pwc-archive/papers-with-abstracts huggingface dataset:
End-to-end Binary Representation Learning via Direct Binary Embedding
Authors: Liu Liu, Alireza Rahimpour, Ali Taalimi, Hairong Qi
Date: 2017-03-15 00:00:00
Tasks: Image Retrieval, Object Recognition, Quantization, Representation Learning, Retrieval

Learning binary representation is essential to large-scale computer vision
tasks. Most existing algorithms require a separate quantization constraint to
learn effective hashing functions. In this work, we present Direct Binary
Embedding (DBE), a simple yet very effective algorithm to learn binary
representation in an end-to-end fashion. By appending an ingeniously designed
DBE layer to the deep convolutional neural network (DCNN), DBE learns binary
code directly from the continuous DBE layer activation without quantization
error. By employing the deep residual network (ResNet) as DCNN component, DBE
captures rich semantics from images. Furthermore, in the effort of handling
multilabel images, we design a joint cross entropy loss that includes both
softmax cross entropy and weighted binary cross entropy in consideration of the
correlation and independence of labels, respectively. Extensive experiments
demonstrate the significant superiority of DBE over state-of-the-art methods on
tasks of natural object recognition, image retrieval and image annotation.
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis
Authors: Maxim Raginsky, Alexander Rakhlin, Matus Telgarsky
Date: 2017-02-13 00:00:00

Stochastic Gradient Langevin Dynamics (SGLD) is a popular variant of
Stochastic Gradient Descent, where properly scaled isotropic Gaussian noise is
added to an unbiased estimate of the gradient at each iteration. This modest
change allows SGLD to escape local minima and suffices to guarantee asymptotic
convergence to global minimizers for sufficiently regular non-convex objectives
(Gelfand and Mitter, 1991). The present work provides a nonasymptotic analysis
in the context of non-convex learning problems, giving finite-time guarantees
for SGLD to find approximate minimizers of both empirical and population risks.
As in the asymptotic setting, our analysis relates the discrete-time SGLD
Markov chain to a continuous-time diffusion process. A new tool that drives the
results is the use of weighted transportation cost inequalities to quantify the
rate of convergence of SGLD to a stationary distribution in the Euclidean
$2$-Wasserstein distance.
DeepSF: deep convolutional neural network for mapping protein sequences to folds
Authors: Jie Hou, Badri Adhikari, Jianlin Cheng
Date: 2017-06-04 00:00:00
Tasks: Clustering, General Classification, TAR

Motivation
  Protein fold recognition is an important problem in structural
bioinformatics. Almost all traditional fold recognition methods use sequence
(homology) comparison to indirectly predict the fold of a tar get protein based
on the fold of a template protein with known structure, which cannot explain
the relationship between sequence and fold. Only a few methods had been
developed to classify protein sequences into a small number of folds due to
methodological limitations, which are not generally useful in practice.
  Results
  We develop a deep 1D-convolution neural network (DeepSF) to directly classify
any protein se quence into one of 1195 known folds, which is useful for both
fold recognition and the study of se quence-structure relationship. Different
from traditional sequence alignment (comparison) based methods, our method
automatically extracts fold-related features from a protein sequence of any
length and map it to the fold space. We train and test our method on the
datasets curated from SCOP1.75, yielding a classification accuracy of 80.4%. On
the independent testing dataset curated from SCOP2.06, the classification
accuracy is 77.0%. We compare our method with a top profile profile alignment
method - HHSearch on hard template-based and template-free modeling targets of
CASP9-12 in terms of fold recognition accuracy. The accuracy of our method is
14.5%-29.1% higher than HHSearch on template-free modeling targets and
4.5%-16.7% higher on hard template-based modeling targets for top 1, 5, and 10
predicted folds. The hidden features extracted from sequence by our method is
robust against sequence mutation, insertion, deletion and truncation, and can
be used for other protein pattern recognition problems such as protein
clustering, comparison and ranking.
Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks
Authors: Hao Dong, Guang Yang, Fangde Liu, Yuanhan Mo, Yike Guo
Date: 2017-05-10 00:00:00
Tasks: Brain Tumor Segmentation, Diagnostic, Image Segmentation, Segmentation, Semantic Segmentation, Tumor Segmentation

