pwc-archive/papers-with-abstracts huggingface dataset:
Learning Fine-grained Features via a CNN Tree for Large-scale Classification
Authors: Zhenhua Wang, Xingxing Wang, Gang Wang
Date: 2015-11-14 00:00:00
Tasks: General Classification, image-classification, Image Classification

We propose a novel approach to enhance the discriminability of Convolutional
Neural Networks (CNN). The key idea is to build a tree structure that could
progressively learn fine-grained features to distinguish a subset of classes,
by learning features only among these classes. Such features are expected to be
more discriminative, compared to features learned for all the classes. We
develop a new algorithm to effectively learn the tree structure from a large
number of classes. Experiments on large-scale image classification tasks
demonstrate that our method could boost the performance of a given basic CNN
model. Our method is quite general, hence it can potentially be used in
combination with many other deep learning models.
Total stability of kernel methods
Authors: Andreas Christmann, Dao-Hong Xiang, Ding-Xuan Zhou
Date: 2017-09-22 00:00:00

Regularized empirical risk minimization using kernels and their corresponding
reproducing kernel Hilbert spaces (RKHSs) plays an important role in machine
learning. However, the actually used kernel often depends on one or on a few
hyperparameters or the kernel is even data dependent in a much more complicated
manner. Examples are Gaussian RBF kernels, kernel learning, and hierarchical
Gaussian kernels which were recently proposed for deep learning. Therefore, the
actually used kernel is often computed by a grid search or in an iterative
manner and can often only be considered as an approximation to the "ideal" or
"optimal" kernel. The paper gives conditions under which classical kernel based
methods based on a convex Lipschitz loss function and on a bounded and smooth
kernel are stable, if the probability measure $P$, the regularization parameter
$\lambda$, and the kernel $k$ may slightly change in a simultaneous manner.
Similar results are also given for pairwise learning. Therefore, the topic of
this paper is somewhat more general than in classical robust statistics, where
usually only the influence of small perturbations of the probability measure
$P$ on the estimated function is considered.
Tight Bounds on $\ell_1$ Approximation and Learning of Self-Bounding Functions
Authors: Vitaly Feldman, Pravesh Kothari, Jan Vondrák
Date: 2014-04-18 00:00:00

We study the complexity of learning and approximation of self-bounding functions over the uniform distribution on the Boolean hypercube ${0,1}^n$. Informally, a function $f:{0,1}^n \rightarrow \mathbb{R}$ is self-bounding if for every $x \in {0,1}^n$, $f(x)$ upper bounds the sum of all the $n$ marginal decreases in the value of the function at $x$. Self-bounding functions include such well-known classes of functions as submodular and fractionally-subadditive (XOS) functions. They were introduced by Boucheron et al. (2000) in the context of concentration of measure inequalities. Our main result is a nearly tight $\ell_1$-approximation of self-bounding functions by low-degree juntas. Specifically, all self-bounding functions can be $\epsilon$-approximated in $\ell_1$ by a polynomial of degree $\tilde{O}(1/\epsilon)$ over $2^{\tilde{O}(1/\epsilon)}$ variables. We show that both the degree and junta-size are optimal up to logarithmic terms. Previous techniques considered stronger $\ell_2$ approximation and proved nearly tight bounds of $\Theta(1/\epsilon^{2})$ on the degree and $2^{\Theta(1/\epsilon^2)}$ on the number of variables. Our bounds rely on the analysis of noise stability of self-bounding functions together with a stronger connection between noise stability and $\ell_1$ approximation by low-degree polynomials. This technique can also be used to get tighter bounds on $\ell_1$ approximation by low-degree polynomials and faster learning algorithm for halfspaces. These results lead to improved and in several cases almost tight bounds for PAC and agnostic learning of self-bounding functions relative to the uniform distribution. In particular, assuming hardness of learning juntas, we show that PAC and agnostic learning of self-bounding functions have complexity of $n^{\tilde{\Theta}(1/\epsilon)}$.
Confident Multiple Choice Learning
Authors: Kimin Lee, Changho Hwang, Kyoungsoo Park, Jinwoo Shin
Date: 2017-06-12 00:00:00
Proceeding: ICML 2017 8
Tasks: General Classification, image-classification, Image Classification, Multiple-choice

