pwc-archive/papers-with-abstracts huggingface dataset:
Monte-Carlo Tree Search by Best Arm Identification
Authors: Emilie Kaufmann, Wouter Koolen
Date: 2017-06-09 00:00:00
Proceeding: NeurIPS 2017 12

Recent advances in bandit tools and techniques for sequential learning are
steadily enabling new applications and are promising the resolution of a range
of challenging related problems. We study the game tree search problem, where
the goal is to quickly identify the optimal move in a given game tree by
sequentially sampling its stochastic payoffs. We develop new algorithms for
trees of arbitrary depth, that operate by summarizing all deeper levels of the
tree into confidence intervals at depth one, and applying a best arm
identification procedure at the root. We prove new sample complexity guarantees
with a refined dependence on the problem instance. We show experimentally that
our algorithms outperform existing elimination-based algorithms and match
previous special-purpose methods for depth-two trees.
A Spectral Algorithm with Additive Clustering for the Recovery of Overlapping Communities in Networks
Authors: Emilie Kaufmann, Thomas Bonald, Marc Lelarge
Date: 2015-06-12 00:00:00
Tasks: Clustering

This paper presents a novel spectral algorithm with additive clustering
designed to identify overlapping communities in networks. The algorithm is
based on geometric properties of the spectrum of the expected adjacency matrix
in a random graph model that we call stochastic blockmodel with overlap (SBMO).
An adaptive version of the algorithm, that does not require the knowledge of
the number of hidden communities, is proved to be consistent under the SBMO
when the degrees in the graph are (slightly more than) logarithmic. The
algorithm is shown to perform well on simulated data and on real-world graphs
with known overlapping communities.
Asymptotically Optimal Algorithms for Budgeted Multiple Play Bandits
Authors: Alexander Luedtke, Emilie Kaufmann, Antoine Chambaz
Date: 2016-06-30 00:00:00
Tasks: Thompson Sampling

We study a generalization of the multi-armed bandit problem with multiple plays where there is a cost associated with pulling each arm and the agent has a budget at each time that dictates how much she can expect to spend. We derive an asymptotic regret lower bound for any uniformly efficient algorithm in our setting. We then study a variant of Thompson sampling for Bernoulli rewards and a variant of KL-UCB for both single-parameter exponential families and bounded, finitely supported rewards. We show these algorithms are asymptotically optimal, both in rateand leading problem-dependent constants, including in the thick margin setting where multiple arms fall on the decision boundary.
Learning Solving Procedure for Artificial Neural Network
Authors: Ju-Hong Lee, Moon-Ju Kang, Bumghi Choi
Date: 2017-11-06 00:00:00
Tasks: Representation Learning

It is expected that progress toward true artificial intelligence will be
achieved through the emergence of a system that integrates representation
learning and complex reasoning (LeCun et al. 2015). In response to this
prediction, research has been conducted on implementing the symbolic reasoning
of a von Neumann computer in an artificial neural network (Graves et al. 2016;
Graves et al. 2014; Reed et al. 2015). However, these studies have many
limitations in realizing neural-symbolic integration (Jaeger. 2016). Here, we
present a new learning paradigm: a learning solving procedure (LSP) that learns
the procedure for solving complex problems. This is not accomplished merely by
learning input-output data, but by learning algorithms through a solving
procedure that obtains the output as a sequence of tasks for a given input
problem. The LSP neural network system not only learns simple problems of
addition and multiplication, but also the algorithms of complicated problems,
such as complex arithmetic expression, sorting, and Hanoi Tower. To realize
this, the LSP neural network structure consists of a deep neural network and
long short-term memory, which are recursively combined. Through
experimentation, we demonstrate the efficiency and scalability of LSP and its
validity as a mechanism of complex reasoning.
Object Detection Through Exploration With A Foveated Visual Field
Authors: Emre Akbas, Miguel P. Eckstein
Date: 2014-08-04 00:00:00
Tasks: Object, object-detection, Object Detection

We present a foveated object detector (FOD) as a biologically-inspired
alternative to the sliding window (SW) approach which is the dominant method of
search in computer vision object detection. Similar to the human visual system,
the FOD has higher resolution at the fovea and lower resolution at the visual
periphery. Consequently, more computational resources are allocated at the
fovea and relatively fewer at the periphery. The FOD processes the entire
scene, uses retino-specific object detection classifiers to guide eye
movements, aligns its fovea with regions of interest in the input image and
integrates observations across multiple fixations. Our approach combines modern
object detectors from computer vision with a recent model of peripheral pooling
regions found at the V1 layer of the human visual system. We assessed various
eye movement strategies on the PASCAL VOC 2007 dataset and show that the FOD
performs on par with the SW detector while bringing significant computational
cost savings.
Online Learning for Changing Environments using Coin Betting
Authors: Kwang-Sung Jun, Francesco Orabona, Stephen Wright, Rebecca Willett
Date: 2017-11-06 00:00:00
Tasks: Metric Learning

