pwc-archive/papers-with-abstracts huggingface dataset:
Planar Object Tracking in the Wild: A Benchmark
Authors: Pengpeng Liang, Yifan Wu, Hu Lu, Liming Wang, Chunyuan Liao, Haibin Ling
Date: 2017-03-23 00:00:00
Tasks: Homography Estimation, Object, Object Tracking

Planar object tracking is an actively studied problem in vision-based robotic
applications. While several benchmarks have been constructed for evaluating
state-of-the-art algorithms, there is a lack of video sequences captured in the
wild rather than in constrained laboratory environment. In this paper, we
present a carefully designed planar object tracking benchmark containing 210
videos of 30 planar objects sampled in the natural environment. In particular,
for each object, we shoot seven videos involving various challenging factors,
namely scale change, rotation, perspective distortion, motion blur, occlusion,
out-of-view, and unconstrained. The ground truth is carefully annotated
semi-manually to ensure the quality. Moreover, eleven state-of-the-art
algorithms are evaluated on the benchmark using two evaluation metrics, with
detailed analysis provided for the evaluation results. We expect the proposed
benchmark to benefit future studies on planar object tracking.
Learning Topic Models by Neighborhood Aggregation
Authors: Ryohei Hisano
Date: 2018-02-22 00:00:00
Tasks: Document Classification, Topic Models

Topic models are frequently used in machine learning owing to their high interpretability and modular structure. However, extending a topic model to include a supervisory signal, to incorporate pre-trained word embedding vectors and to include a nonlinear output function is not an easy task because one has to resort to a highly intricate approximate inference procedure. The present paper shows that topic modeling with pre-trained word embedding vectors can be viewed as implementing a neighborhood aggregation algorithm where messages are passed through a network defined over words. From the network view of topic models, nodes correspond to words in a document and edges correspond to either a relationship describing co-occurring words in a document or a relationship describing the same word in the corpus. The network view allows us to extend the model to include supervisory signals, incorporate pre-trained word embedding vectors and include a nonlinear output function in a simple manner. In experiments, we show that our approach outperforms the state-of-the-art supervised Latent Dirichlet Allocation implementation in terms of held-out document classification tasks.
Enriched Long-term Recurrent Convolutional Network for Facial Micro-Expression Recognition
Authors: Huai-Qian Khor, John See, Raphael C. -W. Phan, Weiyao Lin
Date: 2018-05-22 00:00:00
Tasks: Data Augmentation, Micro Expression Recognition, Micro-Expression Recognition, Specificity

Facial micro-expression (ME) recognition has posed a huge challenge to
researchers for its subtlety in motion and limited databases. Recently,
handcrafted techniques have achieved superior performance in micro-expression
recognition but at the cost of domain specificity and cumbersome parametric
tunings. In this paper, we propose an Enriched Long-term Recurrent
Convolutional Network (ELRCN) that first encodes each micro-expression frame
into a feature vector through CNN module(s), then predicts the micro-expression
by passing the feature vector through a Long Short-term Memory (LSTM) module.
The framework contains two different network variants: (1) Channel-wise
stacking of input data for spatial enrichment, (2) Feature-wise stacking of
features for temporal enrichment. We demonstrate that the proposed approach is
able to achieve reasonably good performance, without data augmentation. In
addition, we also present ablation studies conducted on the framework and
visualizations of what CNN "sees" when predicting the micro-expression classes.
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks
Authors: Yusuke Tsuzuku, Issei Sato, Masashi Sugiyama
Date: 2018-02-12 00:00:00
Proceeding: NeurIPS 2018 12

High sensitivity of neural networks against malicious perturbations on inputs
causes security concerns. To take a steady step towards robust classifiers, we
aim to create neural network models provably defended from perturbations. Prior
certification work requires strong assumptions on network structures and
massive computational costs, and thus the range of their applications was
limited. From the relationship between the Lipschitz constants and prediction
margins, we present a computationally efficient calculation technique to
lower-bound the size of adversarial perturbations that can deceive networks,
and that is widely applicable to various complicated networks. Moreover, we
propose an efficient training procedure that robustifies networks and
significantly improves the provably guarded areas around data points. In
experimental evaluations, our method showed its ability to provide a
non-trivial guarantee and enhance robustness for even large networks.
Training Convolutional Networks with Web Images
Authors: Nizar Massouh
Date: 2018-05-22 00:00:00
Tasks: Deep Learning, General Classification