Ensemble methods are arguably the most trustworthy techniques for boosting
the performance of machine learning models. Popular independent ensembles (IE)
relying on naive averaging/voting scheme have been of typical choice for most
applications involving deep neural networks, but they do not consider advanced
collaboration among ensemble models. In this paper, we propose new ensemble
methods specialized for deep neural networks, called confident multiple choice
learning (CMCL): it is a variant of multiple choice learning (MCL) via
addressing its overconfidence issue.In particular, the proposed major
components of CMCL beyond the original MCL scheme are (i) new loss, i.e.,
confident oracle loss, (ii) new architecture, i.e., feature sharing and (iii)
new training method, i.e., stochastic labeling. We demonstrate the effect of
CMCL via experiments on the image classification on CIFAR and SVHN, and the
foreground-background segmentation on the iCoseg. In particular, CMCL using 5
residual networks provides 14.05% and 6.60% relative reductions in the top-1
error rates from the corresponding IE scheme for the classification task on
CIFAR and SVHN, respectively.
High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference
Authors: Xiaoguang Han, Zhen Li, Haibin Huang, Evangelos Kalogerakis, Yizhou Yu
Date: 2017-09-22 00:00:00
Proceeding: ICCV 2017 10
Tasks: Decoder

We propose a data-driven method for recovering miss-ing parts of 3D shapes.
Our method is based on a new deep learning architecture consisting of two
sub-networks: a global structure inference network and a local geometry
refinement network. The global structure inference network incorporates a long
short-term memorized context fusion module (LSTM-CF) that infers the global
structure of the shape based on multi-view depth information provided as part
of the input. It also includes a 3D fully convolutional (3DFCN) module that
further enriches the global structure representation according to volumetric
information in the input. Under the guidance of the global structure network,
the local geometry refinement network takes as input lo-cal 3D patches around
missing regions, and progressively produces a high-resolution, complete surface
through a volumetric encoder-decoder architecture. Our method jointly trains
the global structure inference and local geometry refinement networks in an
end-to-end manner. We perform qualitative and quantitative evaluations on six
object categories, demonstrating that our method outperforms existing
state-of-the-art work on shape completion.
Demography-based Facial Retouching Detection using Subclass Supervised Sparse Autoencoder
Authors: Aparna Bharati, Mayank Vatsa, Richa Singh, Kevin W. Bowyer, Xin Tong
Date: 2017-09-22 00:00:00

Digital retouching of face images is becoming more widespread due to the
introduction of software packages that automate the task. Several researchers
have introduced algorithms to detect whether a face image is original or
retouched. However, previous work on this topic has not considered whether or
how accuracy of retouching detection varies with the demography of face images.
In this paper, we introduce a new Multi-Demographic Retouched Faces (MDRF)
dataset, which contains images belonging to two genders, male and female, and
three ethnicities, Indian, Chinese, and Caucasian. Further, retouched images
are created using two different retouching software packages. The second major
contribution of this research is a novel semi-supervised autoencoder
incorporating "subclass" information to improve classification. The proposed
approach outperforms existing state-of-the-art detection algorithms for the
task of generalized retouching detection. Experiments conducted with multiple
combinations of ethnicities show that accuracy of retouching detection can vary
greatly based on the demographics of the training and testing images.
Inverse Reinforcement Learning with Conditional Choice Probabilities
Authors: Mohit Sharma, Kris M. Kitani, Joachim Groeger
Date: 2017-09-22 00:00:00
Tasks: Econometrics, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We make an important connection to existing results in econometrics to
describe an alternative formulation of inverse reinforcement learning (IRL). In
particular, we describe an algorithm using Conditional Choice Probabilities
(CCP), which are maximum likelihood estimates of the policy estimated from
expert demonstrations, to solve the IRL problem. Using the language of
structural econometrics, we re-frame the optimal decision problem and introduce
an alternative representation of value functions due to (Hotz and Miller 1993).
In addition to presenting the theoretical connections that bridge the IRL
literature between Economics and Robotics, the use of CCPs also has the
practical benefit of reducing the computational cost of solving the IRL
problem. Specifically, under the CCP representation, we show how one can avoid
repeated calls to the dynamic programming subroutine typically used in IRL. We
show via extensive experimentation on standard IRL benchmarks that CCP-IRL is
able to outperform MaxEnt-IRL, with as much as a 5x speedup and without
compromising on the quality of the recovered reward function.
Happy Travelers Take Big Pictures: A Psychological Study with Machine Learning and Big Data
Authors: Xuefeng Liang, Lixin Fan, Yuen Peng Loh, Yang Liu, Song Tong
Date: 2017-09-22 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning

