pwc-archive/papers-with-abstracts huggingface dataset:
Image Stitching by Line-guided Local Warping with Global Similarity Constraint
Authors: Tian-Zhu Xiang, Gui-Song Xia, Xiang Bai, Liangpei Zhang
Date: 2017-02-25 00:00:00
Tasks: Image Stitching

Low-textured image stitching remains a challenging problem. It is difficult
to achieve good alignment and it is easy to break image structures due to
insufficient and unreliable point correspondences. Moreover, because of the
viewpoint variations between multiple images, the stitched images suffer from
projective distortions. To solve these problems, this paper presents a
line-guided local warping method with a global similarity constraint for image
stitching. Line features which serve well for geometric descriptions and scene
constraints, are employed to guide image stitching accurately. On one hand, the
line features are integrated into a local warping model through a designed
weight function. On the other hand, line features are adopted to impose strong
geometric constraints, including line correspondence and line colinearity, to
improve the stitching performance through mesh optimization. To mitigate
projective distortions, we adopt a global similarity constraint, which is
integrated with the projective warps via a designed weight strategy. This
constraint causes the final warp to slowly change from a projective to a
similarity transformation across the image. Finally, the images undergo a
two-stage alignment scheme that provides accurate alignment and reduces
projective distortion. We evaluate our method on a series of images and compare
it with several other methods. The experimental results demonstrate that the
proposed method provides a convincing stitching performance and that it
outperforms other state-of-the-art methods.
TransFlow: Unsupervised Motion Flow by Joint Geometric and Pixel-level Estimation
Authors: Stefano Alletto, Davide Abati, Simone Calderara, Rita Cucchiara, Luca Rigazio
Date: 2017-06-01 00:00:00
Tasks: Optical Flow Estimation

We address unsupervised optical flow estimation for ego-centric motion. We
argue that optical flow can be cast as a geometrical warping between two
successive video frames and devise a deep architecture to estimate such
transformation in two stages. First, a dense pixel-level flow is computed with
a geometric prior imposing strong spatial constraints. Such prior is typical of
driving scenes, where the point of view is coherent with the vehicle motion. We
show how such global transformation can be approximated with an homography and
how spatial transformer layers can be employed to compute the flow field
implied by such transformation. The second stage then refines the prediction
feeding a second deeper network. A final reconstruction loss compares the
warping of frame X(t) with the subsequent frame X(t+1) and guides both
estimates. The model, which we named TransFlow, performs favorably compared to
other unsupervised algorithms, and shows better generalization compared to
supervised methods with a 3x reduction in error on unseen data.
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning
Authors: Marco Fraccaro, Simon Kamronn, Ulrich Paquet, Ole Winther
Date: 2017-10-16 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Imputation

This paper takes a step towards temporal reasoning in a dynamically changing
video, not in the pixel space that constitutes its frames, but in a latent
space that describes the non-linear dynamics of the objects in its world. We
introduce the Kalman variational auto-encoder, a framework for unsupervised
learning of sequential data that disentangles two latent representations: an
object's representation, coming from a recognition model, and a latent state
describing its dynamics. As a result, the evolution of the world can be
imagined and missing data imputed, both without the need to generate high
dimensional frames at each time step. The model is trained end-to-end on videos
of a variety of simulated physical systems, and outperforms competing methods
in generative and missing data imputation tasks.
Stochastic Subsampling for Factorizing Huge Matrices
Authors: Arthur Mensch, Julien Mairal, Bertrand Thirion, Gael Varoquaux
Date: 2017-01-19 00:00:00
Tasks: Dictionary Learning

