pwc-archive/papers-with-abstracts huggingface dataset:
On Maintaining Linear Convergence of Distributed Learning and Optimization under Limited Communication
Authors: Sindri Magnússon, Hossein Shokri-Ghadikolaei, Na Li
Date: 2019-02-26 00:00:00
Tasks: BIG-bench Machine Learning, Distributed Optimization

In distributed optimization and machine learning, multiple nodes coordinate to solve large problems. To do this, the nodes need to compress important algorithm information to bits so that it can be communicated over a digital channel. The communication time of these algorithms follows a complex interplay between a) the algorithm's convergence properties, b) the compression scheme, and c) the transmission rate offered by the digital channel. We explore these relationships for a general class of linearly convergent distributed algorithms. In particular, we illustrate how to design quantizers for these algorithms that compress the communicated information to a few bits while still preserving the linear convergence. Moreover, we characterize the communication time of these algorithms as a function of the available transmission rate. We illustrate our results on learning algorithms using different communication structures, such as decentralized algorithms where a single master coordinates information from many workers and fully distributed algorithms where only neighbours in a communication graph can communicate. We conclude that a co-design of machine learning and communication protocols are mandatory to flourish machine learning over networks.
Bengali Handwritten Character Classification using Transfer Learning on Deep Convolutional Neural Network
Authors: Swagato Chatterjee, Rwik Kumar Dutta, Debayan Ganguly, Kingshuk Chatterjee, Sudipta Roy
Date: 2019-02-25 00:00:00
Tasks: Ensemble Learning, General Classification, Transfer Learning

In this paper, we propose a solution which uses state-of-the-art techniques
in Deep Learning to tackle the problem of Bengali Handwritten Character
Recognition ( HCR ). Our method uses lesser iterations to train than most other
comparable methods. We employ Transfer Learning on ResNet 50, a
state-of-the-art deep Convolutional Neural Network Model, pretrained on
ImageNet dataset. We also use other techniques like a modified version of One
Cycle Policy, varying the input image sizes etc. to ensure that our training
occurs fast. We use the BanglaLekha-Isolated Dataset for evaluation of our
technique which consists of 84 classes (50 Basic, 10 Numerals and 24 Compound
Characters). We are able to achieve 96.12% accuracy in just 47 epochs on
BanglaLekha-Isolated dataset. When comparing our method with that of other
researchers, considering number of classes and without using Ensemble Learning,
the proposed solution achieves state of the art result for Handwritten Bengali
Character Recognition. Code and weight files are available at
https://github.com/swagato-c/bangla-hwcr-present.
Generative Models for Low-Rank Video Representation and Reconstruction
Authors: Rakib Hyder, M. Salman Asif
Date: 2019-02-25 00:00:00

Finding compact representation of videos is an essential component in almost
every problem related to video processing or understanding. In this paper, we
propose a generative model to learn compact latent codes that can efficiently
represent and reconstruct a video sequence from its missing or under-sampled
measurements. We use a generative network that is trained to map a compact code
into an image. We first demonstrate that if a video sequence belongs to the
range of the pretrained generative network, then we can recover it by
estimating the underlying compact latent codes. Then we demonstrate that even
if the video sequence does not belong to the range of a pretrained network, we
can still recover the true video sequence by jointly updating the latent codes
and the weights of the generative network. To avoid overfitting in our model,
we regularize the recovery problem by imposing low-rank and similarity
constraints on the latent codes of the neighboring frames in the video
sequence. We use our methods to recover a variety of videos from compressive
measurements at different compression rates. We also demonstrate that we can
generate missing frames in a video sequence by interpolating the latent codes
of the observed frames in the low-dimensional space.
Image Classification on IoT Edge Devices: Profiling and Modeling
Authors: Salma Abdel Magid, Francesco Petrini, Behnam Dezfouli
Date: 2019-02-24 00:00:00
Tasks: Classification, Edge-computing, General Classification, image-classification, Image Classification