In psychology, theory-driven researches are usually conducted with extensive
laboratory experiments, yet rarely tested or disproved with big data. In this
paper, we make use of 418K travel photos with traveler ratings to test the
influential "broaden-and-build" theory, that suggests positive emotions broaden
one's visual attention. The core hypothesis examined in this study is that
positive emotion is associated with a wider attention, hence highly-rated sites
would trigger wide-angle photographs. By analyzing travel photos, we find a
strong correlation between a preference for wide-angle photos and the high
rating of tourist sites on TripAdvisor. We are able to carry out this analysis
through the use of deep learning algorithms to classify the photos into wide
and narrow angles, and present this study as an exemplar of how big data and
deep learning can be used to test laboratory findings in the wild.
Hierarchical Detail Enhancing Mesh-Based Shape Generation with 3D Generative Adversarial Network
Authors: Chiyu "Max" Jiang, Philip Marcus
Date: 2017-09-22 00:00:00
Tasks: Deep Learning, Generative Adversarial Network

Automatic mesh-based shape generation is of great interest across a wide
range of disciplines, from industrial design to gaming, computer graphics and
various other forms of digital art. While most traditional methods focus on
primitive based model generation, advances in deep learning made it possible to
learn 3-dimensional geometric shape representations in an end-to-end manner.
However, most current deep learning based frameworks focus on the
representation and generation of voxel and point-cloud based shapes, making it
not directly applicable to design and graphics communities. This study
addresses the needs for automatic generation of mesh-based geometries, and
propose a novel framework that utilizes signed distance function representation
that generates detail preserving three-dimensional surface mesh by a deep
learning based approach.
EB-GLS: An Improved Guided Local Search Based on the Big Valley Structure
Authors: Jialong Shi, Qingfu Zhang, Edward Tsang
Date: 2017-09-22 00:00:00
Tasks: Combinatorial Optimization, Traveling Salesman Problem

Local search is a basic building block in memetic algorithms. Guided Local
Search (GLS) can improve the efficiency of local search. By changing the guide
function, GLS guides a local search to escape from locally optimal solutions
and find better solutions. The key component of GLS is its penalizing mechanism
which determines which feature is selected to penalize when the search is
trapped in a locally optimal solution. The original GLS penalizing mechanism
only makes use of the cost and the current penalty value of each feature. It is
well known that many combinatorial optimization problems have a big valley
structure, i.e., the better a solution is, the more the chance it is closer to
a globally optimal solution. This paper proposes to use big valley structure
assumption to improve the GLS penalizing mechanism. An improved GLS algorithm
called Elite Biased GLS (EB-GLS) is proposed. EB-GLS records and maintains an
elite solution as an estimate of the globally optimal solutions, and reduces
the chance of penalizing the features in this solution. We have systematically
tested the proposed algorithm on the symmetric traveling salesman problem.
Experimental results show that EB-GLS is significantly better than GLS.
Goal-Driven Dynamics Learning via Bayesian Optimization
Authors: Somil Bansal, Roberto Calandra, Ted Xiao, Sergey Levine, Claire J. Tomlin
Date: 2017-03-27 00:00:00
Tasks: Active Learning, Bayesian Optimization