In this thesis we investigate the effect of using web images to build a large
scale database to be used along a deep learning method for a classification
task. We replicate the ImageNet large scale database (ILSVRC-2012) from images
collected from the web using 4 different download strategies varying: the
search engine, the query and the image resolution. As a deep learning method,
we will choose the Convolutional Neural Network that was very successful with
recognition tasks; the AlexNet.
Estimating the Rating of Reviewers Based on the Text
Authors: Mohammadamir Kavousi, Sepehr Saadatmand
Date: 2018-05-22 00:00:00

User-generated texts such as reviews and social media are valuable sources of
information. Online reviews are important assets for users to buy a product,
see a movie, or make a decision. Therefore, rating of a review is one of the
reliable factors for all users to read and trust the reviews. This paper
analyzes the texts of the reviews to evaluate and predict the ratings.
Moreover, we study the effect of lexical features generated from text as well
as sentimental words on the accuracy of rating prediction. Our analysis show
that words with high information gain score are more efficient compared to
words with high TF-IDF value. In addition, we explore the best number of
features for predicting the ratings of the reviews.
Wikipedia for Smart Machines and Double Deep Machine Learning
Authors: Moshe Ben-Bassat
Date: 2017-11-17 00:00:00
Tasks: Anatomy, BIG-bench Machine Learning, Deep Learning, Face Recognition, Medical Diagnosis

Very important breakthroughs in data centric deep learning algorithms led to
impressive performance in transactional point applications of Artificial
Intelligence (AI) such as Face Recognition, or EKG classification. With all due
appreciation, however, knowledge blind data only machine learning algorithms
have severe limitations for non-transactional AI applications, such as medical
diagnosis beyond the EKG results. Such applications require deeper and broader
knowledge in their problem solving capabilities, e.g. integrating anatomy and
physiology knowledge with EKG results and other patient findings. Following a
review and illustrations of such limitations for several real life AI
applications, we point at ways to overcome them. The proposed Wikipedia for
Smart Machines initiative aims at building repositories of software structures
that represent humanity science & technology knowledge in various parts of
life; knowledge that we all learn in schools, universities and during our
professional life. Target readers for these repositories are smart machines;
not human. AI software developers will have these Reusable Knowledge structures
readily available, hence, the proposed name ReKopedia. Big Data is by now a
mature technology, it is time to focus on Big Knowledge. Some will be derived
from data, some will be obtained from mankind gigantic repository of knowledge.
Wikipedia for smart machines along with the new Double Deep Learning approach
offer a paradigm for integrating datacentric deep learning algorithms with
algorithms that leverage deep knowledge, e.g. evidential reasoning and
causality reasoning. For illustration, a project is described to produce
ReKopedia knowledge modules for medical diagnosis of about 1,000 disorders.
Data is important, but knowledge deep, basic, and commonsense is equally
important.
DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy
Authors: Yu Li, Fan Xu, Fa Zhang, Pingyong Xu, Mingshu Zhang, Ming Fan, Lihua Li, Xin Gao, Renmin Han
Date: 2018-05-20 00:00:00
Tasks: Bayesian Inference, Deep Learning, Super-Resolution, Time Series, Time Series Analysis

