pwc-archive/papers-with-abstracts huggingface dataset:
A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis
Authors: Tor Lattimore
Date: 2017-03-27 00:00:00
Proceeding: NeurIPS 2017 12

Existing strategies for finite-armed stochastic bandits mostly depend on a
parameter of scale that must be known in advance. Sometimes this is in the form
of a bound on the payoffs, or the knowledge of a variance or subgaussian
parameter. The notable exceptions are the analysis of Gaussian bandits with
unknown mean and variance by Cowan and Katehakis [2015] and of uniform
distributions with unknown support [Cowan and Katehakis, 2015]. The results
derived in these specialised cases are generalised here to the non-parametric
setup, where the learner knows only a bound on the kurtosis of the noise, which
is a scale free measure of the extremity of outliers.
Multiple Instance Learning with the Optimal Sub-Pattern Assignment Metric
Authors: Quang N. Tran, Ba-Ngu Vo, Dinh Phung, Ba-Tuong Vo, Thuong Nguyen
Date: 2017-03-27 00:00:00
Tasks: Clustering, General Classification, Multiple Instance Learning, Novelty Detection

Multiple instance data are sets or multi-sets of unordered elements. Using
metrics or distances for sets, we propose an approach to several multiple
instance learning tasks, such as clustering (unsupervised learning),
classification (supervised learning), and novelty detection (semi-supervised
learning). In particular, we introduce the Optimal Sub-Pattern Assignment
metric to multiple instance learning so as to provide versatile design choices.
Numerical experiments on both simulated and real data are presented to
illustrate the versatility of the proposed solution.
Using Neural Networks to Compute Approximate and Guaranteed Feasible Hamilton-Jacobi-Bellman PDE Solutions
Authors: Frank Jiang, Glen Chou, Mo Chen, Claire J. Tomlin
Date: 2016-11-10 00:00:00

To sidestep the curse of dimensionality when computing solutions to
Hamilton-Jacobi-Bellman partial differential equations (HJB PDE), we propose an
algorithm that leverages a neural network to approximate the value function. We
show that our final approximation of the value function generates near optimal
controls which are guaranteed to successfully drive the system to a target
state. Our framework is not dependent on state space discretization, leading to
a significant reduction in computation time and space complexity in comparison
with dynamic programming-based approaches. Using this grid-free approach also
enables us to plan over longer time horizons with relatively little additional
computation overhead. Unlike many previous neural network HJB PDE approximating
formulations, our approximation is strictly conservative and hence any
trajectories we generate will be strictly feasible. For demonstration, we
specialize our new general framework to the Dubins car model and discuss how
the framework can be applied to other models with higher-dimensional state
spaces.
Photo Filter Recommendation by Category-Aware Aesthetic Learning
Authors: Wei-Tse Sun, Ting-Hsuan Chao, Yin-Hsi Kuo, Winston H. Hsu
Date: 2016-08-18 00:00:00
Tasks: General Classification

Nowadays, social media has become a popular platform for the public to share
photos. To make photos more visually appealing, users usually apply filters on
their photos without domain knowledge. However, due to the growing number of
filter types, it becomes a major issue for users to choose the best filter
type. For this purpose, filter recommendation for photo aesthetics takes an
important role in image quality ranking problems. In these years, several works
have declared that Convolutional Neural Networks (CNNs) outperform traditional
methods in image aesthetic categorization, which classifies images into high or
low quality. Most of them do not consider the effect on filtered images; hence,
we propose a novel image aesthetic learning for filter recommendation. Instead
of binarizing image quality, we adjust the state-of-the-art CNN architectures
and design a pairwise loss function to learn the embedded aesthetic responses
in hidden layers for filtered images. Based on our pilot study, we observe
image categories (e.g., portrait, landscape, food) will affect user preference
on filter selection. We further integrate category classification into our
proposed aesthetic-oriented models. To the best of our knowledge, there is no
public dataset for aesthetic judgment with filtered images. We create a new
dataset called Filter Aesthetic Comparison Dataset (FACD). It contains 28,160
filtered images based on the AVA dataset and 42,240 reliable image pairs with
aesthetic annotations using Amazon Mechanical Turk. It is the first dataset
containing filtered images and user preference labels. We conduct experiments
on the collected FACD for filter recommendation, and the results show that our
proposed category-aware aesthetic learning outperforms aesthetic classification
methods (e.g., 12% relative improvement).
DropRegion Training of Inception Font Network for High-Performance Chinese Font Recognition
Authors: Shuangping Huangm Zhuoyao Zhong, Lianwen Jin, Shuye Zhang, Haobin Wang
Date: 2017-03-17 00:00:00
Tasks: Font Recognition