A major challenge in brain tumor treatment planning and quantitative
evaluation is determination of the tumor extent. The noninvasive magnetic
resonance imaging (MRI) technique has emerged as a front-line diagnostic tool
for brain tumors without ionizing radiation. Manual segmentation of brain tumor
extent from 3D MRI volumes is a very time-consuming task and the performance is
highly relied on operator's experience. In this context, a reliable fully
automatic segmentation method for the brain tumor segmentation is necessary for
an efficient measurement of the tumor extent. In this study, we propose a fully
automatic method for brain tumor segmentation, which is developed using U-Net
based deep convolutional networks. Our method was evaluated on Multimodal Brain
Tumor Image Segmentation (BRATS 2015) datasets, which contain 220 high-grade
brain tumor and 54 low-grade tumor cases. Cross-validation has shown that our
method can obtain promising segmentation efficiently.
I2T2I: Learning Text to Image Synthesis with Textual Data Augmentation
Authors: Hao Dong, Jingqing Zhang, Douglas McIlwraith, Yike Guo
Date: 2017-03-20 00:00:00
Tasks: Caption Generation, Data Augmentation, Image Captioning, Image Generation, Image to text, Sentence, Text to Image Generation, Text-to-Image Generation, Transfer Learning

Translating information between text and image is a fundamental problem in
artificial intelligence that connects natural language processing and computer
vision. In the past few years, performance in image caption generation has seen
significant improvement through the adoption of recurrent neural networks
(RNN). Meanwhile, text-to-image generation begun to generate plausible images
using datasets of specific categories like birds and flowers. We've even seen
image generation from multi-category datasets such as the Microsoft Common
Objects in Context (MSCOCO) through the use of generative adversarial networks
(GANs). Synthesizing objects with a complex shape, however, is still
challenging. For example, animals and humans have many degrees of freedom,
which means that they can take on many complex shapes. We propose a new
training method called Image-Text-Image (I2T2I) which integrates text-to-image
and image-to-text (image captioning) synthesis to improve the performance of
text-to-image synthesis. We demonstrate that %the capability of our method to
understand the sentence descriptions, so as to I2T2I can generate better
multi-categories images using MSCOCO than the state-of-the-art. We also
demonstrate that I2T2I can achieve transfer learning by using a pre-trained
image captioning module to generate human images on the MPII Human Pose
Image Compression Based on Compressive Sensing: End-to-End Comparison with JPEG
Authors: Xin Yuan, Raziel Haimi-Cohen
Date: 2017-06-03 00:00:00
Tasks: Compressive Sensing, Image Compression, Quantization

We present an end-to-end image compression system based on compressive sensing. The presented system integrates the conventional scheme of compressive sampling and reconstruction with quantization and entropy coding. The compression performance, in terms of decoded image quality versus data rate, is shown to be comparable with JPEG and significantly better at the low rate range. We study the parameters that influence the system performance, including (i) the choice of sensing matrix, (ii) the trade-off between quantization and compression ratio, and (iii) the reconstruction algorithms. We propose an effective method to jointly control the quantization step and compression ratio in order to achieve near optimal quality at any given bit rate. Furthermore, our proposed image compression system can be directly used in the compressive sensing camera, e.g. the single pixel camera, to construct a hardware compressive sampling system.
Order embeddings and character-level convolutions for multimodal alignment
Authors: Jônatas Wehrmann, Anderson Mattjie, Rodrigo C. Barros
Date: 2017-06-03 00:00:00
Tasks: Retrieval, Semantic correspondence, Word Embeddings