Real-world robots are becoming increasingly complex and commonly act in
poorly understood environments where it is extremely challenging to model or
learn their true dynamics. Therefore, it might be desirable to take a
task-specific approach, wherein the focus is on explicitly learning the
dynamics model which achieves the best control performance for the task at
hand, rather than learning the true dynamics. In this work, we use Bayesian
optimization in an active learning framework where a locally linear dynamics
model is learned with the intent of maximizing the control performance, and
used in conjunction with optimal control schemes to efficiently design a
controller for a given task. This model is updated directly based on the
performance observed in experiments on the physical system in an iterative
manner until a desired performance is achieved. We demonstrate the efficacy of
the proposed approach through simulations and real experiments on a quadrotor
testbed.
Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank
Authors: Liang Zhao, Siyu Liao, Yanzhi Wang, Zhe Li, Jian Tang, Victor Pan, Bo Yuan
Date: 2017-03-01 00:00:00
Proceeding: ICML 2017 8

Recently low displacement rank (LDR) matrices, or so-called structured
matrices, have been proposed to compress large-scale neural networks. Empirical
results have shown that neural networks with weight matrices of LDR matrices,
referred as LDR neural networks, can achieve significant reduction in space and
computational complexity while retaining high accuracy. We formally study LDR
matrices in deep learning. First, we prove the universal approximation property
of LDR neural networks with a mild condition on the displacement operators. We
then show that the error bounds of LDR neural networks are as efficient as
general neural networks with both single-layer and multiple-layer structure.
Finally, we propose back-propagation based training algorithm for general LDR
neural networks.
Smart Mirror: Intelligent Makeup Recommendation and Synthesis
Authors: Tam V. Nguyen, Luoqi Liu
Date: 2017-09-22 00:00:00

The female facial image beautification usually requires professional editing
softwares, which are relatively difficult for common users. In this demo, we
introduce a practical system for automatic and personalized facial makeup
recommendation and synthesis. First, a model describing the relations among
facial features, facial attributes and makeup attributes is learned as the
makeup recommendation model for suggesting the most suitable makeup attributes.
Then the recommended makeup attributes are seamlessly synthesized onto the
input facial image.
Novel Evaluation Metrics for Seam Carving based Image Retargeting
Authors: Tam V. Nguyen, Guangyu Gao
Date: 2017-09-22 00:00:00
Tasks: Image Retargeting

Image retargeting effectively resizes images by preserving the
recognizability of important image regions. Most of retargeting methods rely on
good importance maps as a cue to retain or remove certain regions in the input
image. In addition, the traditional evaluation exhaustively depends on user
ratings. There is a legitimate need for a methodological approach for
evaluating retargeted results. Therefore, in this paper, we conduct a study and
analysis on the prominent method in image retargeting, Seam Carving. First, we
introduce two novel evaluation metrics which can be considered as the proxy of
user ratings. Second, we exploit salient object dataset as a benchmark for this
task. We then investigate different types of importance maps for this
particular problem. The experiments show that humans in general agree with the
evaluation metrics on the retargeted results and some importance map methods
are consistently more favorable than others.
Deep Reinforcement Learning for High Precision Assembly Tasks
Authors: Tadanobu Inoue, Giovanni De Magistris, Asim Munawar, Tsuyoshi Yokoya, Ryuki Tachibana
Date: 2017-08-14 00:00:00
Tasks: Deep Reinforcement Learning, Position, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Vocal Bursts Intensity Prediction

High precision assembly of mechanical parts requires accuracy exceeding the
robot precision. Conventional part mating methods used in the current
manufacturing requires tedious tuning of numerous parameters before deployment.
We show how the robot can successfully perform a tight clearance peg-in-hole
task through training a recurrent neural network with reinforcement learning.
In addition to saving the manual effort, the proposed technique also shows
robustness against position and angle errors for the peg-in-hole task. The
neural network learns to take the optimal action by observing the robot sensors
to estimate the system state. The advantages of our proposed method is
validated experimentally on a 7-axis articulated robot arm.
Virtual Blood Vessels in Complex Background using Stereo X-ray Images
Authors: Qiuyu Chen, Ryoma Bise, Lin Gu, Yinqiang Zheng, Imari Sato, Jenq-Neng Hwang, Nobuaki Imanishi, Sadakazu Aiso
Date: 2017-09-22 00:00:00
Tasks: Stereo Matching, Stereo Matching Hand