A key challenge in online learning is that classical algorithms can be slow
to adapt to changing environments. Recent studies have proposed "meta"
algorithms that convert any online learning algorithm to one that is adaptive
to changing environments, where the adaptivity is analyzed in a quantity called
the strongly-adaptive regret. This paper describes a new meta algorithm that
has a strongly-adaptive regret bound that is a factor of $\sqrt{\log(T)}$
better than other algorithms with the same time complexity, where $T$ is the
time horizon. We also extend our algorithm to achieve a first-order (i.e.,
dependent on the observed losses) strongly-adaptive regret bound for the first
time, to our knowledge. At its heart is a new parameter-free algorithm for the
learning with expert advice (LEA) problem in which experts sometimes do not
output advice for consecutive time steps (i.e., \emph{sleeping} experts). This
algorithm is derived by a reduction from optimal algorithms for the so-called
coin betting problem. Empirical results show that our algorithm outperforms
state-of-the-art methods in both learning with expert advice and metric
learning scenarios.
High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks
Authors: Krzysztof J. Geras, Stacey Wolfson, Yiqiu Shen, Nan Wu, S. Gene Kim, Eric Kim, Laura Heacock, Ujas Parikh, Linda Moy, Kyunghyun Cho
Date: 2017-03-21 00:00:00
Tasks: Breast Cancer Detection, Medical Diagnosis

Advances in deep learning for natural images have prompted a surge of
interest in applying similar techniques to medical images. The majority of the
initial attempts focused on replacing the input of a deep convolutional neural
network with a medical image, which does not take into consideration the
fundamental differences between these two types of images. Specifically, fine
details are necessary for detection in medical images, unlike in natural images
where coarse structures matter most. This difference makes it inadequate to use
the existing network architectures developed for natural images, because they
work on heavily downscaled images to reduce the memory requirements. This hides
details necessary to make accurate predictions. Additionally, a single exam in
medical imaging often comes with a set of views which must be fused in order to
reach a correct conclusion. In our work, we propose to use a multi-view deep
convolutional neural network that handles a set of high-resolution medical
images. We evaluate it on large-scale mammography-based breast cancer screening
(BI-RADS prediction) using 886,000 images. We focus on investigating the impact
of the training set size and image size on the prediction accuracy. Our results
highlight that performance increases with the size of training set, and that
the best performance can only be achieved using the original resolution. In the
reader study, performed on a random subset of the test set, we confirmed the
efficacy of our model, which achieved performance comparable to a committee of
radiologists when presented with the same data.
KGAN: How to Break The Minimax Game in GAN
Authors: Trung Le, Tu Dinh Nguyen, Dinh Phung
Date: 2017-11-06 00:00:00
Tasks: General Classification

Generative Adversarial Networks (GANs) were intuitively and attractively
explained under the perspective of game theory, wherein two involving parties
are a discriminator and a generator. In this game, the task of the
discriminator is to discriminate the real and generated (i.e., fake) data,
whilst the task of the generator is to generate the fake data that maximally
confuses the discriminator. In this paper, we propose a new viewpoint for GANs,
which is termed as the minimizing general loss viewpoint. This viewpoint shows
a connection between the general loss of a classification problem regarding a
convex loss function and a f-divergence between the true and fake data
distributions. Mathematically, we proposed a setting for the classification
problem of the true and fake data, wherein we can prove that the general loss
of this classification problem is exactly the negative f-divergence for a
certain convex function f. This allows us to interpret the problem of learning
the generator for dismissing the f-divergence between the true and fake data
distributions as that of maximizing the general loss which is equivalent to the
min-max problem in GAN if the Logistic loss is used in the classification
problem. However, this viewpoint strengthens GANs in two ways. First, it allows
us to employ any convex loss function for the discriminator. Second, it
suggests that rather than limiting ourselves in NN-based discriminators, we can
alternatively utilize other powerful families. Bearing this viewpoint, we then
propose using the kernel-based family for discriminators. This family has two
appealing features: i) a powerful capacity in classifying non-linear nature
data and ii) being convex in the feature space. Using the convexity of this
family, we can further develop Fenchel duality to equivalently transform the
max-min problem to the max-max dual problem.
Structure Regularized Bidirectional Recurrent Convolutional Neural Network for Relation Classification
Authors: Ji Wen
Date: 2017-11-06 00:00:00
Tasks: Classification, General Classification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Relation, Relation Classification, Sentence