With the novel and fast advances in the area of deep neural networks, several
challenging image-based tasks have been recently approached by researchers in
pattern recognition and computer vision. In this paper, we address one of these
tasks, which is to match image content with natural language descriptions,
sometimes referred as multimodal content retrieval. Such a task is particularly
challenging considering that we must find a semantic correspondence between
captions and the respective image, a challenge for both computer vision and
natural language processing areas. For such, we propose a novel multimodal
approach based solely on convolutional neural networks for aligning images with
their captions by directly convolving raw characters. Our proposed
character-based textual embeddings allow the replacement of both
word-embeddings and recurrent neural networks for text understanding, saving
processing time and requiring fewer learnable parameters. Our method is based
on the idea of projecting both visual and textual information into a common
embedding space. For training such embeddings we optimize a contrastive loss
function that is computed to minimize order-violations between images and their
respective descriptions. We achieve state-of-the-art performance in the largest
and most well-known image-text alignment dataset, namely Microsoft COCO, with a
method that is conceptually much simpler and that possesses considerably fewer
parameters than current approaches.
Swarm Intelligence in Semi-supervised Classification
Authors: Shahira Shaaban Azab, Hesham Ahmed Hefny
Date: 2017-06-03 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification

This Paper represents a literature review of Swarm intelligence algorithm in
the area of semi-supervised classification. There are many research papers for
applying swarm intelligence algorithms in the area of machine learning. Some
algorithms of SI are applied in the area of ML either solely or hybrid with
other ML algorithms. SI algorithms are also used for tuning parameters of ML
algorithm, or as a backbone for ML algorithms. This paper introduces a brief
literature review for applying swarm intelligence algorithms in the field of
semi-supervised learning
Semi-supervised Classification: Cluster and Label Approach using Particle Swarm Optimization
Authors: Shahira Shaaban Azab, Mohamed Farouk Abdel Hady, Hesham Ahmed Hefny
Date: 2017-06-03 00:00:00
Tasks: Classification, Clustering, General Classification

Classification predicts classes of objects using the knowledge learned during
the training phase. This process requires learning from labeled samples.
However, the labeled samples usually limited. Annotation process is annoying,
tedious, expensive, and requires human experts. Meanwhile, unlabeled data is
available and almost free. Semi-supervised learning approaches make use of both
labeled and unlabeled data. This paper introduces cluster and label approach
using PSO for semi-supervised classification. PSO is competitive to traditional
clustering algorithms. A new local best PSO is presented to cluster the
unlabeled data. The available labeled data guides the learning process. The
experiments are conducted using four state-of-the-art datasets from different
domains. The results compared with Label Propagation a popular semi-supervised
classifier and two state-of-the-art supervised classification models, namely
k-nearest neighbors and decision trees. The experiments show the efficiency of
the proposed model.
Classification of Major Depressive Disorder via Multi-Site Weighted LASSO Model
Authors: Dajiang Zhu, Brandalyn C. Riedel, Neda Jahanshad, Nynke A. Groenewold, Dan J. Stein, Ian H. Gotlib, Matthew D. Sacchet, Danai Dima, James H. Cole, Cynthia H. Y. Fu, Henrik Walter, Ilya M. Veer, Thomas Frodl, Lianne Schmaal, Dick J. Veltman, Paul M. Thompson
Date: 2017-05-26 00:00:00
Tasks: Classification, feature selection, General Classification

Large-scale collaborative analysis of brain imaging data, in psychiatry and
neu-rology, offers a new source of statistical power to discover features that
boost ac-curacy in disease classification, differential diagnosis, and outcome
prediction. However, due to data privacy regulations or limited accessibility
to large datasets across the world, it is challenging to efficiently integrate
distributed information. Here we propose a novel classification framework
through multi-site weighted LASSO: each site performs an iterative weighted
LASSO for feature selection separately. Within each iteration, the
classification result and the selected features are collected to update the
weighting parameters for each feature. This new weight is used to guide the
LASSO process at the next iteration. Only the fea-tures that help to improve
the classification accuracy are preserved. In tests on da-ta from five sites
(299 patients with major depressive disorder (MDD) and 258 normal controls),
our method boosted classification accuracy for MDD by 4.9% on average. This
result shows the potential of the proposed new strategy as an ef-fective and
practical collaborative platform for machine learning on large scale
distributed imaging and biobank data.
Visuospatial Skill Learning for Robots
Authors: S. Reza Ahmadzadeh, Fulvio Mastrogiovanni, Petar Kormushev
Date: 2017-06-03 00:00:00
Tasks: Imitation Learning