With the advent of powerful, low-cost IoT systems, processing data closer to where the data originates, known as edge computing, has become an increasingly viable option. In addition to lowering the cost of networking infrastructures, edge computing reduces edge-cloud delay, which is essential for mission-critical applications. In this paper, we show the feasibility and study the performance of image classification using IoT devices. Specifically, we explore the relationships between various factors of image classification algorithms that may affect energy consumption such as dataset size, image resolution, algorithm type, algorithm phase, and device hardware. Our experiments show a strong, positive linear relationship between three predictor variables, namely model complexity, image resolution, and dataset size, with respect to energy consumption. In addition, in order to provide a means of predicting the energy consumption of an edge device performing image classification, we investigate the usage of three machine learning algorithms using the data generated from our experiments. The performance as well as the trade offs for using linear regression, Gaussian process, and random forests are discussed and validated. Our results indicate that the random forest model outperforms the two former algorithms, with an R-squared value of 0.95 and 0.79 for two different validation datasets.
Deep Learning in Cardiology
Authors: Paschalis Bizopoulos, Dimitrios Koutsouris
Date: 2019-02-22 00:00:00
Tasks: Deep Learning, Representation Learning

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.
autoAx: An Automatic Design Space Exploration and Circuit Building Methodology utilizing Libraries of Approximate Components
Authors: Vojtech Mrazek, Muhammad Abdullah Hanif, Zdenek Vasicek, Lukas Sekanina, Muhammad Shafique
Date: 2019-02-22 00:00:00

Approximate computing is an emerging paradigm for developing highly
energy-efficient computing systems such as various accelerators. In the
literature, many libraries of elementary approximate circuits have already been
proposed to simplify the design process of approximate accelerators. Because
these libraries contain from tens to thousands of approximate implementations
for a single arithmetic operation it is intractable to find an optimal
combination of approximate circuits in the library even for an application
consisting of a few operations. An open problem is "how to effectively combine
circuits from these libraries to construct complex approximate accelerators".
This paper proposes a novel methodology for searching, selecting and combining
the most suitable approximate circuits from a set of available libraries to
generate an approximate accelerator for a given application. To enable fast
design space generation and exploration, the methodology utilizes machine
learning techniques to create computational models estimating the overall
quality of processing and hardware cost without performing full synthesis at
the accelerator level. Using the methodology, we construct hundreds of
approximate accelerators (for a Sobel edge detector) showing different but
relevant tradeoffs between the quality of processing and hardware cost and
identify a corresponding Pareto-frontier. Furthermore, when searching for
approximate implementations of a generic Gaussian filter consisting of 17
arithmetic operations, the proposed approach allows us to identify
approximately $10^3$ highly important implementations from $10^{23}$ possible
solutions in a few hours, while the exhaustive search would take four months on
a high-end processor.
CMR motion artifact correction using generative adversarial nets
Authors: Yunxuan Zhang, Weiliang Zhang, Qinyan Zhang, Jijiang Yang, Xiuyu Chen, Shihua Zhao
Date: 2019-02-21 00:00:00
Tasks: Deep Learning, Edge Detection

Cardiovascular Magnetic Resonance (CMR) plays an important role in the
diagnoses and treatment of cardiovascular diseases while motion artifacts which
are formed during the scanning process of CMR seriously affects doctors to find
the exact focus. The current correction methods mainly focus on the K-space
which is a grid of raw data obtained from the MR signal directly and then
transfer to CMR image by inverse Fourier transform. They are neither effective
nor efficient and can not be utilized in clinic. In this paper, we propose a
novel approach for CMR motion artifact correction using deep learning.
Specially, we use deep residual network (ResNet) as net framework and train our
model in adversarial manner. Our approach is motivated by the connection
between image motion blur and CMR motion artifact, so we can transfer methods
from motion-deblur where deep learning has made great progress to CMR
motion-correction successfully. To evaluate motion artifact correction methods,
we propose a novel algorithm on how edge detection results are improved by
deblurred algorithm. Boosted by deep learning and adversarial training
algorithm, our model is trainable in an end-to-end manner, can be tested in
real-time and achieves the state-of-art results for CMR correction.
Artist Style Transfer Via Quadratic Potential
Authors: Rahul Bhalley, Jianlin Su
Date: 2019-02-14 00:00:00
Tasks: Style Transfer

