

Modern federated networks, such as those comprised of wearable devices, mobile phones, or autonomous vehicles, generate massive amounts of data each day. This wealth of data can help to learn models that can improve the user experience on each device. However, the scale and heterogeneity of federated data presents new challenges in research areas such as federated learning, meta-learning, and multi-task learning. As the machine learning community begins to tackle these challenges, we are at a critical time to ensure that developments made in these areas are grounded with realistic benchmarks. To this end, we propose LEAF, a modular benchmarking framework for learning in federated settings. LEAF includes a suite of open-source federated datasets, a rigorous evaluation framework, and a set of reference implementations, all geared towards capturing the obstacles and intricacies of practical federated environments.
Disease Detection in Weakly Annotated Volumetric Medical Images using a Convolutional LSTM Network
Authors: Nathaniel Braman, David Beymer, Ehsan Dehghan
Date: 2018-12-03 00:00:00
Tasks: Multiple Instance Learning

We explore a solution for learning disease signatures from weakly, yet easily
obtainable, annotated volumetric medical imaging data by analyzing 3D volumes
as a sequence of 2D images. We demonstrate the performance of our solution in
the detection of emphysema in lung cancer screening low-dose CT images. Our
approach utilizes convolutional long short-term memory (LSTM) to "scan"
sequentially through an imaging volume for the presence of disease in a portion
of scanned region. This framework allowed effective learning given only
volumetric images and binary disease labels, thus enabling training from a
large dataset of 6,631 un-annotated image volumes from 4,486 patients. When
evaluated in a testing set of 2,163 volumes from 2,163 patients, our model
distinguished emphysema with area under the receiver operating characteristic
curve (AUC) of .83. This approach was found to outperform 2D convolutional
neural networks (CNN) implemented with various multiple-instance learning
schemes (AUC=0.69-0.76) and a 3D CNN (AUC=.77).
A System for Automated Image Editing from Natural Language Commands
Authors: Jacqueline Brixey, Ramesh Manuvinakurike, Nham Le, Tuan Lai, Walter Chang, Trung Bui
Date: 2018-12-03 00:00:00

This work presents the task of modifying images in an image editing program
using natural language written commands. We utilize a corpus of over 6000 image
edit text requests to alter real world images collected via crowdsourcing. A
novel framework composed of actions and entities to map a user's natural
language request to executable commands in an image editing program is
described. We resolve previously labeled annotator disagreement through a
voting process and complete annotation of the corpus. We experimented with
different machine learning models and found that the LSTM, the SVM, and the
bidirectional LSTM-CRF joint models are the best to detect image editing
actions and associated entities in a given utterance.
ZerNet: Convolutional Neural Networks on Arbitrary Surfaces via Zernike Local Tangent Space Estimation
Authors: Zhiyu Sun, Ethan Rooke, Jerome Charton, Yusen He, Jia Lu, Stephen Baek
Date: 2018-12-03 00:00:00

In this paper, we propose a novel formulation to extend CNNs to two-dimensional (2D) manifolds using orthogonal basis functions, called Zernike polynomials. In many areas, geometric features play a key role in understanding scientific phenomena. Thus, an ability to codify geometric features into a mathematical quantity can be critical. Recently, convolutional neural networks (CNNs) have demonstrated the promising capability of extracting and codifying features from visual information. However, the progress has been concentrated in computer vision applications where there exists an inherent grid-like structure. In contrast, many geometry processing problems are defined on curved surfaces, and the generalization of CNNs is not quite trivial. The difficulties are rooted in the lack of key ingredients such as the canonical grid-like representation, the notion of consistent orientation, and a compatible local topology across the domain. In this paper, we prove that the convolution of two functions can be represented as a simple dot product between Zernike polynomial coefficients; and the rotation of a convolution kernel is essentially a set of 2-by-2 rotation matrices applied to the coefficients. As such, the key contribution of this work resides in a concise but rigorous mathematical generalization of the CNN building blocks.
Crowd Sourcing based Active Learning Approach for Parking Sign Recognition
Authors: Humayun Irshad, Qazaleh Mirsharif, Jennifer Prendki
Date: 2018-12-03 00:00:00
Tasks: Active Learning, BIG-bench Machine Learning, Transfer Learning

