pwc-archive/papers-with-abstracts huggingface dataset:
Topics and Label Propagation: Best of Both Worlds for Weakly Supervised Text Classification
Authors: Sachin Pawar, Nitin Ramrakhiyani, Swapnil Hingmire, Girish K. Palshikar
Date: 2017-12-04 00:00:00
Tasks: General Classification, text-classification, Text Classification

We propose a Label Propagation based algorithm for weakly supervised text
classification. We construct a graph where each document is represented by a
node and edge weights represent similarities among the documents. Additionally,
we discover underlying topics using Latent Dirichlet Allocation (LDA) and
enrich the document graph by including the topics in the form of additional
nodes. The edge weights between a topic and a text document represent level of
"affinity" between them. Our approach does not require document level
labelling, instead it expects manual labels only for topic nodes. This
significantly minimizes the level of supervision needed as only a few topics
are observed to be enough for achieving sufficiently high accuracy. The Label
Propagation Algorithm is employed on this enriched graph to propagate labels
among the nodes. Our approach combines the advantages of Label Propagation
(through document-document similarities) and Topic Modelling (for minimal but
smart supervision). We demonstrate the effectiveness of our approach on various
datasets and compare with state-of-the-art weakly supervised text
classification approaches.
Deep Learning Diffuse Optical Tomography
Authors: Jaejun Yoo, Sohail Sabir, Duchang Heo, Kee Hyun Kim, Abdul Wahab, Yoonseok Choi, Seul-I Lee, Eun Young Chae, Hak Hee Kim, Young Min Bae, Young-wook Choi, Seungryong Cho, Jong Chul Ye
Date: 2017-12-04 00:00:00
Tasks: Breast Cancer Detection, Deep Learning

Diffuse optical tomography (DOT) has been investigated as an alternative imaging modality for breast cancer detection thanks to its excellent contrast to hemoglobin oxidization level. However, due to the complicated non-linear photon scattering physics and ill-posedness, the conventional reconstruction algorithms are sensitive to imaging parameters such as boundary conditions. To address this, here we propose a novel deep learning approach that learns non-linear photon scattering physics and obtains an accurate three dimensional (3D) distribution of optical anomalies. In contrast to the traditional black-box deep learning approaches, our deep network is designed to invert the Lippman-Schwinger integral equation using the recent mathematical theory of deep convolutional framelets. As an example of clinical relevance, we applied the method to our prototype DOT system. We show that our deep neural network, trained with only simulation data, can accurately recover the location of anomalies within biomimetic phantoms and live animals without the use of an exogenous contrast agent.
Butterfly Effect: Bidirectional Control of Classification Performance by Small Additive Perturbation
Authors: YoungJoon Yoo, SeongUk Park, Junyoung Choi, Sangdoo Yun, Nojun Kwak
Date: 2017-11-27 00:00:00
Tasks: Classification, General Classification

This paper proposes a new algorithm for controlling classification results by
generating a small additive perturbation without changing the classifier
network. Our work is inspired by existing works generating adversarial
perturbation that worsens classification performance. In contrast to the
existing methods, our work aims to generate perturbations that can enhance
overall classification performance. To solve this performance enhancement
problem, we newly propose a perturbation generation network (PGN) influenced by
the adversarial learning strategy. In our problem, the information in a large
external dataset is summarized by a small additive perturbation, which helps to
improve the performance of the classifier trained with the target dataset. In
addition to this performance enhancement problem, we show that the proposed PGN
can be adopted to solve the classical adversarial problem without utilizing the
information on the target classifier. The mentioned characteristics of our
method are verified through extensive experiments on publicly available visual
datasets.
The Minor Fall, the Major Lift: Inferring Emotional Valence of Musical Chords through Lyrics
Authors: Artemy Kolchinsky, Nakul Dhande, Kengjeun Park, Yong-Yeol Ahn
Date: 2017-06-26 00:00:00

