pwc-archive/papers-with-abstracts huggingface dataset:
Partition Pruning: Parallelization-Aware Pruning for Deep Neural Networks
Authors: Sina Shahhosseini, Ahmad Albaqsami, Masoomeh Jasemi, Nader Bagherzadeh
Date: 2019-01-21 00:00:00

Parameters of recent neural networks require a huge amount of memory. These
parameters are used by neural networks to perform machine learning tasks when
processing inputs. To speed up inference, we develop Partition Pruning, an
innovative scheme to reduce the parameters used while taking into consideration
parallelization. We evaluated the performance and energy consumption of
parallel inference of partitioned models, which showed a 7.72x speed up of
performance and a 2.73x reduction in the energy used for computing pruned
layers of TinyVGG16 in comparison to running the unpruned model on a single
accelerator. In addition, our method showed a limited reduction some numbers in
accuracy while partitioning fully connected layers.
Hotels-50K: A Global Hotel Recognition Dataset
Authors: Abby Stylianou, Hong Xuan, Maya Shende, Jonathan Brandt, Richard Souvenir, Robert Pless
Date: 2019-01-26 00:00:00
Tasks: Data Augmentation

Recognizing a hotel from an image of a hotel room is important for human
trafficking investigations. Images directly link victims to places and can help
verify where victims have been trafficked, and where their traffickers might
move them or others in the future. Recognizing the hotel from images is
challenging because of low image quality, uncommon camera perspectives, large
occlusions (often the victim), and the similarity of objects (e.g., furniture,
art, bedding) across different hotel rooms.
  To support efforts towards this hotel recognition task, we have curated a
dataset of over 1 million annotated hotel room images from 50,000 hotels. These
images include professionally captured photographs from travel websites and
crowd-sourced images from a mobile application, which are more similar to the
types of images analyzed in real-world investigations. We present a baseline
approach based on a standard network architecture and a collection of
data-augmentation approaches tuned to this problem domain.
Learning to Clean: A GAN Perspective
Authors: Monika Sharma, Abhishek Verma, Lovekesh Vig
Date: 2019-01-28 00:00:00
Tasks: Denoising, Image-to-Image Translation, Translation

In the big data era, the impetus to digitize the vast reservoirs of data
trapped in unstructured scanned documents such as invoices, bank documents and
courier receipts has gained fresh momentum. The scanning process often results
in the introduction of artifacts such as background noise, blur due to camera
motion, watermarkings, coffee stains, or faded text. These artifacts pose many
readability challenges to current text recognition algorithms and significantly
degrade their performance. Existing learning based denoising techniques require
a dataset comprising of noisy documents paired with cleaned versions. In such
scenarios, a model can be trained to generate clean documents from noisy
versions. However, very often in the real world such a paired dataset is not
available, and all we have for training our denoising model are unpaired sets
of noisy and clean images. This paper explores the use of GANs to generate
denoised versions of the noisy documents. In particular, where paired
information is available, we formulate the problem as an image-to-image
translation task i.e, translating a document from noisy domain ( i.e.,
background noise, blurred, faded, watermarked ) to a target clean document
using Generative Adversarial Networks (GAN). However, in the absence of paired
images for training, we employed CycleGAN which is known to learn a mapping
between the distributions of the noisy images to the denoised images using
unpaired data to achieve image-to-image translation for cleaning the noisy
documents. We compare the performance of CycleGAN for document cleaning tasks
using unpaired images with a Conditional GAN trained on paired data from the
same dataset. Experiments were performed on a public document dataset on which
different types of noise were artificially induced, results demonstrate that
CycleGAN learns a more robust mapping from the space of noisy to clean
documents.
Learning to navigate image manifolds induced by generative adversarial networks for unsupervised video generation
Authors: Isabela Albuquerque, João Monteiro, Tiago H. Falk
Date: 2019-01-23 00:00:00
Tasks: Navigate, Video Generation

In this work, we introduce a two-step framework for generative modeling of
temporal data. Specifically, the generative adversarial networks (GANs) setting
is employed to generate synthetic scenes of moving objects. To do so, we
propose a two-step training scheme within which: a generator of static frames
is trained first. Afterwards, a recurrent model is trained with the goal of
providing a sequence of inputs to the previously trained frames generator, thus
yielding scenes which look natural. The adversarial setting is employed in both
training steps. However, with the aim of avoiding known training instabilities
in GANs, a multiple discriminator approach is used to train both models.
Results in the studied video dataset indicate that, by employing such an
approach, the recurrent part is able to learn how to coherently navigate the
image manifold induced by the frames generator, thus yielding more
natural-looking scenes.
Improving generation quality of pointer networks via guided attention
Authors: Kushal Chawla, Kundan Krishna, Balaji Vasan Srinivasan
Date: 2019-01-20 00:00:00
Tasks: Abstractive Text Summarization