Deep learning models have been used extensively to solve real-world problems
in recent years. The performance of such models relies heavily on large amounts
of labeled data for training. While the advances of data collection technology
have enabled the acquisition of a massive volume of data, labeling the data
remains an expensive and time-consuming task. Active learning techniques are
being progressively adopted to accelerate the development of machine learning
solutions by allowing the model to query the data they learn from. In this
paper, we introduce a real-world problem, the recognition of parking signs, and
present a framework that combines active learning techniques with a transfer
learning approach and crowd-sourcing tools to create and train a machine
learning solution to the problem. We discuss how such a framework contributes
to building an accurate model in a cost-effective and fast way to solve the
parking sign recognition problem in spite of the unevenness of the data
associated with the fact that street-level images (such as parking signs) vary
in shape, color, orientation and scale, and often appear on top of different
types of background.
Brief survey of Mobility Analyses based on Mobile Phone Datasets
Authors: Carlos Sarraute, Martin Minnoni
Date: 2018-12-03 00:00:00
Tasks: Survey

This is a brief survey of the research performed by Grandata Labs in
collaboration with numerous academic groups around the world on the topic of
human mobility. A driving theme in these projects is to use and improve Data
Science techniques to understand mobility, as it can be observed through the
lens of mobile phone datasets. We describe applications of mobility analyses
for urban planning, prediction of data traffic usage, building delay tolerant
networks, generating epidemiologic risk maps and measuring the predictability
of human mobility.
Distilling Information from a Flood: A Possibility for the Use of Meta-Analysis and Systematic Review in Machine Learning Research
Authors: Peter Henderson, Emma Brunskill
Date: 2018-12-03 00:00:00
Tasks: BIG-bench Machine Learning, Epidemiology, Reinforcement Learning

The current flood of information in all areas of machine learning research,
from computer vision to reinforcement learning, has made it difficult to make
aggregate scientific inferences. It can be challenging to distill a myriad of
similar papers into a set of useful principles, to determine which new
methodologies to use for a particular application, and to be confident that one
has compared against all relevant related work when developing new ideas.
However, such a rapidly growing body of research literature is a problem that
other fields have already faced - in particular, medicine and epidemiology. In
those fields, systematic reviews and meta-analyses have been used exactly for
dealing with these issues and it is not uncommon for entire journals to be
dedicated to such analyses. Here, we suggest the field of machine learning
might similarly benefit from meta-analysis and systematic review, and we
encourage further discussion and development along this direction.
Semantic Image Inpainting Through Improved Wasserstein Generative Adversarial Networks
Authors: Patricia Vitoria, Joan Sintes, Coloma Ballester
Date: 2018-12-03 00:00:00
Tasks: Generative Adversarial Network, Image Inpainting

Image inpainting is the task of filling-in missing regions of a damaged or
incomplete image. In this work we tackle this problem not only by using the
available visual data but also by incorporating image semantics through the use
of generative models. Our contribution is twofold: First, we learn a data
latent space by training an improved version of the Wasserstein generative
adversarial network, for which we incorporate a new generator and discriminator
architecture. Second, the learned semantic information is combined with a new
optimization loss for inpainting whose minimization infers the missing content
conditioned by the available data. It takes into account powerful contextual
and perceptual content inherent in the image itself. The benefits include the
ability to recover large regions by accumulating semantic information even it
is not fully present in the damaged image. Experiments show that the presented
method obtains qualitative and quantitative top-tier results in different
experimental situations and also achieves accurate photo-realism comparable to
state-of-the-art works.
Learning Multimodal Graph-to-Graph Translation for Molecular Optimization
Authors: Wengong Jin, Kevin Yang, Regina Barzilay, Tommi Jaakkola
Date: 2018-12-03 00:00:00
Tasks: Decoder, Graph-To-Graph Translation, Translation