We investigate the association between musical chords and lyrics by analyzing
a large dataset of user-contributed guitar tablatures. Motivated by the idea
that the emotional content of chords is reflected in the words used in
corresponding lyrics, we analyze associations between lyrics and chord
categories. We also examine the usage patterns of chords and lyrics in
different musical genres, historical eras, and geographical regions. Our
overall results confirms a previously known association between Major chords
and positive valence. We also report a wide variation in this association
across regions, genres, and eras. Our results suggest possible existence of
different emotional associations for other types of chords.
Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs
Authors: Cristóbal Esteban, Stephanie L. Hyland, Gunnar Rätsch
Date: 2017-06-08 00:00:00
Proceeding: ICLR 2018 1
Tasks: Time Series, Time Series Analysis, Time Series Generation

Generative Adversarial Networks (GANs) have shown remarkable success as a
framework for training models to produce realistic-looking data. In this work,
we propose a Recurrent GAN (RGAN) and Recurrent Conditional GAN (RCGAN) to
produce realistic real-valued multi-dimensional time series, with an emphasis
on their application to medical data. RGANs make use of recurrent neural
networks in the generator and the discriminator. In the case of RCGANs, both of
these RNNs are conditioned on auxiliary information. We demonstrate our models
in a set of toy datasets, where we show visually and quantitatively (using
sample likelihood and maximum mean discrepancy) that they can successfully
generate realistic time-series. We also describe novel evaluation methods for
GANs, where we generate a synthetic labelled training dataset, and evaluate on
a real test set the performance of a model trained on the synthetic data, and
vice-versa. We illustrate with these metrics that RCGANs can generate
time-series data useful for supervised training, with only minor degradation in
performance on real test data. This is demonstrated on digit classification
from 'serialised' MNIST and by training an early warning system on a medical
dataset of 17,000 patients from an intensive care unit. We further discuss and
analyse the privacy concerns that may arise when using RCGANs to generate
realistic synthetic medical time series data.
Towards Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs
Authors: Jun Yu, Xingxin Xu, Fei Gao, Shengjie Shi, Meng Wang, DaCheng Tao, Qingming Huang
Date: 2017-12-04 00:00:00
Tasks: Face Sketch Synthesis, Generative Adversarial Network

Face photo-sketch synthesis aims at generating a facial sketch/photo conditioned on a given photo/sketch. It is of wide applications including digital entertainment and law enforcement. Precisely depicting face photos/sketches remains challenging due to the restrictions on structural realism and textural consistency. While existing methods achieve compelling results, they mostly yield blurred effects and great deformation over various facial components, leading to the unrealistic feeling of synthesized images. To tackle this challenge, in this work, we propose to use the facial composition information to help the synthesis of face sketch/photo. Specially, we propose a novel composition-aided generative adversarial network (CA-GAN) for face photo-sketch synthesis. In CA-GAN, we utilize paired inputs including a face photo/sketch and the corresponding pixel-wise face labels for generating a sketch/photo. In addition, to focus training on hard-generated components and delicate facial structures, we propose a compositional reconstruction loss. Finally, we use stacked CA-GANs (SCA-GAN) to further rectify defects and add compelling details. Experimental results show that our method is capable of generating both visually comfortable and identity-preserving face sketches/photos over a wide range of challenging data. Our method achieves the state-of-the-art quality, reducing best previous Frechet Inception distance (FID) by a large margin. Besides, we demonstrate that the proposed method is of considerable generalization ability. We have made our code and results publicly available: https://fei-hdu.github.io/ca-gan/.
Proceedings of the Fifth Workshop on Proof eXchange for Theorem Proving
Authors: Catherine Dubois, Bruno Woltzenlogel Paleo
Date: 2017-12-04 00:00:00
Tasks: Automated Theorem Proving

This volume of EPTCS contains the proceedings of the Fifth Workshop on Proof
Exchange for Theorem Proving (PxTP 2017), held on September 23-24, 2017 as part
of the Tableaux, FroCoS and ITP conferences in Brasilia, Brazil. The PxTP
workshop series brings together researchers working on various aspects of
communication, integration, and cooperation between reasoning systems and
formalisms, with a special focus on proofs. The progress in computer-aided
reasoning, both automated and interactive, during the past decades, made it
possible to build deduction tools that are increasingly more applicable to a
wider range of problems and are able to tackle larger problems progressively
faster. In recent years, cooperation between such tools in larger systems has
demonstrated the potential to reduce the amount of manual intervention.
Cooperation between reasoning systems relies on availability of theoretical
formalisms and practical tools to exchange problems, proofs, and models. The
PxTP workshop series strives to encourage such cooperation by inviting
contributions on all aspects of cooperation between reasoning tools, whether
automatic or interactive.
Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge
Authors: Ryan J. Gallagher, Kyle Reing, David Kale, Greg Ver Steeg
Date: 2016-11-30 00:00:00
Proceeding: TACL 2017 1

