pwc-archive/papers-with-abstracts huggingface dataset:
DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders
Authors: Ido Cohen, Eli David, Nathan S. Netanyahu, Noa Liscovitch, Gal Chechik
Date: 2017-11-27 00:00:00
Tasks: Denoising, General Classification

This paper presents a novel deep learning-based method for learning a
functional representation of mammalian neural images. The method uses a deep
convolutional denoising autoencoder (CDAE) for generating an invariant, compact
representation of in situ hybridization (ISH) images. While most existing
methods for bio-imaging analysis were not developed to handle images with
highly complex anatomical structures, the results presented in this paper show
that functional representation extracted by CDAE can help learn features of
functional gene ontology categories for their classification in a highly
accurate manner. Using this CDAE representation, our method outperforms the
previous state-of-the-art classification rate, by improving the average AUC
from 0.92 to 0.98, i.e., achieving 75% reduction in error. The method operates
on input images that were downsampled significantly with respect to the
original ones to make it computationally feasible.
Transfer Learning in CNNs Using Filter-Trees
Authors: Suresh Kirthi Kumaraswamy, PS Sastry, KR Ramakrishnan
Date: 2017-11-27 00:00:00
Tasks: Transfer Learning

Convolutional Neural Networks (CNNs) are very effective for many pattern
recognition tasks. However, training deep CNNs needs extensive computation and
large training data. In this paper we propose Bank of Filter-Trees (BFT) as a
trans- fer learning mechanism for improving efficiency of learning CNNs. A
filter-tree corresponding to a filter in k^{th} convolu- tional layer of a CNN
is a subnetwork consisting of the filter along with all its connections to
filters in all preceding layers. An ensemble of such filter-trees created from
the k^{th} layers of many CNNs learnt on different but related tasks, forms the
BFT. To learn a new CNN, we sample from the BFT to select a set of filter
trees. This fixes the target net up to the k th layer and only the remaining
network would be learnt using train- ing data of new task. Through simulations
we demonstrate the effectiveness of this idea of BFT. This method constitutes a
novel transfer learning technique where transfer is at a sub- network level;
transfer can be effected from multiple source networks; and, with no finetuning
of the transferred weights, the performance achieved is on par with networks
that are trained from scratch.
Code Attention: Translating Code to Comments by Exploiting Domain Features
Authors: Wenhao Zheng, Hong-Yu Zhou, Ming Li, Jianxin Wu
Date: 2017-09-22 00:00:00
Tasks: Comment Generation

Appropriate comments of code snippets provide insight for code functionality,
which are helpful for program comprehension. However, due to the great cost of
authoring with the comments, many code projects do not contain adequate
comments. Automatic comment generation techniques have been proposed to
generate comments from pieces of code in order to alleviate the human efforts
in annotating the code. Most existing approaches attempt to exploit certain
correlations (usually manually given) between code and generated comments,
which could be easily violated if the coding patterns change and hence the
performance of comment generation declines. In this paper, we first build
C2CGit, a large dataset from open projects in GitHub, which is more than
20$\times$ larger than existing datasets. Then we propose a new attention
module called Code Attention to translate code to comments, which is able to
utilize the domain features of code snippets, such as symbols and identifiers.
We make ablation studies to determine effects of different parts in Code
Attention. Experimental results demonstrate that the proposed module has better
performance over existing approaches in both BLEU and METEOR.
Deep Reinforcement Learning for Sepsis Treatment
Authors: Aniruddh Raghu, Matthieu Komorowski, Imran Ahmed, Leo Celi, Peter Szolovits, Marzyeh Ghassemi
Date: 2017-11-27 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), State Space Models