We view molecular optimization as a graph-to-graph translation problem. The
goal is to learn to map from one molecular graph to another with better
properties based on an available corpus of paired molecules. Since molecules
can be optimized in different ways, there are multiple viable translations for
each input graph. A key challenge is therefore to model diverse translation
outputs. Our primary contributions include a junction tree encoder-decoder for
learning diverse graph translations along with a novel adversarial training
method for aligning distributions of molecules. Diverse output distributions in
our model are explicitly realized by low-dimensional latent vectors that
modulate the translation process. We evaluate our model on multiple molecular
optimization tasks and show that our model outperforms previous
state-of-the-art baselines.
Machine Friendly Machine Learning: Interpretation of Computed Tomography Without Image Reconstruction
Authors: Hyunkwang Lee, Chao Huang, Sehyo Yune, Shahein H. Tajmir, Myeongchan Kim, Synho Do
Date: 2018-12-03 00:00:00
Tasks: BIG-bench Machine Learning, Computed Tomography (CT), Deep Learning, General Classification, Image Reconstruction, Medical Image Analysis

Recent advancements in deep learning for automated image processing and
classification have accelerated many new applications for medical image
analysis. However, most deep learning applications have been developed using
reconstructed, human-interpretable medical images. While image reconstruction
from raw sensor data is required for the creation of medical images, the
reconstruction process only uses a partial representation of all the data
acquired. Here we report the development of a system to directly process raw
computed tomography (CT) data in sinogram-space, bypassing the intermediary
step of image reconstruction. Two classification tasks were evaluated for their
feasibility for sinogram-space machine learning: body region identification and
intracranial hemorrhage (ICH) detection. Our proposed SinoNet performed
favorably compared to conventional reconstructed image-space-based systems for
both tasks, regardless of scanning geometries in terms of projections or
detectors. Further, SinoNet performed significantly better when using sparsely
sampled sinograms than conventional networks operating in image-space. As a
result, sinogram-space algorithms could be used in field settings for binary
diagnosis testing, triage, and in clinical settings where low radiation dose is
desired. These findings also demonstrate another strength of deep learning
where it can analyze and interpret sinograms that are virtually impossible for
human experts.
QR code denoising using parallel Hopfield networks
Authors: Ishan Bhatnagar, Shubhang Bhatnagar
Date: 2018-12-03 00:00:00
Tasks: Denoising

We propose a novel algorithm for using Hopfield networks to denoise QR codes.
Hopfield networks have mostly been used as a noise tolerant memory or to solve
difficult combinatorial problems. One of the major drawbacks in their use in
noise tolerant associative memory is their low capacity of storage, scaling
only linearly with the number of nodes in the network. A larger capacity
therefore requires a larger number of nodes, thereby reducing the speed of
convergence of the network in addition to increasing hardware costs for
acquiring more precise data to be fed to a larger number of nodes. Our paper
proposes a new algorithm to allow the use of several Hopfield networks in
parallel thereby increasing the cumulative storage capacity of the system many
times as compared to a single Hopfield network. Our algorithm would also be
much faster than a larger single Hopfield network with the same total capacity.
This enables their use in applications like denoising QR codes, which we have
demonstrated in our paper. We then test our network on a large set of QR code
images with different types of noise and demonstrate that such a system of
Hopfield networks can be used to denoise and recognize QR codes in real time.
A Hybrid Instance-based Transfer Learning Method
Authors: Azin Asgarian, Parinaz Sobhani, Ji Chao Zhang, Madalin Mihailescu, Ariel Sibilia, Ahmed Bilal Ashraf, Babak Taati
Date: 2018-12-03 00:00:00
Tasks: Transfer Learning