We present a matrix-factorization algorithm that scales to input matrices
with both huge number of rows and columns. Learned factors may be sparse or
dense and/or non-negative, which makes our algorithm suitable for dictionary
learning, sparse component analysis, and non-negative matrix factorization. Our
algorithm streams matrix columns while subsampling them to iteratively learn
the matrix factors. At each iteration, the row dimension of a new sample is
reduced by subsampling, resulting in lower time complexity compared to a simple
streaming algorithm. Our method comes with convergence guarantees to reach a
stationary point of the matrix-factorization problem. We demonstrate its
efficiency on massive functional Magnetic Resonance Imaging data (2 TB), and on
patches extracted from hyperspectral images (103 GB). For both problems, which
involve different penalties on rows and columns, we obtain significant
speed-ups compared to state-of-the-art algorithms.
Hit Song Prediction for Pop Music by Siamese CNN with Ranking Loss
Authors: Lang-Chi Yu, Yi-Hsuan Yang, Yun-Ning Hung, Yi-An Chen
Date: 2017-10-30 00:00:00
Tasks: Prediction, regression

A model for hit song prediction can be used in the pop music industry to
identify emerging trends and potential artists or songs before they are
marketed to the public. While most previous work formulates hit song prediction
as a regression or classification problem, we present in this paper a
convolutional neural network (CNN) model that treats it as a ranking problem.
Specifically, we use a commercial dataset with daily play-counts to train a
multi-objective Siamese CNN model with Euclidean loss and pairwise ranking loss
to learn from audio the relative ranking relations among songs. Besides, we
devise a number of pair sampling methods according to some empirical
observation of the data. Our experiment shows that the proposed model with a
sampling method called A/B sampling leads to much higher accuracy in hit song
prediction than the baseline regression model. Moreover, we can further improve
the accuracy by using a neural attention mechanism to extract the highlights of
songs and by using a separate CNN model to offer high-level features of songs.
DART: Distribution Aware Retinal Transform for Event-based Cameras
Authors: Bharath Ramesh, Hong Yang, Garrick Orchard, Ngoc Anh Le Thi, Shihao Zhang, Cheng Xiang
Date: 2017-10-30 00:00:00
Tasks: Event-based vision, General Classification, Object, Object Tracking, One-Shot Learning

We introduce a generic visual descriptor, termed as distribution aware
retinal transform (DART), that encodes the structural context using log-polar
grids for event cameras. The DART descriptor is applied to four different
problems, namely object classification, tracking, detection and feature
matching: (1) The DART features are directly employed as local descriptors in a
bag-of-features classification framework and testing is carried out on four
standard event-based object datasets (N-MNIST, MNIST-DVS, CIFAR10-DVS,
NCaltech-101). (2) Extending the classification system, tracking is
demonstrated using two key novelties: (i) For overcoming the low-sample problem
for the one-shot learning of a binary classifier, statistical bootstrapping is
leveraged with online learning; (ii) To achieve tracker robustness, the scale
and rotation equivariance property of the DART descriptors is exploited for the
one-shot learning. (3) To solve the long-term object tracking problem, an
object detector is designed using the principle of cluster majority voting. The
detection scheme is then combined with the tracker to result in a high
intersection-over-union score with augmented ground truth annotations on the
publicly available event camera dataset. (4) Finally, the event context encoded
by DART greatly simplifies the feature correspondence problem, especially for
spatio-temporal slices far apart in time, which has not been explicitly tackled
in the event-based vision domain.
Understanding GANs: the LQG Setting
Authors: Soheil Feizi, Farzan Farnia, Tony Ginart, David Tse
Date: 2017-10-30 00:00:00
Proceeding: ICLR 2018 1

Generative Adversarial Networks (GANs) have become a popular method to learn
a probability model from data. In this paper, we aim to provide an
understanding of some of the basic issues surrounding GANs including their
formulation, generalization and stability on a simple benchmark where the data
has a high-dimensional Gaussian distribution. Even in this simple benchmark,
the GAN problem has not been well-understood as we observe that existing
state-of-the-art GAN architectures may fail to learn a proper generative
distribution owing to (1) stability issues (i.e., convergence to bad local
solutions or not converging at all), (2) approximation issues (i.e., having
improper global GAN optimizers caused by inappropriate GAN's loss functions),
and (3) generalizability issues (i.e., requiring large number of samples for
training). In this setup, we propose a GAN architecture which recovers the
maximum-likelihood solution and demonstrates fast generalization. Moreover, we
analyze global stability of different computational approaches for the proposed
GAN optimization and highlight their pros and cons. Finally, we outline an
extension of our model-based approach to design GANs in more complex setups
than the considered Gaussian benchmark.
Clustering Mixed Datasets Using Homogeneity Analysis with Applications to Big Data
Authors: Rajiv Sambasivan, Sourish Das
Date: 2016-08-17 00:00:00
Tasks: Clustering