Sepsis is a leading cause of mortality in intensive care units and costs
hospitals billions annually. Treating a septic patient is highly challenging,
because individual patients respond very differently to medical interventions
and there is no universally agreed-upon treatment for sepsis. In this work, we
propose an approach to deduce treatment policies for septic patients by using
continuous state-space models and deep reinforcement learning. Our model learns
clinically interpretable treatment policies, similar in important aspects to
the treatment policies of physicians. The learned policies could be used to aid
intensive care clinicians in medical decision making and improve the likelihood
of patient survival.
Visual Feature Attribution using Wasserstein GANs
Authors: Christian F. Baumgartner, Lisa M. Koch, Kerem Can Tezcan, Jia Xi Ang, Ender Konukoglu
Date: 2017-11-24 00:00:00
Proceeding: CVPR 2018 6

Attributing the pixels of an input image to a certain category is an
important and well-studied problem in computer vision, with applications
ranging from weakly supervised localisation to understanding hidden effects in
the data. In recent years, approaches based on interpreting a previously
trained neural network classifier have become the de facto state-of-the-art and
are commonly used on medical as well as natural image datasets. In this paper,
we discuss a limitation of these approaches which may lead to only a subset of
the category specific features being detected. To address this problem we
develop a novel feature attribution technique based on Wasserstein Generative
Adversarial Networks (WGAN), which does not suffer from this limitation. We
show that our proposed method performs substantially better than the
state-of-the-art for visual attribution on a synthetic dataset and on real 3D
neuroimaging data from patients with mild cognitive impairment (MCI) and
Alzheimer's disease (AD). For AD patients the method produces compellingly
realistic disease effect maps which are very close to the observed effects.
A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series
Authors: Stanislas Chambon, Mathieu Galtier, Pierrick Arnal, Gilles Wainrib, Alexandre Gramfort
Date: 2017-07-05 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), General Classification, Time Series, Time Series Analysis

Sleep stage classification constitutes an important preliminary exam in the
diagnosis of sleep disorders. It is traditionally performed by a sleep expert
who assigns to each 30s of signal a sleep stage, based on the visual inspection
of signals such as electroencephalograms (EEG), electrooculograms (EOG),
electrocardiograms (ECG) and electromyograms (EMG). We introduce here the first
deep learning approach for sleep stage classification that learns end-to-end
without computing spectrograms or extracting hand-crafted features, that
exploits all multivariate and multimodal Polysomnography (PSG) signals (EEG,
EMG and EOG), and that can exploit the temporal context of each 30s window of
data. For each modality the first layer learns linear spatial filters that
exploit the array of sensors to increase the signal-to-noise ratio, and the
last layer feeds the learnt representation to a softmax classifier. Our model
is compared to alternative automatic approaches based on convolutional networks
or decisions trees. Results obtained on 61 publicly available PSG records with
up to 20 EEG channels demonstrate that our network architecture yields
state-of-the-art performance. Our study reveals a number of insights on the
spatio-temporal distribution of the signal of interest: a good trade-off for
optimal classification performance measured with balanced accuracy is to use 6
EEG with 2 EOG (left and right) and 3 EMG chin channels. Also exploiting one
minute of data before and after each data segment offers the strongest
improvement when a limited number of channels is available. As sleep experts,
our system exploits the multivariate and multimodal nature of PSG signals in
order to deliver state-of-the-art classification performance with a small
computational cost.
FuCoLoT -- A Fully-Correlational Long-Term Tracker
Authors: Alan Lukežič, Luka Čehovin Zajc, Tomáš Vojíř, Jiří Matas, Matej Kristan
Date: 2017-11-27 00:00:00
Tasks: CPU

We propose FuCoLoT -- a Fully Correlational Long-term Tracker. It exploits
the novel DCF constrained filter learning method to design a detector that is
able to re-detect the target in the whole image efficiently. FuCoLoT maintains
several correlation filters trained on different time scales that act as the
detector components. A novel mechanism based on the correlation response is
used for tracking failure estimation. FuCoLoT achieves state-of-the-art results
on standard short-term benchmarks and it outperforms the current
best-performing tracker on the long-term UAV20L benchmark by over 19%. It has
an order of magnitude smaller memory footprint than its best-performing
competitors and runs at 15fps in a single CPU thread.
Optimization on Product Submanifolds of Convolution Kernels
Authors: Mete Ozay, Takayuki Okatani
Date: 2017-01-22 00:00:00