In recent years, supervised machine learning models have demonstrated
tremendous success in a variety of application domains. Despite the promising
results, these successful models are data hungry and their performance relies
heavily on the size of training data. However, in many healthcare applications
it is difficult to collect sufficiently large training datasets. Transfer
learning can help overcome this issue by transferring the knowledge from
readily available datasets (source) to a new dataset (target). In this work, we
propose a hybrid instance-based transfer learning method that outperforms a set
of baselines including state-of-the-art instance-based transfer learning
approaches. Our method uses a probabilistic weighting strategy to fuse
information from the source domain to the model learned in the target domain.
Our method is generic, applicable to multiple source domains, and robust with
respect to negative transfer. We demonstrate the effectiveness of our approach
through extensive experiments for two different applications.
Bach2Bach: Generating Music Using A Deep Reinforcement Learning Approach
Authors: Nikhil Kotecha
Date: 2018-12-03 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A model of music needs to have the ability to recall past details and have a
clear, coherent understanding of musical structure. Detailed in the paper is a
deep reinforcement learning architecture that predicts and generates polyphonic
music aligned with musical rules. The probabilistic model presented is a
Bi-axial LSTM trained with a pseudo-kernel reminiscent of a convolutional
kernel. To encourage exploration and impose greater global coherence on the
generated music, a deep reinforcement learning approach DQN is adopted. When
analyzed quantitatively and qualitatively, this approach performs well in
composing polyphonic music.
Transferring Knowledge across Learning Processes
Authors: Sebastian Flennerhag, Pablo G. Moreno, Neil D. Lawrence, Andreas Damianou
Date: 2018-12-03 00:00:00
Proceeding: ICLR 2019 5
Tasks: Meta-Learning, Reinforcement Learning, Transfer Learning

In complex transfer learning scenarios new tasks might not be tightly linked
to previous tasks. Approaches that transfer information contained only in the
final parameters of a source model will therefore struggle. Instead, transfer
learning at a higher level of abstraction is needed. We propose Leap, a
framework that achieves this by transferring knowledge across learning
processes. We associate each task with a manifold on which the training process
travels from initialization to final parameters and construct a meta-learning
objective that minimizes the expected length of this path. Our framework
leverages only information obtained during training and can be computed on the
fly at negligible cost. We demonstrate that our framework outperforms competing
methods, both in meta-learning and transfer learning, on a set of computer
vision tasks. Finally, we demonstrate that Leap can transfer knowledge across
learning processes in demanding reinforcement learning environments (Atari)
that involve millions of gradient steps.
MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language
Authors: Hamid Reza Vaezi Joze, Oscar Koller
Date: 2018-12-03 00:00:00
Tasks: Action Recognition, Sign Language Recognition, Temporal Action Localization, Video Classification

Sign language recognition is a challenging and often underestimated problem comprising multi-modal articulators (handshape, orientation, movement, upper body and face) that integrate asynchronously on multiple streams. Learning powerful statistical models in such a scenario requires much data, particularly to apply recent advances of the field. However, labeled data is a scarce resource for sign language due to the enormous cost of transcribing these unwritten languages. We propose the first real-life large-scale sign language data set comprising over 25,000 annotated videos, which we thoroughly evaluate with state-of-the-art methods from sign and related action recognition. Unlike the current state-of-the-art, the data set allows to investigate the generalization to unseen individuals (signer-independent test) in a realistic setting with over 200 signers. Previous work mostly deals with limited vocabulary tasks, while here, we cover a large class count of 1000 signs in challenging and unconstrained real-life recording conditions. We further propose I3D, known from video classifications, as a powerful and suitable architecture for sign language recognition, outperforming the current state-of-the-art by a large margin. The data set is publicly available to the community.
Disentangling Latent Hands for Image Synthesis and Pose Estimation
Authors: Linlin Yang, Angela Yao
Date: 2018-12-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Generation, Pose Estimation

Hand image synthesis and pose estimation from RGB images are both highly
challenging tasks due to the large discrepancy between factors of variation
ranging from image background content to camera viewpoint. To better analyze
these factors of variation, we propose the use of disentangled representations
and a disentangled variational autoencoder (dVAE) that allows for specific
sampling and inference of these factors. The derived objective from the
variational lower bound as well as the proposed training strategy are highly
flexible, allowing us to handle cross-modal encoders and decoders as well as
semi-supervised learning scenarios. Experiments show that our dVAE can
synthesize highly realistic images of the hand specifiable by both pose and
image background content and also estimate 3D hand poses from RGB images with
accuracy competitive with state-of-the-art on two public benchmarks.