While generative models such as Latent Dirichlet Allocation (LDA) have proven
fruitful in topic modeling, they often require detailed assumptions and careful
specification of hyperparameters. Such model complexity issues only compound
when trying to generalize generative models to incorporate human input. We
introduce Correlation Explanation (CorEx), an alternative approach to topic
modeling that does not assume an underlying generative model, and instead
learns maximally informative topics through an information-theoretic framework.
This framework naturally generalizes to hierarchical and semi-supervised
extensions with no additional modeling assumptions. In particular, word-level
domain knowledge can be flexibly incorporated within CorEx through anchor
words, allowing topic separability and representation to be promoted with
minimal human intervention. Across a variety of datasets, metrics, and
experiments, we demonstrate that CorEx produces topics that are comparable in
quality to those produced by unsupervised and semi-supervised variants of LDA.
Testing Symmetric Markov Chains from a Single Trajectory
Authors: Constantinos Daskalakis, Nishanth Dikkala, Nick Gravin
Date: 2017-04-22 00:00:00

Classical distribution testing assumes access to i.i.d. samples from the
distribution that is being tested. We initiate the study of Markov chain
testing, assuming access to a single trajectory of a Markov Chain. In
particular, we observe a single trajectory X0,...,Xt,... of an unknown,
symmetric, and finite state Markov Chain M. We do not control the starting
state X0, and we cannot restart the chain. Given our single trajectory, the
goal is to test whether M is identical to a model Markov Chain M0 , or far from
it under an appropriate notion of difference. We propose a measure of
difference between two Markov chains, motivated by the early work of Kazakos
[Kaz78], which captures the scaling behavior of the total variation distance
between trajectories sampled from the Markov chains as the length of these
trajectories grows. We provide efficient testers and information-theoretic
lower bounds for testing identity of symmetric Markov chains under our proposed
measure of difference, which are tight up to logarithmic factors if the hitting
times of the model chain M0 is O(n) in the size of the state space n.
Improving Object Detection from Scratch via Gated Feature Reuse
Authors: Zhiqiang Shen, Honghui Shi, Jiahui Yu, Hai Phan, Rogerio Feris, Liangliang Cao, Ding Liu, Xinchao Wang, Thomas Huang, Marios Savvides
Date: 2017-12-04 00:00:00
Tasks: Object, object-detection, Object Detection, Prediction

In this paper, we present a simple and parameter-efficient drop-in module for one-stage object detectors like SSD when learning from scratch (i.e., without pre-trained models). We call our module GFR (Gated Feature Reuse), which exhibits two main advantages. First, we introduce a novel gate-controlled prediction strategy enabled by Squeeze-and-Excitation to adaptively enhance or attenuate supervision at different scales based on the input object size. As a result, our model is more effective in detecting diverse sizes of objects. Second, we propose a feature-pyramids structure to squeeze rich spatial and semantic features into a single prediction layer, which strengthens feature representation and reduces the number of parameters to learn. We apply the proposed structure on DSOD and SSD detection frameworks, and evaluate the performance on PASCAL VOC 2007, 2012 and COCO datasets. With fewer model parameters, GFR-DSOD outperforms the baseline DSOD by 1.4%, 1.1%, 1.7% and 0.6%, respectively. GFR-SSD also outperforms the original SSD and SSD with dense prediction by 3.6% and 2.8% on VOC 2007 dataset. Code is available at: https://github.com/szq0214/GFR-DSOD .
A GRU-based Encoder-Decoder Approach with Attention for Online Handwritten Mathematical Expression Recognition
Authors: Jianshu Zhang, Jun Du, Li-Rong Dai
Date: 2017-12-04 00:00:00
Tasks: Decoder