In this paper we address the problem of artist style transfer where the
painting style of a given artist is applied on a real world photograph. We
train our neural networks in adversarial setting via recently introduced
quadratic potential divergence for stable learning process. To further improve
the quality of generated artist stylized images we also integrate some of the
recently introduced deep learning techniques in our method. To our best
knowledge this is the first attempt towards artist style transfer via quadratic
potential divergence. We provide some stylized image samples in the
supplementary material. The source code for experimentation was written in
PyTorch and is available online in my GitHub repository.
Earthquake magnitude prediction in Hindukush region using machine learning techniques
Authors: Khawaja Asim, Francisco Martínez-Álvarez, Abdul Basit, Talat Iqbal
Date: 2016-09-08 00:00:00
Proceeding: Springer 2016 9
Tasks: BIG-bench Machine Learning, Binary Classification, Earthquake prediction, Prediction, Specificity

Earthquake magnitude prediction for Hindukush region has been carried out in this research using the temporal sequence of historic seismic activities in combination with the machine learning classifiers. Prediction has been made on the basis of mathematically calculated eight seismic indicators using the earthquake catalog of the region. These parameters are based on the well-known geophysical facts of Gutenberg–Richter’s inverse law, distribution of characteristic earthquake magnitudes and seismic quiescence. In this research, four machine learning techniques including pattern recognition neural network, recurrent neural network, random forest and linear programming boost ensemble classifier are separately applied to model relationships between calculated seismic parameters and future earthquake occurrences. The problem is formulated as a binary classification task and predictions are made for earthquakes of magnitude greater than or equal to 5.5 (\(M \ge\) 5.5), for the duration of 1 month. Furthermore, the analysis of earthquake prediction results is carried out for every machine learning classifier in terms of sensitivity, specificity, true and false predictive values. Accuracy is another performance measure considered for analyzing the results. Earthquake magnitude prediction for the Hindukush using these aforementioned techniques show significant and encouraging results, thus constituting a step forward toward the final robust prediction mechanism which is not available so far.
Adaptive Masked Proxies for Few-Shot Segmentation
Authors: Mennatullah Siam, Boris Oreshkin, Martin Jagersand
Date: 2019-02-19 00:00:00
Tasks: Continual Learning, Few-Shot Semantic Segmentation, Segmentation, Semantic Segmentation, Video Object Segmentation, Video Semantic Segmentation

Deep learning has thrived by training on large-scale datasets. However, in robotics applications sample efficiency is critical. We propose a novel adaptive masked proxies method that constructs the final segmentation layer weights from few labelled samples. It utilizes multi-resolution average pooling on base embeddings masked with the label to act as a positive proxy for the new class, while fusing it with the previously learned class signatures. Our method is evaluated on PASCAL-$5^i$ dataset and outperforms the state-of-the-art in the few-shot semantic segmentation. Unlike previous methods, our approach does not require a second branch to estimate parameters or prototypes, which enables it to be used with 2-stream motion and appearance based segmentation networks. We further propose a novel setup for evaluating continual learning of object segmentation which we name incremental PASCAL (iPASCAL) where our method outperforms the baseline method. Our code is publicly available at https://github.com/MSiam/AdaptiveMaskedProxies.
Do ImageNet Classifiers Generalize to ImageNet?
Authors: Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar
Date: 2019-02-13 00:00:00
Proceeding: NeurIPS Workshop ImageNet_PPF 2021 12
Tasks: General Classification