Datasets with a mixture of numerical and categorical attributes are routinely
encountered in many application domains. In this work we examine an approach to
clustering such datasets using homogeneity analysis. Homogeneity analysis
determines a euclidean representation of the data. This can be analyzed by
leveraging the large body of tools and techniques for data with a euclidean
representation. Experiments conducted as part of this study suggest that this
approach can be useful in the analysis and exploration of big datasets with a
mixture of numerical and categorical attributes.
How deep learning works --The geometry of deep learning
Authors: Xiao Dong, Jiasong Wu, Ling Zhou
Date: 2017-10-30 00:00:00
Tasks: Deep Learning, Template Matching

Why and how that deep learning works well on different tasks remains a
mystery from a theoretical perspective. In this paper we draw a geometric
picture of the deep learning system by finding its analogies with two existing
geometric structures, the geometry of quantum computations and the geometry of
the diffeomorphic template matching. In this framework, we give the geometric
structures of different deep learning systems including convolutional neural
networks, residual networks, recursive neural networks, recurrent neural
networks and the equilibrium prapagation framework. We can also analysis the
relationship between the geometrical structures and their performance of
different networks in an algorithmic level so that the geometric framework may
guide the design of the structures and algorithms of deep learning systems.
Generative Adversarial Source Separation
Authors: Cem Subakan, Paris Smaragdis
Date: 2017-10-30 00:00:00

Generative source separation methods such as non-negative matrix
factorization (NMF) or auto-encoders, rely on the assumption of an output
probability density. Generative Adversarial Networks (GANs) can learn data
distributions without needing a parametric assumption on the output density. We
show on a speech source separation experiment that, a multi-layer perceptron
trained with a Wasserstein-GAN formulation outperforms NMF, auto-encoders
trained with maximum likelihood, and variational auto-encoders in terms of
source to distortion ratio.
Understanding Hidden Memories of Recurrent Neural Networks
Authors: Yao Ming, Shaozu Cao, Ruixiang Zhang, Zhen Li, Yuanzhe Chen, Yangqiu Song, Huamin Qu
Date: 2017-10-30 00:00:00
Tasks: Clustering, Sentence

Recurrent neural networks (RNNs) have been successfully applied to various
natural language processing (NLP) tasks and achieved better results than
conventional methods. However, the lack of understanding of the mechanisms
behind their effectiveness limits further improvements on their architectures.
In this paper, we present a visual analytics method for understanding and
comparing RNN models for NLP tasks. We propose a technique to explain the
function of individual hidden state units based on their expected response to
input texts. We then co-cluster hidden state units and words based on the
expected response and visualize co-clustering results as memory chips and word
clouds to provide more structured knowledge on RNNs' hidden states. We also
propose a glyph-based sequence visualization based on aggregate information to
analyze the behavior of an RNN's hidden state at the sentence-level. The
usability and effectiveness of our method are demonstrated through case studies
and reviews from domain experts.
Transfer Learning to Learn with Multitask Neural Model Search
Authors: Catherine Wong, Andrea Gesmundo
Date: 2017-10-30 00:00:00
Proceeding: ICLR 2018 1
Tasks: Hyperparameter Optimization, Neural Architecture Search, Reinforcement Learning, Transfer Learning