Super-resolution fluorescence microscopy, with a resolution beyond the
diffraction limit of light, has become an indispensable tool to directly
visualize biological structures in living cells at a nanometer-scale
resolution. Despite advances in high-density super-resolution fluorescent
techniques, existing methods still have bottlenecks, including extremely long
execution time, artificial thinning and thickening of structures, and lack of
ability to capture latent structures. Here we propose a novel deep learning
guided Bayesian inference approach, DLBI, for the time-series analysis of
high-density fluorescent images. Our method combines the strength of deep
learning and statistical inference, where deep learning captures the underlying
distribution of the fluorophores that are consistent with the observed
time-series fluorescent images by exploring local features and correlation
along time-axis, and statistical inference further refines the ultrastructure
extracted by deep learning and endues physical meaning to the final image.
Comprehensive experimental results on both real and simulated datasets
demonstrate that our method provides more accurate and realistic local patch
and large-field reconstruction than the state-of-the-art method, the 3B
analysis, while our method is more than two orders of magnitude faster. The
main program is available at https://github.com/lykaust15/DLBI
Spherical Convolutional Neural Network for 3D Point Clouds
Authors: Huan Lei, Naveed Akhtar, Ajmal Mian
Date: 2018-05-21 00:00:00
Tasks: 3D Object Classification, General Classification, Translation

We propose a neural network for 3D point cloud processing that exploits
`spherical' convolution kernels and octree partitioning of space. The proposed
metric-based spherical kernels systematically quantize point neighborhoods to
identify local geometric structures in data, while maintaining the properties
of translation-invariance and asymmetry. The network architecture itself is
guided by octree data structuring that takes full advantage of the sparse
nature of irregular point clouds. We specify spherical kernels with the help of
neurons in each layer that in turn are associated with spatial locations. We
exploit this association to avert dynamic kernel generation during network
training, that enables efficient learning with high resolution point clouds. We
demonstrate the utility of the spherical convolutional neural network for 3D
object classification on standard benchmark datasets.
Learning to Optimize via Wasserstein Deep Inverse Optimal Control
Authors: Yichen Wang, Le Song, Hongyuan Zha
Date: 2018-05-22 00:00:00
Tasks: Generative Adversarial Network, Recommendation Systems, Reinforcement Learning

We study the inverse optimal control problem in social sciences: we aim at
learning a user's true cost function from the observed temporal behavior. In
contrast to traditional phenomenological works that aim to learn a generative
model to fit the behavioral data, we propose a novel variational principle and
treat user as a reinforcement learning algorithm, which acts by optimizing his
cost function. We first propose a unified KL framework that generalizes
existing maximum entropy inverse optimal control methods. We further propose a
two-step Wasserstein inverse optimal control framework. In the first step, we
compute the optimal measure with a novel mass transport equation. In the second
step, we formulate the learning problem as a generative adversarial network. In
two real world experiments - recommender systems and social networks, we show
that our framework obtains significant performance gains over both existing
inverse optimal control methods and point process based generative models.
On the Convergence and Robustness of Training GANs with Regularized Optimal Transport
Authors: Maziar Sanjabi, Jimmy Ba, Meisam Razaviyayn, Jason D. Lee
Date: 2018-02-22 00:00:00
Proceeding: NeurIPS 2018 12

Generative Adversarial Networks (GANs) are one of the most practical methods
for learning data distributions. A popular GAN formulation is based on the use
of Wasserstein distance as a metric between probability distributions.
Unfortunately, minimizing the Wasserstein distance between the data
distribution and the generative model distribution is a computationally
challenging problem as its objective is non-convex, non-smooth, and even hard
to compute. In this work, we show that obtaining gradient information of the
smoothed Wasserstein GAN formulation, which is based on regularized Optimal
Transport (OT), is computationally effortless and hence one can apply first
order optimization methods to minimize this objective. Consequently, we
establish theoretical convergence guarantee to stationarity for a proposed
class of GAN optimization algorithms. Unlike the original non-smooth
formulation, our algorithm only requires solving the discriminator to
approximate optimality. We apply our method to learning MNIST digits as well as
CIFAR-10images. Our experiments show that our method is computationally
efficient and generates images comparable to the state of the art algorithms
given the same architecture and computational power.
An Incremental Path-Following Splitting Method for Linearly Constrained Nonconvex Nonsmooth Programs
Authors: Linbo Qiao, Wei Liu, Steven Hoi
Date: 2018-01-30 00:00:00