In this study, we present a novel end-to-end approach based on the
encoder-decoder framework with the attention mechanism for online handwritten
mathematical expression recognition (OHMER). First, the input two-dimensional
ink trajectory information of handwritten expression is encoded via the gated
recurrent unit based recurrent neural network (GRU-RNN). Then the decoder is
also implemented by the GRU-RNN with a coverage-based attention model. The
proposed approach can simultaneously accomplish the symbol recognition and
structural analysis to output a character sequence in LaTeX format. Validated
on the CROHME 2014 competition task, our approach significantly outperforms the
state-of-the-art with an expression recognition accuracy of 52.43% by only
using the official training dataset. Furthermore, the alignments between the
input trajectories of handwritten expressions and the output LaTeX sequences
are visualized by the attention mechanism to show the effectiveness of the
proposed method.
On the Effects of Batch and Weight Normalization in Generative Adversarial Networks
Authors: Sitao Xiang, Hao Li
Date: 2017-04-13 00:00:00

Generative adversarial networks (GANs) are highly effective unsupervised
learning frameworks that can generate very sharp data, even for data such as
images with complex, highly multimodal distributions. However GANs are known to
be very hard to train, suffering from problems such as mode collapse and
disturbing visual artifacts. Batch normalization (BN) techniques have been
introduced to address the training. Though BN accelerates the training in the
beginning, our experiments show that the use of BN can be unstable and
negatively impact the quality of the trained model. The evaluation of BN and
numerous other recent schemes for improving GAN training is hindered by the
lack of an effective objective quality measure for GAN models. To address these
issues, we first introduce a weight normalization (WN) approach for GAN
training that significantly improves the stability, efficiency and the quality
of the generated samples. To allow a methodical evaluation, we introduce
squared Euclidean reconstruction error on a test set as a new objective
measure, to assess training performance in terms of speed, stability, and
quality of generated samples. Our experiments with a standard DCGAN
architecture on commonly used datasets (CelebA, LSUN bedroom, and CIFAR-10)
indicate that training using WN is generally superior to BN for GANs, achieving
10% lower mean squared loss for reconstruction and significantly better
qualitative results than BN. We further demonstrate the stability of WN on a
21-layer ResNet trained with the CelebA data set. The code for this paper is
available at https://github.com/stormraiser/gan-weightnorm-resnet
3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes
Authors: Si-Qi Liu, Daguang Xu, S. Kevin Zhou, Thomas Mertelmeier, Julia Wicklein, Anna Jerebko, Sasa Grbic, Olivier Pauly, Weidong Cai, Dorin Comaniciu
Date: 2017-11-23 00:00:00
Tasks: GPU, Lesion Detection, Medical Image Analysis, Tumor Segmentation

While deep convolutional neural networks (CNN) have been successfully applied
for 2D image analysis, it is still challenging to apply them to 3D anisotropic
volumes, especially when the within-slice resolution is much higher than the
between-slice resolution and when the amount of 3D volumes is relatively small.
On one hand, direct learning of CNN with 3D convolution kernels suffers from
the lack of data and likely ends up with poor generalization; insufficient GPU
memory limits the model size or representational power. On the other hand,
applying 2D CNN with generalizable features to 2D slices ignores between-slice
information. Coupling 2D network with LSTM to further handle the between-slice
information is not optimal due to the difficulty in LSTM learning. To overcome
the above challenges, we propose a 3D Anisotropic Hybrid Network (AH-Net) that
transfers convolutional features learned from 2D images to 3D anisotropic
volumes. Such a transfer inherits the desired strong generalization capability
for within-slice information while naturally exploiting between-slice
information for more effective modelling. The focal loss is further utilized
for more effective end-to-end learning. We experiment with the proposed 3D
AH-Net on two different medical image analysis tasks, namely lesion detection
from a Digital Breast Tomosynthesis volume, and liver and liver tumor
segmentation from a Computed Tomography volume and obtain the state-of-the-art
results.
A Two-Streamed Network for Estimating Fine-Scaled Depth Maps from Single RGB Images
Authors: Jun Li, Reinhard Klein, Angela Yao
Date: 2016-07-04 00:00:00
Proceeding: ICCV 2017 10
Tasks: Monocular Depth Estimation