Chinese font recognition (CFR) has gained significant attention in recent
years. However, due to the sparsity of labeled font samples and the structural
complexity of Chinese characters, CFR is still a challenging task. In this
paper, a DropRegion method is proposed to generate a large number of stochastic
variant font samples whose local regions are selectively disrupted and an
inception font network (IFN) with two additional convolutional neural network
(CNN) structure elements, i.e., a cascaded cross-channel parametric pooling
(CCCP) and global average pooling, is designed. Because the distribution of
strokes in a font image is non-stationary, an elastic meshing technique that
adaptively constructs a set of local regions with equalized information is
developed. Thus, DropRegion is seamlessly embedded in the IFN, which enables
end-to-end training; the proposed DropRegion-IFN can be used for high
performance CFR. Experimental results have confirmed the effectiveness of our
new approach for CFR.
A Visual Measure of Changes to Weighted Self-Organizing Map Patterns
Authors: Younjin Chung, Joachim Gudmundsson, Masahiro Takatsuka
Date: 2017-03-27 00:00:00

Estimating output changes by input changes is the main task in causal
analysis. In previous work, input and output Self-Organizing Maps (SOMs) were
associated for causal analysis of multivariate and nonlinear data. Based on the
association, a weight distribution of the output conditional on a given input
was obtained over the output map space. Such a weighted SOM pattern of the
output changes when the input changes. In order to analyze the change, it is
important to measure the difference of the patterns. Many methods have been
proposed for the dissimilarity measure of patterns. However, it remains a major
challenge when attempting to measure how the patterns change. In this paper, we
propose a visualization approach that simplifies the comparison of the
difference in terms of the pattern property. Using this approach, the change
can be analyzed by integrating colors and star glyph shapes representing the
property dissimilarity. Ecological data is used to demonstrate the usefulness
of our approach and the experimental results show that our approach provides
the change information effectively.
Exploiting Color Name Space for Salient Object Detection
Authors: Jing Lou, Huan Wang, Longtao Chen, Fenglei Xu, Qingyuan Xia, Wei Zhu, Mingwu Ren
Date: 2017-03-27 00:00:00
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

In this paper, we will investigate the contribution of color names for the task of salient object detection. An input image is first converted to color name space, which is consisted of 11 probabilistic channels. By exploiting a surroundedness cue, we obtain a saliency map through a linear combination of a set of sequential attention maps. To overcome the limitation of only using the surroundedness cue, two global cues with respect to color names are invoked to guide the computation of a weighted saliency map. Finally, we integrate the above two saliency maps into a unified framework to generate the final result. In addition, an improved post-processing procedure is introduced to effectively suppress image backgrounds while uniformly highlight salient objects. Experimental results show that the proposed model produces more accurate saliency maps and performs well against twenty-one saliency models in terms of three evaluation metrics on three public data sets.
Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric Bayes
Authors: Feras Saad, Vikash Mansinghka
Date: 2016-11-05 00:00:00
Tasks: Missing Values, Probabilistic Programming, Specificity