We build new test sets for the CIFAR-10 and ImageNet datasets. Both benchmarks have been the focus of intense research for almost a decade, raising the danger of overfitting to excessively re-used test sets. By closely following the original dataset creation processes, we test to what extent current classification models generalize to new data. We evaluate a broad range of models and find accuracy drops of 3% - 15% on CIFAR-10 and 11% - 14% on ImageNet. However, accuracy gains on the original test sets translate to larger gains on the new test sets. Our results suggest that the accuracy drops are not caused by adaptivity, but by the models' inability to generalize to slightly "harder" images than those found in the original test sets.
Graph-RISE: Graph-Regularized Image Semantic Embedding
Authors: Da-Cheng Juan, Chun-Ta Lu, Zhen Li, Futang Peng, Aleksei Timofeev, Yi-Ting Chen, Yaxi Gao, Tom Duerig, Andrew Tomkins, Sujith Ravi
Date: 2019-02-14 00:00:00
Tasks: Clustering, General Classification, Graph Learning, image-classification, Image Classification, Image Retrieval, Retrieval, Triplet

Learning image representations to capture fine-grained semantics has been a
challenging and important task enabling many applications such as image search
and clustering. In this paper, we present Graph-Regularized Image Semantic
Embedding (Graph-RISE), a large-scale neural graph learning framework that
allows us to train embeddings to discriminate an unprecedented O(40M)
ultra-fine-grained semantic labels. Graph-RISE outperforms state-of-the-art
image embedding algorithms on several evaluation tasks, including image
classification and triplet ranking. We provide case studies to demonstrate
that, qualitatively, image retrieval based on Graph-RISE effectively captures
semantics and, compared to the state-of-the-art, differentiates nuances at
levels that are closer to human-perception.
Generalising Deep Learning MRI Reconstruction across Different Domains
Authors: Cheng Ouyang, Jo Schlemper, Carlo Biffi, Gavin Seegoolam, Jose Caballero, Anthony N. Price, Joseph V. Hajnal, Daniel Rueckert
Date: 2019-01-31 00:00:00
Tasks: Deep Learning, MRI Reconstruction

We look into robustness of deep learning based MRI reconstruction when tested
on unseen contrasts and organs. We then propose to generalise the network by
training with large publicly-available natural image datasets with synthesised
phase information to achieve high cross-domain reconstruction performance which
is competitive with domain-specific training. To explain its generalisation
mechanism, we have also analysed patch sets for different training datasets.
Automated Detection of Pre-Disaster Building Images from Google Street View
Authors: Chul Min Yeum, Ali Lenjani, Shirley J. Dyke, Ilias Bilionis
Date: 2019-02-13 00:00:00

After a disaster, teams of structural engineers collect vast amounts of
images from damaged buildings to obtain lessons and gain knowledge from the
event. Images of damaged buildings and components provide valuable evidence to
understand the consequences on our structures. However, in many cases, images
of damaged buildings are often captured without sufficient spatial context.
Also, they may be hard to recognize in cases with severe damage. Incorporating
past images showing a pre-disaster condition of such buildings is helpful to
accurately evaluate possible circumstances related to a building's failure. One
of the best resources to observe the pre-disaster condition of the buildings is
Google Street View. A sequence of 360 panorama images which are captured along
streets enables all-around views at each location on the street. Once a user
knows the GPS information near the building, all external views of the building
can be made available. In this study, we develop an automated technique to
extract past building images from 360 panorama images serviced by Google Street
View. Users only need to provide a geo-tagged image, collected near the target
building, and the rest of the process is fully automated. High-quality and
undistorted building images are extracted from past panoramas. Since the
panoramas are collected from various locations near the building along the
street, the user can identify its pre-disaster conditions from the full set of
external views.
Operational Neural Networks
Authors: Serkan Kiranyaz, Turker Ince, Alexandros Iosifidis, Moncef Gabbouj
Date: 2019-02-15 00:00:00
Tasks: Diversity