The stationary point of Problem 2 is NOT the stationary point of Problem 1.
We are sorry and we are working on fixing this error.
Joint Image Captioning and Question Answering
Authors: Jialin Wu, Zeyuan Hu, Raymond J. Mooney
Date: 2018-05-22 00:00:00
Tasks: Image Captioning, Question Answering, Visual Question Answering (VQA)

Answering visual questions need acquire daily common knowledge and model the
semantic connection among different parts in images, which is too difficult for
VQA systems to learn from images with the only supervision from answers.
Meanwhile, image captioning systems with beam search strategy tend to generate
similar captions and fail to diversely describe images. To address the
aforementioned issues, we present a system to have these two tasks compensate
with each other, which is capable of jointly producing image captions and
answering visual questions. In particular, we utilize question and image
features to generate question-related captions and use the generated captions
as additional features to provide new knowledge to the VQA system. For image
captioning, our system attains more informative results in term of the relative
improvements on VQA tasks as well as competitive results using automated
metrics. Applying our system to the VQA tasks, our results on VQA v2 dataset
achieve 65.8% using generated captions and 69.1% using annotated captions in
validation set and 68.4% in the test-standard set. Further, an ensemble of 10
models results in 69.7% in the test-standard split.
Designing the Game to Play: Optimizing Payoff Structure in Security Games
Authors: Zheyuan Ryan Shi, Ziye Tang, Long Tran-Thanh, Rohit Singh, Fei Fang
Date: 2018-05-05 00:00:00
Tasks: Form

Effective game-theoretic modeling of defender-attacker behavior is becoming
increasingly important. In many domains, the defender functions not only as a
player but also the designer of the game's payoff structure. We study
Stackelberg Security Games where the defender, in addition to allocating
defensive resources to protect targets from the attacker, can strategically
manipulate the attacker's payoff under budget constraints in weighted L^p-norm
form regarding the amount of change. Focusing on problems with weighted
L^1-norm form constraint, we present (i) a mixed integer linear program-based
algorithm with approximation guarantee; (ii) a branch-and-bound based algorithm
with improved efficiency achieved by effective pruning; (iii) a polynomial time
approximation scheme for a special but practical class of problems. In
addition, we show that problems under budget constraints in L^0-norm form and
weighted L^\infty-norm form can be solved in polynomial time. We provide an
extensive experimental evaluation of our proposed algorithms.
Learning and Transferring IDs Representation in E-commerce
Authors: Kui Zhao, Yuechuan Li, Zhaoqian Shuai, Cheng Yang
Date: 2017-12-22 00:00:00

Many machine intelligence techniques are developed in E-commerce and one of
the most essential components is the representation of IDs, including user ID,
item ID, product ID, store ID, brand ID, category ID etc. The classical
encoding based methods (like one-hot encoding) are inefficient in that it
suffers sparsity problems due to its high dimension, and it cannot reflect the
relationships among IDs, either homogeneous or heterogeneous ones. In this
paper, we propose an embedding based framework to learn and transfer the
representation of IDs. As the implicit feedbacks of users, a tremendous amount
of item ID sequences can be easily collected from the interactive sessions. By
jointly using these informative sequences and the structural connections among
IDs, all types of IDs can be embedded into one low-dimensional semantic space.
Subsequently, the learned representations are utilized and transferred in four
scenarios: (i) measuring the similarity between items, (ii) transferring from
seen items to unseen items, (iii) transferring across different domains, (iv)
transferring across different tasks. We deploy and evaluate the proposed
approach in Hema App and the results validate its effectiveness.
Adversarial Examples that Fool both Computer Vision and Time-Limited Humans
Authors: Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alex Kurakin, Ian Goodfellow, Jascha Sohl-Dickstein
Date: 2018-02-22 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: BIG-bench Machine Learning, Open-Ended Question Answering