A novel skill learning approach is proposed that allows a robot to acquire
human-like visuospatial skills for object manipulation tasks. Visuospatial
skills are attained by observing spatial relationships among objects through
demonstrations. The proposed Visuospatial Skill Learning (VSL) is a goal-based
approach that focuses on achieving a desired goal configuration of objects
relative to one another while maintaining the sequence of operations. VSL is
capable of learning and generalizing multi-operation skills from a single
demonstration, while requiring minimum prior knowledge about the objects and
the environment. In contrast to many existing approaches, VSL offers
simplicity, efficiency and user-friendly human-robot interaction. We also show
that VSL can be easily extended towards 3D object manipulation tasks, simply by
employing point cloud processing techniques. In addition, a robot learning
framework, VSL-SP, is proposed by integrating VSL, Imitation Learning, and a
conventional planning method. In VSL-SP, the sequence of performed actions are
learned using VSL, while the sensorimotor skills are learned using a
conventional trajectory-based learning approach. such integration easily
extends robot capabilities to novel situations, even by users without
programming ability. In VSL-SP the internal planner of VSL is integrated with
an existing action-level symbolic planner. Using the underlying constraints of
the task and extracted symbolic predicates, identified by VSL, symbolic
representation of the task is updated. Therefore the planner maintains a
generalized representation of each skill as a reusable action, which can be
used in planning and performed independently during the learning phase. The
proposed approach is validated through several real-world experiments.
A novel image tag completion method based on convolutional neural network
Authors: Yanyan Geng, Guohui Zhang, Weizhi Li, Yi Gu, Ru-Ze Liang, Gaoyuan Liang, Jingbin Wang, Yanbin Wu, Nitin Patil, Jing-Yan Wang
Date: 2017-03-02 00:00:00
Tasks: Image Retrieval, Retrieval, TAG

In the problems of image retrieval and annotation, complete textual tag lists
of images play critical roles. However, in real-world applications, the image
tags are usually incomplete, thus it is important to learn the complete tags
for images. In this paper, we study the problem of image tag complete and
proposed a novel method for this problem based on a popular image
representation method, convolutional neural network (CNN). The method estimates
the complete tags from the convolutional filtering outputs of images based on a
linear predictor. The CNN parameters, linear predictor, and the complete tags
are learned jointly by our method. We build a minimization problem to encourage
the consistency between the complete tags and the available incomplete tags,
reduce the estimation error, and reduce the model complexity. An iterative
algorithm is developed to solve the minimization problem. Experiments over
benchmark image data sets show its effectiveness.
Learning from networked examples
Authors: Yuyi Wang, Jan Ramon, Zheng-Chu Guo
Date: 2014-05-11 00:00:00

Many machine learning algorithms are based on the assumption that training
examples are drawn independently. However, this assumption does not hold
anymore when learning from a networked sample because two or more training
examples may share some common objects, and hence share the features of these
shared objects. We show that the classic approach of ignoring this problem
potentially can have a harmful effect on the accuracy of statistics, and then
consider alternatives. One of these is to only use independent examples,
discarding other information. However, this is clearly suboptimal. We analyze
sample error bounds in this networked setting, providing significantly improved
results. An important component of our approach is formed by efficient sample
weighting schemes, which leads to novel concentration inequalities.
On the relation between dependency distance, crossing dependencies, and parsing. Comment on "Dependency distance: a new perspective on syntactic patterns in natural languages" by Haitao Liu et al
Authors: Carlos Gómez-Rodríguez
Date: 2017-05-27 00:00:00