Feed-forward, fully-connected Artificial Neural Networks (ANNs) or the so-called Multi-Layer Perceptrons (MLPs) are well-known universal approximators. However, their learning performance varies significantly depending on the function or the solution space that they attempt to approximate. This is mainly because of their homogenous configuration based solely on the linear neuron model. Therefore, while they learn very well those problems with a monotonous, relatively simple and linearly separable solution space, they may entirely fail to do so when the solution space is highly nonlinear and complex. Sharing the same linear neuron model with two additional constraints (local connections and weight sharing), this is also true for the conventional Convolutional Neural Networks (CNNs) and, it is, therefore, not surprising that in many challenging problems only the deep CNNs with a massive complexity and depth can achieve the required diversity and the learning performance. In order to address this drawback and also to accomplish a more generalized model over the convolutional neurons, this study proposes a novel network model, called Operational Neural Networks (ONNs), which can be heterogeneous and encapsulate neurons with any set of operators to boost diversity and to learn highly complex and multi-modal functions or spaces with minimal network complexity and training data. Finally, a novel training method is formulated to back-propagate the error through the operational layers of ONNs. Experimental results over highly challenging problems demonstrate the superior learning capabilities of ONNs even with few neurons and hidden layers.
Channel Max Pooling Layer for Fine-Grained Vehicle Classification
Authors: Zhanyu Ma, Dongliang Chang, Xiaoxu Li
Date: 2019-02-14 00:00:00
Tasks: Classification, Fine-Grained Vehicle Classification, General Classification

Deep convolutional networks have recently shown excellent performance on Fine-Grained Vehicle Classification. Based on these existing works, we consider that the back-probation algorithm does not focus on extracting less discriminative feature as much as possible, but focus on that the loss function equals zero. Intuitively, if we can learn less discriminative features, and these features still could fit the training data well, the generalization ability of neural network could be improved. Therefore, we propose a new layer which is placed between fully connected layers and convolutional layers, called as Chanel Max Pooling. The proposed layer groups the features map first and then compress each group into a new feature map by computing maximum of pixels with same positions in the group of feature maps. Meanwhile, the proposed layer has an advantage that it could help neural network reduce massive parameters. Experimental results on two fine-grained vehicle datasets, the Stanford Cars-196 dataset and the Comp Cars dataset, demonstrate that the proposed layer could improve classification accuracies of deep neural networks on fine-grained vehicle classification in the situation that a massive of parameters are reduced. Moreover, it has a competitive performance with the-state-of-art performance on the two datasets.
Actions Generation from Captions
Authors: Xuan Liang, Yida Xu
Date: 2019-02-14 00:00:00
Tasks: Action Generation, Generative Adversarial Network, Time Series, Time Series Analysis

Sequence transduction models have been widely explored in many natural
language processing tasks. However, the target sequence usually consists of
discrete tokens which represent word indices in a given vocabulary. We barely
see the case where target sequence is composed of continuous vectors, where
each vector is an element of a time series taken successively in a temporal
domain. In this work, we introduce a new data set, named Action Generation Data
Set (AGDS) which is specifically designed to carry out the task of
caption-to-action generation. This data set contains caption-action pairs. The
caption is comprised of a sequence of words describing the interactive movement
between two people, and the action is a captured sequence of poses representing
the movement. This data set is introduced to study the ability of generating
continuous sequences through sequence transduction models. We also propose a
model to innovatively combine Multi-Head Attention (MHA) and Generative
Adversarial Network (GAN) together. In our model, we have one generator to
generate actions from captions and three discriminators where each of them is
designed to carry out a unique functionality: caption-action consistency
discriminator, pose discriminator and pose transition discriminator. This novel
design allowed us to achieve plausible generation performance which is
demonstrated in the experiments.