Recent advances in optimization methods used for training convolutional
neural networks (CNNs) with kernels, which are normalized according to
particular constraints, have shown remarkable success. This work introduces an
approach for training CNNs using ensembles of joint spaces of kernels
constructed using different constraints. For this purpose, we address a problem
of optimization on ensembles of products of submanifolds (PEMs) of convolution
kernels. To this end, we first propose three strategies to construct ensembles
of PEMs in CNNs. Next, we expound their geometric properties (metric and
curvature properties) in CNNs. We make use of our theoretical results by
developing a geometry-aware SGD algorithm (G-SGD) for optimization on ensembles
of PEMs to train CNNs. Moreover, we analyze convergence properties of G-SGD
considering geometric properties of PEMs. In the experimental analyses, we
employ G-SGD to train CNNs on Cifar-10, Cifar-100 and Imagenet datasets. The
results show that geometric adaptive step size computation methods of G-SGD can
improve training loss and convergence properties of CNNs. Moreover, we observe
that classification performance of baseline CNNs can be boosted using G-SGD on
ensembles of PEMs identified by multiple constraints.
Joint Cuts and Matching of Partitions in One Graph
Authors: Tianshu Yu, Junchi Yan, Jieyi Zhao, Baoxin Li
Date: 2017-11-27 00:00:00
Proceeding: CVPR 2018 6
Tasks: Graph Matching

As two fundamental problems, graph cuts and graph matching have been
investigated over decades, resulting in vast literature in these two topics
respectively. However the way of jointly applying and solving graph cuts and
matching receives few attention. In this paper, we first formalize the problem
of simultaneously cutting a graph into two partitions i.e. graph cuts and
establishing their correspondence i.e. graph matching. Then we develop an
optimization algorithm by updating matching and cutting alternatively, provided
with theoretical analysis. The efficacy of our algorithm is verified on both
synthetic dataset and real-world images containing similar regions or
structures.
Design and Processing of Invertible Orientation Scores of 3D Images for Enhancement of Complex Vasculature
Authors: M. H. J. Janssen, A. J. E. M. Janssen, E. J. Bekkers, J. Olivan Bescos, R. Duits
Date: 2017-07-07 00:00:00

The enhancement and detection of elongated structures in noisy image data is
relevant for many biomedical imaging applications. To handle complex crossing
structures in 2D images, 2D orientation scores $U: \mathbb{R} ^ 2\times S ^ 1
\rightarrow \mathbb{C}$ were introduced, which already showed their use in a
variety of applications. Here we extend this work to 3D orientation scores $U:
\mathbb{R} ^ 3 \times S ^ 2\rightarrow \mathbb{C}$. First, we construct the
orientation score from a given dataset, which is achieved by an invertible
coherent state type of transform. For this transformation we introduce 3D
versions of the 2D cake-wavelets, which are complex wavelets that can
simultaneously detect oriented structures and oriented edges. Here we introduce
two types of cake-wavelets, the first uses a discrete Fourier transform, the
second is designed in the 3D generalized Zernike basis, allowing us to
calculate analytical expressions for the spatial filters. Finally, we show two
applications of the orientation score transformation. In the first application
we propose an extension of crossing-preserving coherence enhancing diffusion
via our invertible orientation scores of 3D images which we apply to real
medical image data. In the second one we develop a new tubularity measure using
3D orientation scores and apply the tubularity measure to both artificial and
real medical data.
Style Transfer in Text: Exploration and Evaluation
Authors: Zhenxin Fu, Xiaoye Tan, Nanyun Peng, Dongyan Zhao, Rui Yan
Date: 2017-11-18 00:00:00
Tasks: Style Transfer, Text Style Transfer