Liu et al. (2017) provide a comprehensive account of research on dependency
distance in human languages. While the article is a very rich and useful report
on this complex subject, here I will expand on a few specific issues where
research in computational linguistics (specifically natural language
processing) can inform DDM research, and vice versa. These aspects have not
been explored much in the article by Liu et al. or elsewhere, probably due to
the little overlap between both research communities, but they may provide
interesting insights for improving our understanding of the evolution of human
languages, the mechanisms by which the brain processes and understands
language, and the construction of effective computer systems to achieve this
goal.
See, Hear, and Read: Deep Aligned Representations
Authors: Yusuf Aytar, Carl Vondrick, Antonio Torralba
Date: 2017-06-03 00:00:00
Tasks: Cross-Modal Retrieval, Representation Learning, Retrieval

We capitalize on large amounts of readily-available, synchronous data to
learn a deep discriminative representations shared across three major natural
modalities: vision, sound and language. By leveraging over a year of sound from
video and millions of sentences paired with images, we jointly train a deep
convolutional network for aligned representation learning. Our experiments
suggest that this representation is useful for several tasks, such as
cross-modal retrieval or transferring classifiers between modalities. Moreover,
although our network is only trained with image+text and image+sound pairs, it
can transfer between text and sound as well, a transfer the network never
observed during training. Visualizations of our representation reveal many
hidden units which automatically emerge to detect concepts, independent of the
modality.
Concurrence-Aware Long Short-Term Sub-Memories for Person-Person Action Recognition
Authors: Xiangbo Shu, Jinhui Tang, Guo-Jun Qi, Yan Song, Zechao Li, Liyan Zhang
Date: 2017-06-03 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Recently, Long Short-Term Memory (LSTM) has become a popular choice to model
individual dynamics for single-person action recognition due to its ability of
modeling the temporal information in various ranges of dynamic contexts.
However, existing RNN models only focus on capturing the temporal dynamics of
the person-person interactions by naively combining the activity dynamics of
individuals or modeling them as a whole. This neglects the inter-related
dynamics of how person-person interactions change over time. To this end, we
propose a novel Concurrence-Aware Long Short-Term Sub-Memories (Co-LSTSM) to
model the long-term inter-related dynamics between two interacting people on
the bounding boxes covering people. Specifically, for each frame, two
sub-memory units store individual motion information, while a concurrent LSTM
unit selectively integrates and stores inter-related motion information between
interacting people from these two sub-memory units via a new co-memory cell.
Experimental results on the BIT and UT datasets show the superiority of
Co-LSTSM compared with the state-of-the-art methods.
Deep-Learning Convolutional Neural Networks for scattered shrub detection with Google Earth Imagery
Authors: Emilio Guirado, Siham Tabik, Domingo Alcaraz-Segura, Javier Cabello, Francisco Herrera
Date: 2017-06-03 00:00:00
Tasks: Data Augmentation, Object Recognition, Transfer Learning

There is a growing demand for accurate high-resolution land cover maps in
many fields, e.g., in land-use planning and biodiversity conservation.
Developing such maps has been performed using Object-Based Image Analysis
(OBIA) methods, which usually reach good accuracies, but require a high human
supervision and the best configuration for one image can hardly be extrapolated
to a different image. Recently, the deep learning Convolutional Neural Networks
(CNNs) have shown outstanding results in object recognition in the field of
computer vision. However, they have not been fully explored yet in land cover
mapping for detecting species of high biodiversity conservation interest. This
paper analyzes the potential of CNNs-based methods for plant species detection
using free high-resolution Google Earth T M images and provides an objective
comparison with the state-of-the-art OBIA-methods. We consider as case study
the detection of Ziziphus lotus shrubs, which are protected as a priority
habitat under the European Union Habitats Directive. According to our results,
compared to OBIA-based methods, the proposed CNN-based detection model, in
combination with data-augmentation, transfer learning and pre-processing,
achieves higher performance with less human intervention and the knowledge it
acquires in the first image can be transferred to other images, which makes the
detection process very fast. The provided methodology can be systematically
reproduced for other species detection.