Deep learning models require extensive architecture design exploration and
hyperparameter optimization to perform well on a given task. The exploration of
the model design space is often made by a human expert, and optimized using a
combination of grid search and search heuristics over a large space of possible
choices. Neural Architecture Search (NAS) is a Reinforcement Learning approach
that has been proposed to automate architecture design. NAS has been
successfully applied to generate Neural Networks that rival the best
human-designed architectures. However, NAS requires sampling, constructing, and
training hundreds to thousands of models to achieve well-performing
architectures. This procedure needs to be executed from scratch for each new
task. The application of NAS to a wide set of tasks currently lacks a way to
transfer generalizable knowledge across tasks. In this paper, we present the
Multitask Neural Model Search (MNMS) controller. Our goal is to learn a
generalizable framework that can condition model construction on successful
model searches for previously seen tasks, thus significantly speeding up the
search for new tasks. We demonstrate that MNMS can conduct an automated
architecture search for multiple tasks simultaneously while still learning
well-performing, specialized models for each task. We then show that
pre-trained MNMS controllers can transfer learning to new tasks. By leveraging
knowledge from previous searches, we find that pre-trained MNMS models start
from a better location in the search space and reduce search time on unseen
tasks, while still discovering models that outperform published human-designed
models.
Distance-based classifier by data transformation for high-dimension, strongly spiked eigenvalue models
Authors: Makoto Aoshima, Kazuyoshi Yata
Date: 2017-10-30 00:00:00

We consider classifiers for high-dimensional data under the strongly spiked
eigenvalue (SSE) model. We first show that high-dimensional data often have the
SSE model. We consider a distance-based classifier using eigenstructures for
the SSE model. We apply the noise reduction methodology to estimation of the
eigenvalues and eigenvectors in the SSE model. We create a new distance-based
classifier by transforming data from the SSE model to the non-SSE model. We
give simulation studies and discuss the performance of the new classifier.
Finally, we demonstrate the new classifier by using microarray data sets.
Cascade Region Proposal and Global Context for Deep Object Detection
Authors: Qiaoyong Zhong, Chao Li, Yingying Zhang, Di Xie, Shicai Yang, ShiLiang Pu
Date: 2017-10-30 00:00:00
Tasks: Object, object-detection, Object Detection, Object Recognition, Region Proposal

Deep region-based object detector consists of a region proposal step and a
deep object recognition step. In this paper, we make significant improvements
on both of the two steps. For region proposal we propose a novel lightweight
cascade structure which can effectively improve RPN proposal quality. For
object recognition we re-implement global context modeling with a few
modications and obtain a performance boost (4.2% mAP gain on the ILSVRC 2016
validation set). Besides, we apply the idea of pre-training extensively and
show its importance in both steps. Together with common training and testing
tricks, we improve Faster R-CNN baseline by a large margin. In particular, we
obtain 87.9% mAP on the PASCAL VOC 2012 test set, 65.3% on the ILSVRC 2016 test
set and 36.8% on the COCO test-std set.
Implicit Causal Models for Genome-wide Association Studies
Authors: Dustin Tran, David M. Blei
Date: 2017-10-30 00:00:00
Proceeding: ICLR 2018 1
Tasks: Bayesian Inference

Progress in probabilistic generative models has accelerated, developing
richer models with neural architectures, implicit densities, and with scalable
algorithms for their Bayesian inference. However, there has been limited
progress in models that capture causal relationships, for example, how
individual genetic factors cause major human diseases. In this work, we focus
on two challenges in particular: How do we build richer causal models, which
can capture highly nonlinear relationships and interactions between multiple
causes? How do we adjust for latent confounders, which are variables
influencing both cause and effect and which prevent learning of causal
relationships? To address these challenges, we synthesize ideas from causality
and modern probabilistic modeling. For the first, we describe implicit causal
models, a class of causal models that leverages neural architectures with an
implicit density. For the second, we describe an implicit causal model that
adjusts for confounders by sharing strength across examples. In experiments, we
scale Bayesian inference on up to a billion genetic measurements. We achieve
state of the art accuracy for identifying causal factors: we significantly
outperform existing genetics methods by an absolute difference of 15-45.3%.
Learning neural trans-dimensional random field language models with noise-contrastive estimation
Authors: Bin Wang, Zhijian Ou
Date: 2017-10-30 00:00:00
Tasks: parameter estimation, speech-recognition, Speech Recognition