Datasets with hundreds of variables and many missing values are commonplace.
In this setting, it is both statistically and computationally challenging to
detect true predictive relationships between variables and also to suppress
false positives. This paper proposes an approach that combines probabilistic
programming, information theory, and non-parametric Bayes. It shows how to use
Bayesian non-parametric modeling to (i) build an ensemble of joint probability
models for all the variables; (ii) efficiently detect marginal independencies;
and (iii) estimate the conditional mutual information between arbitrary subsets
of variables, subject to a broad class of constraints. Users can access these
capabilities using BayesDB, a probabilistic programming platform for
probabilistic data analysis, by writing queries in a simple, SQL-like language.
This paper demonstrates empirically that the method can (i) detect
context-specific (in)dependencies on challenging synthetic problems and (ii)
yield improved sensitivity and specificity over baselines from statistics and
machine learning, on a real-world database of over 300 sparsely observed
indicators of macroeconomic development and public health.
Transductive Zero-Shot Learning with Adaptive Structural Embedding
Authors: Yunlong Yu, Zhong Ji, Jichang Guo, Yanwei Pang
Date: 2017-03-27 00:00:00
Tasks: Domain Adaptation, Zero-Shot Learning

Zero-shot learning (ZSL) endows the computer vision system with the
inferential capability to recognize instances of a new category that has never
seen before. Two fundamental challenges in it are visual-semantic embedding and
domain adaptation in cross-modality learning and unseen class prediction steps,
respectively. To address both challenges, this paper presents two corresponding
methods named Adaptive STructural Embedding (ASTE) and Self-PAsed Selective
Strategy (SPASS), respectively. Specifically, ASTE formulates the
visualsemantic interactions in a latent structural SVM framework to adaptively
adjust the slack variables to embody the different reliableness among training
instances. In this way, the reliable instances are imposed with small
punishments, wheras the less reliable instances are imposed with more severe
punishments. Thus, it ensures a more discriminative embedding. On the other
hand, SPASS offers a framework to alleviate the domain shift problem in ZSL,
which exploits the unseen data in an easy to hard fashion. Particularly, SPASS
borrows the idea from selfpaced learning by iteratively selecting the unseen
instances from reliable to less reliable to gradually adapt the knowledge from
the seen domain to the unseen domain. Subsequently, by combining SPASS and
ASTE, we present a self-paced Transductive ASTE (TASTE) method to progressively
reinforce the classification capacity. Extensive experiments on three benchmark
datasets (i.e., AwA, CUB, and aPY) demonstrate the superiorities of ASTE and
TASTE. Furthermore, we also propose a fast training (FT) strategy to improve
the efficiency of most of existing ZSL methods. The FT strategy is surprisingly
simple and general enough, which can speed up the training time of most
existing methods by 4~300 times while holding the previous performance.
Transductive Zero-Shot Learning with a Self-training dictionary approach
Authors: Yunlong Yu, Zhong Ji, Xi Li, Jichang Guo, Zhongfei Zhang, Haibin Ling, Fei Wu
Date: 2017-03-27 00:00:00
Tasks: Transductive Learning, Transfer Learning, Zero-Shot Learning

As an important and challenging problem in computer vision, zero-shot
learning (ZSL) aims at automatically recognizing the instances from unseen
object classes without training data. To address this problem, ZSL is usually
carried out in the following two aspects: 1) capturing the domain distribution
connections between seen classes data and unseen classes data; and 2) modeling
the semantic interactions between the image feature space and the label
embedding space. Motivated by these observations, we propose a bidirectional
mapping based semantic relationship modeling scheme that seeks for crossmodal
knowledge transfer by simultaneously projecting the image features and label
embeddings into a common latent space. Namely, we have a bidirectional
connection relationship that takes place from the image feature space to the
latent space as well as from the label embedding space to the latent space. To
deal with the domain shift problem, we further present a transductive learning
approach that formulates the class prediction problem in an iterative refining
process, where the object classification capacity is progressively reinforced
through bootstrapping-based model updating over highly reliable instances.
Experimental results on three benchmark datasets (AwA, CUB and SUN) demonstrate
the effectiveness of the proposed approach against the state-of-the-art
approaches.
Scene Flow to Action Map: A New Representation for RGB-D based Action Recognition with Convolutional Neural Networks
Authors: Pichao Wang, Wanqing Li, Zhimin Gao, Yuyao Zhang, Chang Tang, Philip Ogunbona
Date: 2017-02-28 00:00:00
Proceeding: CVPR 2017 7
Tasks: 3D Action Recognition, Action Recognition, Temporal Action Localization