Pointer generator networks have been used successfully for abstractive
summarization. Along with the capability to generate novel words, it also
allows the model to copy from the input text to handle out-of-vocabulary words.
In this paper, we point out two key shortcomings of the summaries generated
with this framework via manual inspection, statistical analysis and human
evaluation. The first shortcoming is the extractive nature of the generated
summaries, since the network eventually learns to copy from the input article
most of the times, affecting the abstractive nature of the generated summaries.
The second shortcoming is the factual inaccuracies in the generated text
despite grammatical correctness. Our analysis indicates that this arises due to
incorrect attention transition between different parts of the article. We
propose an initial attempt towards addressing both these shortcomings by
externally appending traditional linguistic information parsed from the input
text, thereby teaching networks on the structure of the underlying text.
Results indicate feasibility and potential of such additional cues for improved
generation.
Deep Recurrent Factor Model: Interpretable Non-Linear and Time-Varying Multi-Factor Model
Authors: Kei Nakagawa, Tomoki Ito, Masaya Abe, Kiyoshi Izumi
Date: 2019-01-20 00:00:00
Tasks: Management, model

A linear multi-factor model is one of the most important tools in equity
portfolio management. The linear multi-factor models are widely used because
they can be easily interpreted. However, financial markets are not linear and
their accuracy is limited. Recently, deep learning methods were proposed to
predict stock return in terms of the multi-factor model. Although these methods
perform quite well, they have significant disadvantages such as a lack of
transparency and limitations in the interpretability of the prediction. It is
thus difficult for institutional investors to use black-box-type machine
learning techniques in actual investment practice because they should show
accountability to their customers. Consequently, the solution we propose is
based on LSTM with LRP. Specifically, we extend the linear multi-factor model
to be non-linear and time-varying with LSTM. Then, we approximate and linearize
the learned LSTM models by LRP. We call this LSTM+LRP model a deep recurrent
factor model. Finally, we perform an empirical analysis of the Japanese stock
market and show that our recurrent model has better predictive capability than
the traditional linear model and fully-connected deep learning methods.
Inducing Sparse Coding and And-Or Grammar from Generator Network
Authors: Xianglei Xing, Song-Chun Zhu, Ying Nian Wu
Date: 2019-01-20 00:00:00

We introduce an explainable generative model by applying sparse operation on
the feature maps of the generator network. Meaningful hierarchical
representations are obtained using the proposed generative model with sparse
activations. The convolutional kernels from the bottom layer to the top layer
of the generator network can learn primitives such as edges and colors, object
parts, and whole objects layer by layer. From the perspective of the generator
network, we propose a method for inducing both sparse coding and the AND-OR
grammar for images. Experiments show that our method is capable of learning
meaningful and explainable hierarchical representations.
TUNet: Incorporating segmentation maps to improve classification
Authors: Yijun Tian
Date: 2019-01-27 00:00:00
Tasks: Classification, General Classification

Determining the localization of specific protein in human cells is important
for understanding cellular functions and biological processes of underlying
diseases. Among imaging techniques, high-throughput fluorescence microscopy
imaging is an efficient biotechnology to stain the protein of interest in a
cell. In this work, we present a novel classification model Twin U-Net (TUNet)
for processing and classifying the belonging of protein in the Atlas images.
Several notable Deep Learning models including GoogleNet and Resnet have been
employed for comparison. Results have shown that our system obtaining
competitive performance.
MONet: Unsupervised Scene Decomposition and Representation
Authors: Christopher P. Burgess, Loic Matthey, Nicholas Watters, Rishabh Kabra, Irina Higgins, Matt Botvinick, Alexander Lerchner
Date: 2019-01-22 00:00:00
Tasks: Object Discovery, Unsupervised Object Segmentation

The ability to decompose scenes in terms of abstract building blocks is
crucial for general intelligence. Where those basic building blocks share
meaningful properties, interactions and other regularities across scenes, such
decompositions can simplify reasoning and facilitate imagination of novel
scenarios. In particular, representing perceptual observations in terms of
entities should improve data efficiency and transfer performance on a wide
range of tasks. Thus we need models capable of discovering useful
decompositions of scenes by identifying units with such regularities and
representing them in a common format. To address this problem, we have
developed the Multi-Object Network (MONet). In this model, a VAE is trained
end-to-end together with a recurrent attention network -- in a purely
unsupervised manner -- to provide attention masks around, and reconstructions
of, regions of images. We show that this model is capable of learning to
decompose and represent challenging 3D scenes into semantically meaningful
components, such as objects and background elements.
Equivariant Transformer Networks
Authors: Kai Sheng Tai, Peter Bailis, Gregory Valiant
Date: 2019-01-25 00:00:00
Tasks: General Classification, image-classification, Image Classification