Style transfer is an important problem in natural language processing (NLP).
However, the progress in language style transfer is lagged behind other
domains, such as computer vision, mainly because of the lack of parallel data
and principle evaluation metrics. In this paper, we propose to learn style
transfer with non-parallel data. We explore two models to achieve this goal,
and the key idea behind the proposed models is to learn separate content
representations and style representations using adversarial networks. We also
propose novel evaluation metrics which measure two aspects of style transfer:
transfer strength and content preservation. We access our models and the
evaluation metrics on two tasks: paper-news title transfer, and
positive-negative review transfer. Results show that the proposed content
preservation metric is highly correlate to human judgments, and the proposed
models are able to generate sentences with higher style transfer strength and
similar content preservation score comparing to auto-encoder.
Accelerated kernel discriminant analysis
Authors: Nikolaos Gkalelis, Vasileios Mezaris
Date: 2015-04-27 00:00:00
Tasks: General Classification

In this paper, using a novel matrix factorization and simultaneous reduction
to diagonal form approach (or in short simultaneous reduction approach),
Accelerated Kernel Discriminant Analysis (AKDA) and Accelerated Kernel Subclass
Discriminant Analysis (AKSDA) are proposed. Specifically, instead of performing
the simultaneous reduction of the between- and within-class or subclass scatter
matrices, the nonzero eigenpairs (NZEP) of the so-called core matrix, which is
of relatively small dimensionality, and the Cholesky factorization of the
kernel matrix are computed, achieving more than one order of magnitude speed up
over kernel discriminant analysis (KDA). Moreover, consisting of a few
elementary matrix operations and very stable numerical algorithms, AKDA and
AKSDA offer improved classification accuracy. The experimental evaluation on
various datasets confirms that the proposed approaches provide state-of-the-art
performance in terms of both training time and classification accuracy.
HP-GAN: Probabilistic 3D human motion prediction via GAN
Authors: Emad Barsoum, John Kender, Zicheng Liu
Date: 2017-11-27 00:00:00
Tasks: Autonomous Vehicles, Human motion prediction, Human Pose Forecasting, Motion Estimation, motion prediction, Motion Synthesis, Prediction, Synthetic Data Generation

Predicting and understanding human motion dynamics has many applications,
such as motion synthesis, augmented reality, security, and autonomous vehicles.
Due to the recent success of generative adversarial networks (GAN), there has
been much interest in probabilistic estimation and synthetic data generation
using deep neural network architectures and learning algorithms.
  We propose a novel sequence-to-sequence model for probabilistic human motion
prediction, trained with a modified version of improved Wasserstein generative
adversarial networks (WGAN-GP), in which we use a custom loss function designed
for human motion prediction. Our model, which we call HP-GAN, learns a
probability density function of future human poses conditioned on previous
poses. It predicts multiple sequences of possible future human poses, each from
the same input sequence but a different vector z drawn from a random
distribution. Furthermore, to quantify the quality of the non-deterministic
predictions, we simultaneously train a motion-quality-assessment model that
learns the probability that a given skeleton sequence is a real human motion.
  We test our algorithm on two of the largest skeleton datasets: NTURGB-D and
Human3.6M. We train our model on both single and multiple action types. Its
predictive power for long-term motion estimation is demonstrated by generating
multiple plausible futures of more than 30 frames from just 10 frames of input.
We show that most sequences generated from the same input have more than 50\%
probabilities of being judged as a real human sequence. We will release all the
code used in this paper to Github.
Selective Deep Convolutional Features for Image Retrieval
Authors: Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan, Ngai-Man Cheung
Date: 2017-07-04 00:00:00
Tasks: Image Retrieval, Retrieval

Convolutional Neural Network (CNN) is a very powerful approach to extract
discriminative local descriptors for effective image search. Recent work adopts
fine-tuned strategies to further improve the discriminative power of the
descriptors. Taking a different approach, in this paper, we propose a novel
framework to achieve competitive retrieval performance. Firstly, we propose
various masking schemes, namely SIFT-mask, SUM-mask, and MAX-mask, to select a
representative subset of local convolutional features and remove a large number
of redundant features. We demonstrate that this can effectively address the
burstiness issue and improve retrieval accuracy. Secondly, we propose to employ
recent embedding and aggregating methods to further enhance feature
discriminability. Extensive experiments demonstrate that our proposed framework
achieves state-of-the-art retrieval accuracy.
ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation
Authors: Chang Zhou, Jinze Bai, Junshuai Song, Xiaofei Liu, Zhengchao Zhao, Xiusi Chen, Jun Gao
Date: 2017-11-17 00:00:00