Machine learning models are vulnerable to adversarial examples: small changes
to images can cause computer vision models to make mistakes such as identifying
a school bus as an ostrich. However, it is still an open question whether
humans are prone to similar mistakes. Here, we address this question by
leveraging recent techniques that transfer adversarial examples from computer
vision models with known parameters and architecture to other models with
unknown parameters and architecture, and by matching the initial processing of
the human visual system. We find that adversarial examples that strongly
transfer across computer vision models influence the classifications made by
time-limited human observers.
Learning Markov Clustering Networks for Scene Text Detection
Authors: Zichuan Liu, Guosheng Lin, Sheng Yang, Jiashi Feng, Weisi Lin, Wang Ling Goh
Date: 2018-05-22 00:00:00
Proceeding: CVPR 2018 6
Tasks: Clustering, Scene Text Detection, Text Detection

A novel framework named Markov Clustering Network (MCN) is proposed for fast
and robust scene text detection. MCN predicts instance-level bounding boxes by
firstly converting an image into a Stochastic Flow Graph (SFG) and then
performing Markov Clustering on this graph. Our method can detect text objects
with arbitrary size and orientation without prior knowledge of object size. The
stochastic flow graph encode objects' local correlation and semantic
information. An object is modeled as strongly connected nodes, which allows
flexible bottom-up detection for scale-varying and rotated objects. MCN
generates bounding boxes without using Non-Maximum Suppression, and it can be
fully parallelized on GPUs. The evaluation on public benchmarks shows that our
method outperforms the existing methods by a large margin in detecting
multioriented text objects. MCN achieves new state-of-art performance on
challenging MSRA-TD500 dataset with precision of 0.88, recall of 0.79 and
F-score of 0.83. Also, MCN achieves realtime inference with frame rate of 34
FPS, which is $1.5\times$ speedup when compared with the fastest scene text
detection algorithm.
Adversarial Training for Disease Prediction from Electronic Health Records with Missing Data
Authors: Uiwon Hwang, Sungwoon Choi, Han-Byoel Lee, Sungroh Yoon
Date: 2017-11-11 00:00:00
Tasks: Disease Prediction, Imputation, Missing Values, Prediction, Specificity

Electronic health records (EHRs) have contributed to the computerization of
patient records and can thus be used not only for efficient and systematic
medical services, but also for research on biomedical data science. However,
there are many missing values in EHRs when provided in matrix form, which is an
important issue in many biomedical EHR applications. In this paper, we propose
a two-stage framework that includes missing data imputation and disease
prediction to address the missing data problem in EHRs. We compared the disease
prediction performance of generative adversarial networks (GANs) and
conventional learning algorithms in combination with missing data prediction
methods. As a result, we obtained a level of accuracy of 0.9777, sensitivity of
0.9521, specificity of 0.9925, area under the receiver operating characteristic
curve (AUC-ROC) of 0.9889, and F-score of 0.9688 with a stacked autoencoder as
the missing data prediction method and an auxiliary classifier GAN (AC-GAN) as
the disease prediction method. The comparison results show that a combination
of a stacked autoencoder and an AC-GAN significantly outperforms other existing
approaches. Our results suggest that the proposed framework is more robust for
disease prediction from EHRs with missing data.
DifNet: Semantic Segmentation by Diffusion Networks
Authors: Peng Jiang, Fanglin Gu, Yunhai Wang, Changhe Tu, Baoquan Chen
Date: 2018-05-21 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Segmentation, Semantic Segmentation

Deep Neural Networks (DNNs) have recently shown state of the art performance
on semantic segmentation tasks, however, they still suffer from problems of
poor boundary localization and spatial fragmented predictions. The difficulties
lie in the requirement of making dense predictions from a long path model all
at once since details are hard to keep when data goes through deeper layers.
Instead, in this work, we decompose this difficult task into two relative
simple sub-tasks: seed detection which is required to predict initial
predictions without the need of wholeness and preciseness, and similarity
estimation which measures the possibility of any two nodes belong to the same
class without the need of knowing which class they are. We use one branch
network for one sub-task each, and apply a cascade of random walks base on
hierarchical semantics to approximate a complex diffusion process which
propagates seed information to the whole image according to the estimated
similarities. The proposed DifNet consistently produces improvements over the
baseline models with the same depth and with the equivalent number of
parameters, and also achieves promising performance on Pascal VOC and Pascal
Context dataset. OurDifNet is trained end-to-end without complex loss
functions.