Scene flow describes the motion of 3D objects in real world and potentially
could be the basis of a good feature for 3D action recognition. However, its
use for action recognition, especially in the context of convolutional neural
networks (ConvNets), has not been previously studied. In this paper, we propose
the extraction and use of scene flow for action recognition from RGB-D data.
Previous works have considered the depth and RGB modalities as separate
channels and extract features for later fusion. We take a different approach
and consider the modalities as one entity, thus allowing feature extraction for
action recognition at the beginning. Two key questions about the use of scene
flow for action recognition are addressed: how to organize the scene flow
vectors and how to represent the long term dynamics of videos based on scene
flow. In order to calculate the scene flow correctly on the available datasets,
we propose an effective self-calibration method to align the RGB and depth data
spatially without knowledge of the camera parameters. Based on the scene flow
vectors, we propose a new representation, namely, Scene Flow to Action Map
(SFAM), that describes several long term spatio-temporal dynamics for action
recognition. We adopt a channel transform kernel to transform the scene flow
vectors to an optimal color space analogous to RGB. This transformation takes
better advantage of the trained ConvNets models over ImageNet. Experimental
results indicate that this new representation can surpass the performance of
state-of-the-art methods on two large public datasets.
Question Answering from Unstructured Text by Retrieval and Comprehension
Authors: Yusuke Watanabe, Bhuwan Dhingra, Ruslan Salakhutdinov
Date: 2017-03-26 00:00:00
Tasks: Articles, Open-Domain Question Answering, Question Answering, Retrieval

Open domain Question Answering (QA) systems must interact with external
knowledge sources, such as web pages, to find relevant information. Information
sources like Wikipedia, however, are not well structured and difficult to
utilize in comparison with Knowledge Bases (KBs). In this work we present a
two-step approach to question answering from unstructured text, consisting of a
retrieval step and a comprehension step. For comprehension, we present an RNN
based attention model with a novel mixture mechanism for selecting answers from
either retrieved articles or a fixed vocabulary. For retrieval we introduce a
hand-crafted model and a neural model for ranking relevant articles. We achieve
state-of-the-art performance on W IKI M OVIES dataset, reducing the error by
40%. Our experimental results further demonstrate the importance of each of the
introduced components.
Probabilistic Models for Computerized Adaptive Testing
Authors: Martin Plajner
Date: 2017-03-26 00:00:00

In this paper we follow our previous research in the area of Computerized
Adaptive Testing (CAT). We present three different methods for CAT. One of
them, the item response theory, is a well established method, while the other
two, Bayesian and neural networks, are new in the area of educational testing.
In the first part of this paper, we present the concept of CAT and its
advantages and disadvantages. We collected data from paper tests performed with
grammar school students. We provide the summary of data used for our
experiments in the second part. Next, we present three different model types
for CAT. They are based on the item response theory, Bayesian networks, and
neural networks. The general theory associated with each type is briefly
explained and the utilization of these models for CAT is analyzed. Future
research is outlined in the concluding part of the paper. It shows many
interesting research paths that are important not only for CAT but also for
other areas of artificial intelligence.
Recursive Autoconvolution for Unsupervised Learning of Convolutional Neural Networks
Authors: Boris Knyazev, Erhardt Barth, Thomas Martinetz
Date: 2016-06-02 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