A user can be represented as what he/she does along the history. A common way
to deal with the user modeling problem is to manually extract all kinds of
aggregated features over the heterogeneous behaviors, which may fail to fully
represent the data itself due to limited human instinct. Recent works usually
use RNN-based methods to give an overall embedding of a behavior sequence,
which then could be exploited by the downstream applications. However, this can
only preserve very limited information, or aggregated memories of a person.
When a downstream application requires to facilitate the modeled user features,
it may lose the integrity of the specific highly correlated behavior of the
user, and introduce noises derived from unrelated behaviors. This paper
proposes an attention based user behavior modeling framework called ATRank,
which we mainly use for recommendation tasks. Heterogeneous user behaviors are
considered in our model that we project all types of behaviors into multiple
latent semantic spaces, where influence can be made among the behaviors via
self-attention. Downstream applications then can use the user behavior vectors
via vanilla attention. Experiments show that ATRank can achieve better
performance and faster training process. We further explore ATRank to use one
unified model to predict different types of user behaviors at the same time,
showing a comparable performance with the highly optimized individual models.
Interpretable Convolutional Neural Networks for Effective Translation Initiation Site Prediction
Authors: Jasper Zuallaert, Mijung Kim, Yvan Saeys, Wesley De Neve
Date: 2017-11-27 00:00:00
Tasks: Decision Making, Translation

Thanks to rapidly evolving sequencing techniques, the amount of genomic data
at our disposal is growing increasingly large. Determining the gene structure
is a fundamental requirement to effectively interpret gene function and
regulation. An important part in that determination process is the
identification of translation initiation sites. In this paper, we propose a
novel approach for automatic prediction of translation initiation sites,
leveraging convolutional neural networks that allow for automatic feature
extraction. Our experimental results demonstrate that we are able to improve
the state-of-the-art approaches with a decrease of 75.2% in false positive rate
and with a decrease of 24.5% in error rate on chosen datasets. Furthermore, an
in-depth analysis of the decision-making process used by our predictive model
shows that our neural network implicitly learns biologically relevant features
from scratch, without any prior knowledge about the problem at hand, such as
the Kozak consensus sequence, the influence of stop and start codons in the
sequence and the presence of donor splice site patterns. In summary, our
findings yield a better understanding of the internal reasoning of a
convolutional neural network when applying such a neural network to genomic
data.
Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification
Authors: Xiang Long, Chuang Gan, Gerard de Melo, Jiajun Wu, Xiao Liu, Shilei Wen
Date: 2017-11-27 00:00:00
Proceeding: CVPR 2018 6
Tasks: Classification, General Classification, Video Classification

Recently, substantial research effort has focused on how to apply CNNs or
RNNs to better extract temporal patterns from videos, so as to improve the
accuracy of video classification. In this paper, however, we show that temporal
information, especially longer-term patterns, may not be necessary to achieve
competitive results on common video classification datasets. We investigate the
potential of a purely attention based local feature integration. Accounting for
the characteristics of such features in video classification, we propose a
local feature integration framework based on attention clusters, and introduce
a shifting operation to capture more diverse signals. We carefully analyze and
compare the effect of different attention mechanisms, cluster sizes, and the
use of the shifting operation, and also investigate the combination of
attention clusters for multimodal integration. We demonstrate the effectiveness
of our framework on three real-world video classification datasets. Our model
achieves competitive results across all of these. In particular, on the
large-scale Kinetics dataset, our framework obtains an excellent single model
accuracy of 79.4% in terms of the top-1 and 94.0% in terms of the top-5
accuracy on the validation set. The attention clusters are the backbone of our
winner solution at ActivityNet Kinetics Challenge 2017. Code and models will be
released soon.