Estimating depth from a single RGB image is an ill-posed and inherently
ambiguous problem. State-of-the-art deep learning methods can now estimate
accurate 2D depth maps, but when the maps are projected into 3D, they lack
local detail and are often highly distorted. We propose a fast-to-train
two-streamed CNN that predicts depth and depth gradients, which are then fused
together into an accurate and detailed depth map. We also define a novel set
loss over multiple images; by regularizing the estimation between a common set
of images, the network is less prone to over-fitting and achieves better
accuracy than competing methods. Experiments on the NYU Depth v2 dataset shows
that our depth predictions are competitive with state-of-the-art and lead to
faithful 3D projections.
Gaussian Process Regression for Arctic Coastal Erosion Forecasting
Authors: Matthew Kupilik, Frank Witmer, Euan-Angus MacLeod, Caixia Wang, Tom Ravens
Date: 2017-12-04 00:00:00
Tasks: regression

Arctic coastal morphology is governed by multiple factors, many of which are
affected by climatological changes. As the season length for shorefast ice
decreases and temperatures warm permafrost soils, coastlines are more
susceptible to erosion from storm waves. Such coastal erosion is a concern,
since the majority of the population centers and infrastructure in the Arctic
are located near the coasts. Stakeholders and decision makers increasingly need
models capable of scenario-based predictions to assess and mitigate the effects
of coastal morphology on infrastructure and land use. Our research uses
Gaussian process models to forecast Arctic coastal erosion along the Beaufort
Sea near Drew Point, AK. Gaussian process regression is a data-driven modeling
methodology capable of extracting patterns and trends from data-sparse
environments such as remote Arctic coastlines. To train our model, we use
annual coastline positions and near-shore summer temperature averages from
existing datasets and extend these data by extracting additional coastlines
from satellite imagery. We combine our calibrated models with future climate
models to generate a range of plausible future erosion scenarios. Our results
show that the Gaussian process methodology substantially improves yearly
predictions compared to linear and nonlinear least squares methods, and is
capable of generating detailed forecasts suitable for use by decision makers.
Raw Waveform-based Audio Classification Using Sample-level CNN Architectures
Authors: Jongpil Lee, Taejun Kim, Jiyoung Park, Juhan Nam
Date: 2017-12-04 00:00:00
Tasks: Audio Classification, Classification, General Classification, image-classification, Image Classification

Music, speech, and acoustic scene sound are often handled separately in the
audio domain because of their different signal characteristics. However, as the
image domain grows rapidly by versatile image classification models, it is
necessary to study extensible classification models in the audio domain as
well. In this study, we approach this problem using two types of sample-level
deep convolutional neural networks that take raw waveforms as input and uses
filters with small granularity. One is a basic model that consists of
convolution and pooling layers. The other is an improved model that
additionally has residual connections, squeeze-and-excitation modules and
multi-level concatenation. We show that the sample-level models reach
state-of-the-art performance levels for the three different categories of
sound. Also, we visualize the filters along layers and compare the
characteristics of learned filters.
A Deep Learning Approach to Drone Monitoring
Authors: Yueru Chen, Pranav Aggarwal, Jongmoo Choi, C. -C. Jay Kuo
Date: 2017-12-04 00:00:00
Tasks: Deep Learning

A drone monitoring system that integrates deep-learning-based detection and
tracking modules is proposed in this work. The biggest challenge in adopting
deep learning methods for drone detection is the limited amount of training
drone images. To address this issue, we develop a model-based drone
augmentation technique that automatically generates drone images with a
bounding box label on drone's location. To track a small flying drone, we
utilize the residual information between consecutive image frames. Finally, we
present an integrated detection and tracking system that outperforms the
performance of each individual module containing detection or tracking only.
The experiments show that, even being trained on synthetic data, the proposed
system performs well on real world drone images with complex background. The
USC drone detection and tracking dataset with user labeled bounding boxes is
available to the public.