How can prior knowledge on the transformation invariances of a domain be incorporated into the architecture of a neural network? We propose Equivariant Transformers (ETs), a family of differentiable image-to-image mappings that improve the robustness of models towards pre-defined continuous transformation groups. Through the use of specially-derived canonical coordinate systems, ETs incorporate functions that are equivariant by construction with respect to these transformations. We show empirically that ETs can be flexibly composed to improve model robustness towards more complicated transformation groups in several parameters. On a real-world image classification task, ETs improve the sample efficiency of ResNet classifiers, achieving relative improvements in error rate of up to 15% in the limited data regime while increasing model parameter count by less than 1%.
Automatic Information Extraction from Piping and Instrumentation Diagrams
Authors: Rohit Rahul, Shubham Paliwal, Monika Sharma, Lovekesh Vig
Date: 2019-01-28 00:00:00
Tasks: Management

One of the most common modes of representing engineering schematics are
Piping and Instrumentation diagrams (P&IDs) that describe the layout of an
engineering process flow along with the interconnected process equipment. Over
the years, P&ID diagrams have been manually generated, scanned and stored as
image files. These files need to be digitized for purposes of inventory
management and updation, and easy reference to different components of the
schematics. There are several challenging vision problems associated with
digitizing real world P&ID diagrams. Real world P&IDs come in several different
resolutions, and often contain noisy textual information. Extraction of
instrumentation information from these diagrams involves accurate detection of
symbols that frequently have minute visual differences between them.
Identification of pipelines that may converge and diverge at different points
in the image is a further cause for concern. Due to these reasons, to the best
of our knowledge, no system has been proposed for end-to-end data extraction
from P&ID diagrams. However, with the advent of deep learning and the
spectacular successes it has achieved in vision, we hypothesized that it is now
possible to re-examine this problem armed with the latest deep learning models.
To that end, we present a novel pipeline for information extraction from P&ID
sheets via a combination of traditional vision techniques and state-of-the-art
deep learning models to identify and isolate pipeline codes, pipelines, inlets
and outlets, and for detecting symbols. This is followed by association of the
detected components with the appropriate pipeline. The extracted pipeline
information is used to populate a tree-like data-structure for capturing the
structure of the piping schematics. We evaluated proposed method on a real
world dataset of P&ID sheets obtained from an oil firm and have obtained
promising results.
Tree Recognition APP of Mount Tai Based on CNN
Authors: Zhihao Cao, Xinxin Zhang
Date: 2019-01-23 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Mount Tai has abundant sunshine, abundant rainfall and favorable climatic
conditions, forming dense vegetation with various kinds of trees. In order to
make it easier for tourists to understand each tree and experience the culture
of Mount Tai, this paper develops an App for tree recognition of Mount Tai
based on convolution neural network (CNN), taking advantage of CNN efficient
image recognition ability and easy-to-carry characteristics of Android mobile
phone. The APP can accurately identify several common trees in Mount Tai, and
give a brief introduction for tourists.
Fast and Efficient Lenslet Image Compression
Authors: Hadi Amirpour, Antonio Pinheiro, Manuela Pereira, Mohammad Ghanbari
Date: 2019-01-27 00:00:00
Tasks: Depth Estimation, Depth Prediction, Image Compression

Light field imaging is characterized by capturing brightness, color, and
directional information of light rays in a scene. This leads to image
representations with huge amount of data that require efficient coding schemes.
In this paper, lenslet images are rendered into sub-aperture images. These
images are organized as a pseudo-sequence input for the HEVC video codec. To
better exploit redundancy among the neighboring sub-aperture images and
consequently decrease the distances between a sub-aperture image and its
references used for prediction, sub-aperture images are divided into four
smaller groups that are scanned in a serpentine order. The most central
sub-aperture image, which has the highest similarity to all the other images,
is used as the initial reference image for each of the four regions.
Furthermore, a structure is defined that selects spatially adjacent
sub-aperture images as prediction references with the highest similarity to the
current image. In this way, encoding efficiency increases, and furthermore it
leads to a higher similarity among the co-located Coding Three Units (CTUs).
The similarities among the co-located CTUs are exploited to predict Coding Unit
depths.Moreover, independent encoding of each group division enables parallel
processing, that along with the proposed coding unit depth prediction decrease
the encoding execution time by almost 80% on average. Simulation results show
that Rate-Distortion performance of the proposed method has higher compression
gain than the other state-of-the-art lenslet compression methods with lower
computational complexity.
Visual Categorization of Objects into Animal and Plant Classes Using Global Shape Descriptors
Authors: Zahra Sadeghi
Date: 2019-01-25 00:00:00