We propose a fully automatic system to reconstruct and visualize 3D blood
vessels in Augmented Reality (AR) system from stereo X-ray images with bones
and body fat. Currently, typical 3D imaging technologies are expensive and
carrying the risk of irradiation exposure. To reduce the potential harm, we
only need to take two X-ray images before visualizing the vessels. Our system
can effectively reconstruct and visualize vessels in following steps. We first
conduct initial segmentation using Markov Random Field and then refine
segmentation in an entropy based post-process. We parse the segmented vessels
by extracting their centerlines and generating trees. We propose a
coarse-to-fine scheme for stereo matching, including initial matching using
affine transform and dense matching using Hungarian algorithm guided by
Gaussian regression. Finally, we render and visualize the reconstructed model
in a HoloLens based AR system, which can essentially change the way of
visualizing medical data. We have evaluated its performance by using synthetic
and real stereo X-ray images, and achieved satisfactory quantitative and
qualitative results.
Attention-based Mixture Density Recurrent Networks for History-based Recommendation
Authors: Tian Wang, Kyunghyun Cho
Date: 2017-09-22 00:00:00

The goal of personalized history-based recommendation is to automatically
output a distribution over all the items given a sequence of previous purchases
of a user. In this work, we present a novel approach that uses a recurrent
network for summarizing the history of purchases, continuous vectors
representing items for scalability, and a novel attention-based recurrent
mixture density network, which outputs each component in a mixture
sequentially, for modelling a multi-modal conditional distribution. We evaluate
the proposed approach on two publicly available datasets, MovieLens-20M and
RecSys15. The experiments show that the proposed approach, which explicitly
models the multi-modal nature of the predictive distribution, is able to
improve the performance over various baselines in terms of precision, recall
and nDCG.
Predicting Cardiovascular Risk Factors from Retinal Fundus Photographs using Deep Learning
Authors: Ryan Poplin, Avinash V. Varadarajan, Katy Blumer, Yun Liu, Michael V. McConnell, Greg S. Corrado, Lily Peng, Dale R. Webster
Date: 2017-08-31 00:00:00
Tasks: Anatomy

Traditionally, medical discoveries are made by observing associations and
then designing experiments to test these hypotheses. However, observing and
quantifying associations in images can be difficult because of the wide variety
of features, patterns, colors, values, shapes in real data. In this paper, we
use deep learning, a machine learning technique that learns its own features,
to discover new knowledge from retinal fundus images. Using models trained on
data from 284,335 patients, and validated on two independent datasets of 12,026
and 999 patients, we predict cardiovascular risk factors not previously thought
to be present or quantifiable in retinal images, such as such as age (within
3.26 years), gender (0.97 AUC), smoking status (0.71 AUC), HbA1c (within
1.39%), systolic blood pressure (within 11.23mmHg) as well as major adverse
cardiac events (0.70 AUC). We further show that our models used distinct
aspects of the anatomy to generate each prediction, such as the optic disc or
blood vessels, opening avenues of further research.
MRNet-Product2Vec: A Multi-task Recurrent Neural Network for Product Embeddings
Authors: Arijit Biswas, Mukul Bhutani, Subhajit Sanyal
Date: 2017-09-21 00:00:00

E-commerce websites such as Amazon, Alibaba, Flipkart, and Walmart sell
billions of products. Machine learning (ML) algorithms involving products are
often used to improve the customer experience and increase revenue, e.g.,
product similarity, recommendation, and price estimation. The products are
required to be represented as features before training an ML algorithm. In this
paper, we propose an approach called MRNet-Product2Vec for creating generic
embeddings of products within an e-commerce ecosystem. We learn a dense and
low-dimensional embedding where a diverse set of signals related to a product
are explicitly injected into its representation. We train a Discriminative
Multi-task Bidirectional Recurrent Neural Network (RNN), where the input is a
product title fed through a Bidirectional RNN and at the output, product labels
corresponding to fifteen different tasks are predicted. The task set includes
several intrinsic characteristics about a product such as price, weight, size,
color, popularity, and material. We evaluate the proposed embedding
quantitatively and qualitatively. We demonstrate that they are almost as good
as sparse and extremely high-dimensional TF-IDF representation in spite of
having less than 3% of the TF-IDF dimension. We also use a multimodal
autoencoder for comparing products from different language-regions and show
preliminary yet promising qualitative results.