In visual recognition tasks, such as image classification, unsupervised
learning exploits cheap unlabeled data and can help to solve these tasks more
efficiently. We show that the recursive autoconvolution operator, adopted from
physics, boosts existing unsupervised methods by learning more discriminative
filters. We take well established convolutional neural networks and train their
filters layer-wise. In addition, based on previous works we design a network
which extracts more than 600k features per sample, but with the total number of
trainable parameters greatly reduced by introducing shared filters in higher
layers. We evaluate our networks on the MNIST, CIFAR-10, CIFAR-100 and STL-10
image classification benchmarks and report several state of the art results
among other unsupervised methods.
Hadamard Product for Low-rank Bilinear Pooling
Authors: Jin-Hwa Kim, Kyoung-Woon On, Woosang Lim, Jeonghee Kim, Jung-Woo Ha, Byoung-Tak Zhang
Date: 2016-10-14 00:00:00
Tasks: Visual Question Answering, Visual Question Answering (VQA)

Bilinear models provide rich representations compared with linear models.
They have been applied in various visual tasks, such as object recognition,
segmentation, and visual question-answering, to get state-of-the-art
performances taking advantage of the expanded representations. However,
bilinear representations tend to be high-dimensional, limiting the
applicability to computationally complex tasks. We propose low-rank bilinear
pooling using Hadamard product for an efficient attention mechanism of
multimodal learning. We show that our model outperforms compact bilinear
pooling in visual question-answering tasks with the state-of-the-art results on
the VQA dataset, having a better parsimonious property.
Distributed Voting/Ranking with Optimal Number of States per Node
Authors: Saber Salehkaleybar, Arsalan Sharif-Nassab, S. Jamaloddin Golestani
Date: 2017-03-26 00:00:00
Tasks: Distributed Voting

Considering a network with $n$ nodes, where each node initially votes for one
(or more) choices out of $K$ possible choices, we present a Distributed
Multi-choice Voting/Ranking (DMVR) algorithm to determine either the choice
with maximum vote (the voting problem) or to rank all the choices in terms of
their acquired votes (the ranking problem). The algorithm consolidates node
votes across the network by updating the states of interacting nodes using two
key operations, the union and the intersection. The proposed algorithm is
simple, independent from network size, and easily scalable in terms of the
number of choices $K$, using only $K\times 2^{K-1}$ nodal states for voting,
and $K\times K!$ nodal states for ranking. We prove the number of states to be
optimal in the ranking case, this optimality is conjectured to also apply to
the voting case. The time complexity of the algorithm is analyzed in complete
graphs. We show that the time complexity for both ranking and voting is
$O(\log(n))$ for given vote percentages, and is inversely proportional to the
minimum of the vote percentage differences among various choices.
Person Re-Identification by Camera Correlation Aware Feature Augmentation
Authors: Ying-Cong Chen, Xiatian Zhu, Wei-Shi Zheng, Jian-Huang Lai
Date: 2017-03-26 00:00:00
Tasks: Person Re-Identification

The challenge of person re-identification (re-id) is to match individual
images of the same person captured by different non-overlapping camera views
against significant and unknown cross-view feature distortion. While a large
number of distance metric/subspace learning models have been developed for
re-id, the cross-view transformations they learned are view-generic and thus
potentially less effective in quantifying the feature distortion inherent to
each camera view. Learning view-specific feature transformations for re-id
(i.e., view-specific re-id), an under-studied approach, becomes an alternative
resort for this problem. In this work, we formulate a novel view-specific
person re-identification framework from the feature augmentation point of view,
called Camera coRrelation Aware Feature augmenTation (CRAFT). Specifically,
CRAFT performs cross-view adaptation by automatically measuring camera
correlation from cross-view visual data distribution and adaptively conducting
feature augmentation to transform the original features into a new adaptive
space. Through our augmentation framework, view-generic learning algorithms can
be readily generalized to learn and optimize view-specific sub-models whilst
simultaneously modelling view-generic discrimination information. Therefore,
our framework not only inherits the strength of view-generic model learning but
also provides an effective way to take into account view specific
characteristics. Our CRAFT framework can be extended to jointly learn
view-specific feature transformations for person re-id across a large network
with more than two cameras, a largely under-investigated but realistic re-id
setting. Additionally, we present a domain-generic deep person appearance
representation which is designed particularly to be towards view invariant for
facilitating cross-view adaptation by CRAFT.