How humans can distinguish between general categories of objects? Are the
subcategories of living things visually distinctive? In a number of
semantic-category deficits, patients are good at making broad categorization
but are unable to remember fine and specific details. It has been well accepted
that general information about concepts are more robust to damages related to
semantic memory. Results from patients with semantic memory disorders
demonstrate the loss of ability in subcategory recognition. While bottom-up
feature construction has been studied in detail, little attention has been
served to top-down approach and the type of features that could account for
general categorization. In this paper, we show that broad categories of animal
and plant are visually distinguishable without processing textural information.
To this aim, we utilize shape descriptors with an additional phase of feature
learning. The results are evaluated with both supervised and unsupervised
learning mechanisms. The obtained results demonstrate that global encoding of
visual appearance of objects accounts for high discrimination between animal
and plant object categories.
Text line Segmentation in Compressed Representation of Handwritten Document using Tunneling Algorithm
Authors: Amarnath R, P. Nagabhushan
Date: 2019-01-03 00:00:00

In this research work, we perform text line segmentation directly in
compressed representation of an unconstrained handwritten document image. In
this relation, we make use of text line terminal points which is the current
state-of-the-art. The terminal points spotted along both margins (left and
right) of a document image for every text line are considered as source and
target respectively. The tunneling algorithm uses a single agent (or robot) to
identify the coordinate positions in the compressed representation to perform
text-line segmentation of the document. The agent starts at a source point and
progressively tunnels a path routing in between two adjacent text lines and
reaches the probable target. The agent's navigation path from source to the
target bypassing obstacles, if any, results in segregating the two adjacent
text lines. However, the target point would be known only when the agent
reaches the destination; this is applicable for all source points and
henceforth we could analyze the correspondence between source and target nodes.
Artificial Intelligence in Expert systems, dynamic programming and greedy
strategies are employed for every search space while tunneling. An exhaustive
experimentation is carried out on various benchmark datasets including ICDAR13
and the performances are reported.
A2BCD: Asynchronous Acceleration with Optimal Complexity
Authors: Robert Hannah, Fei Feng, Wotao Yin
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

	In this paper, we propose the Asynchronous Accelerated Nonuniform Randomized Block Coordinate Descent algorithm (A2BCD). We prove A2BCD converges linearly to a solution of the convex minimization problem at the same rate as NU_ACDM, so long as the maximum delay is not too large. This is the first asynchronous Nesterov-accelerated algorithm that attains any provable speedup. Moreover, we then prove that these algorithms both have optimal complexity. Asynchronous algorithms complete much faster iterations, and A2BCD has optimal complexity. Hence we observe in experiments that A2BCD is the top-performing coordinate descent algorithm, converging up to 4-5x faster than NU_ACDM on some data sets in terms of wall-clock time. To motivate our theory and proof techniques, we also derive and analyze a continuous-time analog of our algorithm and prove it converges at the same rate.
Learning Bayesian Networks with the bnlearn R Package
Authors: Marco Scutari
Date: 2009-08-26 00:00:00

bnlearn is an R package which includes several algorithms for learning the
structure of Bayesian networks with either discrete or continuous variables.
Both constraint-based and score-based algorithms are implemented, and can use
the functionality provided by the snow package to improve their performance via
parallel computing. Several network scores and conditional independence
algorithms are available for both the learning algorithms and independent use.
Advanced plotting options are provided by the Rgraphviz package.
How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions
Authors: Goran Glavas, Robert Litschko, Sebastian Ruder, Ivan Vulic
Date: 2019-02-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Bilingual Lexicon Induction, Cross-Lingual Transfer, Cross-Lingual Word Embeddings, Misconceptions, Word Embeddings

Cross-lingual word embeddings (CLEs) enable multilingual modeling of meaning
and facilitate cross-lingual transfer of NLP models. Despite their ubiquitous
usage in downstream tasks, recent increasingly popular projection-based CLE
models are almost exclusively evaluated on a single task only: bilingual
lexicon induction (BLI). Even BLI evaluations vary greatly, hindering our
ability to correctly interpret performance and properties of different CLE
models. In this work, we make the first step towards a comprehensive evaluation
of cross-lingual word embeddings. We thoroughly evaluate both supervised and
unsupervised CLE models on a large number of language pairs in the BLI task and
three downstream tasks, providing new insights concerning the ability of
cutting-edge CLE models to support cross-lingual NLP. We empirically
demonstrate that the performance of CLE models largely depends on the task at
hand and that optimizing CLE models for BLI can result in deteriorated
downstream performance. We indicate the most robust supervised and unsupervised
CLE models and emphasize the need to reassess existing baselines, which still
display competitive performance across the board. We hope that our work will
catalyze further work on CLE evaluation and model analysis.