Trans-dimensional random field language models (TRF LMs) where sentences are
modeled as a collection of random fields, have shown close performance with
LSTM LMs in speech recognition and are computationally more efficient in
inference. However, the training efficiency of neural TRF LMs is not
satisfactory, which limits the scalability of TRF LMs on large training corpus.
In this paper, several techniques on both model formulation and parameter
estimation are proposed to improve the training efficiency and the performance
of neural TRF LMs. First, TRFs are reformulated in the form of exponential
tilting of a reference distribution. Second, noise-contrastive estimation (NCE)
is introduced to jointly estimate the model parameters and normalization
constants. Third, we extend the neural TRF LMs by marrying the deep
convolutional neural network (CNN) and the bidirectional LSTM into the
potential function to extract the deep hierarchical features and
bidirectionally sequential features. Utilizing all the above techniques enables
the successful and efficient training of neural TRF LMs on a 40x larger
training set with only 1/3 training time and further reduces the WER with
relative reduction of 4.7% on top of a strong LSTM LM baseline.
Fuzzy clustering using linguistic-valued exponent
Authors: Hung Thai Le, Khang Ding Tran, Hung Van Le
Date: 2017-10-30 00:00:00
Tasks: Clustering

The purpose of this paper is to study the algorithm FCM and some of its
famous innovations, analyse and discover the method of applying hedge algebra
theory that uses algebra to represent linguistic-valued variables, to FCM.
Then, this paper will propose a new FCM-based algorithm which uses hedge
algebra to model FCM's exponent parameter. Finally, the design, analysis and
implementation of the new algorithm as well some experimental results will be
presented to prove our algorithm's capacity of solving clustering problems in
practice.
Contextual Regression: An Accurate and Conveniently Interpretable Nonlinear Model for Mining Discovery from Scientific Data
Authors: Chengyu Liu, Wei Wang
Date: 2017-10-30 00:00:00
Tasks: feature selection, Network Embedding, regression, scientific discovery

Machine learning algorithms such as linear regression, SVM and neural network
have played an increasingly important role in the process of scientific
discovery. However, none of them is both interpretable and accurate on
nonlinear datasets. Here we present contextual regression, a method that joins
these two desirable properties together using a hybrid architecture of neural
network embedding and dot product layer. We demonstrate its high prediction
accuracy and sensitivity through the task of predictive feature selection on a
simulated dataset and the application of predicting open chromatin sites in the
human genome. On the simulated data, our method achieved high fidelity recovery
of feature contributions under random noise levels up to 200%. On the open
chromatin dataset, the application of our method not only outperformed the
state of the art method in terms of accuracy, but also unveiled two previously
unfound open chromatin related histone marks. Our method can fill the blank of
accurate and interpretable nonlinear modeling in scientific data mining tasks.
BAS: Beetle Antennae Search Algorithm for Optimization Problems
Authors: Xiangyuan Jiang, Shuai Li
Date: 2017-10-30 00:00:00

Meta-heuristic algorithms have become very popular because of powerful
performance on the optimization problem. A new algorithm called beetle antennae
search algorithm (BAS) is proposed in the paper inspired by the searching
behavior of longhorn beetles. The BAS algorithm imitates the function of
antennae and the random walking mechanism of beetles in nature, and then two
main steps of detecting and searching are implemented. Finally, the algorithm
is benchmarked on 2 well-known test functions, in which the numerical results
validate the efficacy of the proposed BAS algorithm.
A Saak Transform Approach to Efficient, Scalable and Robust Handwritten Digits Recognition
Authors: Yueru Chen, Zhuwei Xu, Shanshan Cai, Yujian Lang, C. -C. Jay Kuo
Date: 2017-10-29 00:00:00

An efficient, scalable and robust approach to the handwritten digits
recognition problem based on the Saak transform is proposed in this work.
First, multi-stage Saak transforms are used to extract a family of joint
spatial-spectral representations of input images. Then, the Saak coefficients
are used as features and fed into the SVM classifier for the classification
task. In order to control the size of Saak coefficients, we adopt a lossy Saak
transform that uses the principal component analysis (PCA) to select a smaller
set of transform kernels. The handwritten digits recognition problem is well
solved by the convolutional neural network (CNN) such as the LeNet-5. We
conduct a comparative study on the performance of the LeNet-5 and the
Saak-transform-based solutions in terms of scalability and robustness as well
as the efficiency of lossless and lossy Saak transforms under a comparable
accuracy level.