Relation classification is an important semantic processing task in the field
of natural language processing (NLP). In this paper, we present a novel model,
Structure Regularized Bidirectional Recurrent Convolutional Neural
Network(SR-BRCNN), to classify the relation of two entities in a sentence, and
the new dataset of Chinese Sanwen for named entity recognition and relation
classification. Some state-of-the-art systems concentrate on modeling the
shortest dependency path (SDP) between two entities leveraging convolutional or
recurrent neural networks. We further explore how to make full use of the
dependency relations information in the SDP and how to improve the model by the
method of structure regularization. We propose a structure regularized model to
learn relation representations along the SDP extracted from the forest formed
by the structure regularized dependency tree, which benefits reducing the
complexity of the whole model and helps improve the $F_{1}$ score by 10.3.
Experimental results show that our method outperforms the state-of-the-art
approaches on the Chinese Sanwen task and performs as well on the SemEval-2010
Task 8 dataset\footnote{The Chinese Sanwen corpus this paper developed and used
will be released in the further.
From Facial Expression Recognition to Interpersonal Relation Prediction
Authors: Zhanpeng Zhang, Ping Luo, Chen Change Loy, Xiaoou Tang
Date: 2016-09-21 00:00:00
Tasks: Attribute, Facial Expression Recognition, Facial Expression Recognition (FER), Prediction, Relation, Relation Prediction

Interpersonal relation defines the association, e.g., warm, friendliness, and
dominance, between two or more people. Motivated by psychological studies, we
investigate if such fine-grained and high-level relation traits can be
characterized and quantified from face images in the wild. We address this
challenging problem by first studying a deep network architecture for robust
recognition of facial expressions. Unlike existing models that typically learn
from facial expression labels alone, we devise an effective multitask network
that is capable of learning from rich auxiliary attributes such as gender, age,
and head pose, beyond just facial expression data. While conventional
supervised training requires datasets with complete labels (e.g., all samples
must be labeled with gender, age, and expression), we show that this
requirement can be relaxed via a novel attribute propagation method. The
approach further allows us to leverage the inherent correspondences between
heterogeneous attribute sources despite the disparate distributions of
different datasets. With the network we demonstrate state-of-the-art results on
existing facial expression recognition benchmarks. To predict inter-personal
relation, we use the expression recognition network as branches for a Siamese
model. Extensive experiments show that our model is capable of mining mutual
context of faces for accurate fine-grained interpersonal prediction.
Active Learning for Visual Question Answering: An Empirical Study
Authors: Xiao Lin, Devi Parikh
Date: 2017-11-06 00:00:00
Tasks: Active Learning, Visual Question Answering, Visual Question Answering (VQA)

We present an empirical study of active learning for Visual Question
Answering, where a deep VQA model selects informative question-image pairs from
a pool and queries an oracle for answers to maximally improve its performance
under a limited query budget. Drawing analogies from human learning, we explore
cramming (entropy), curiosity-driven (expected model change), and goal-driven
(expected error reduction) active learning approaches, and propose a fast and
effective goal-driven active learning scoring function to pick question-image
pairs for deep VQA models under the Bayesian Neural Network framework. We find
that deep VQA models need large amounts of training data before they can start
asking informative questions. But once they do, all three approaches outperform
the random selection baseline and achieve significant query savings. For the
scenario where the model is allowed to ask generic questions about images but
is evaluated only on specific questions (e.g., questions whose answer is either
yes or no), our proposed goal-driven scoring function performs the best.
Magnetic-Visual Sensor Fusion based Medical SLAM for Endoscopic Capsule Robot
Authors: Mehmet Turan, Yasin Almalioglu, Hunter Gilbert, Helder Araujo, Ender Konukoglu, Metin Sitti
Date: 2017-05-17 00:00:00
Tasks: Diagnostic, Sensor Fusion, Simultaneous Localization and Mapping, Surface Reconstruction

A reliable, real-time simultaneous localization and mapping (SLAM) method is
crucial for the navigation of actively controlled capsule endoscopy robots.
These robots are an emerging, minimally invasive diagnostic and therapeutic
technology for use in the gastrointestinal (GI) tract. In this study, we
propose a dense, non-rigidly deformable, and real-time map fusion approach for
actively controlled endoscopic capsule robot applications. The method combines
magnetic and vision based localization, and makes use of frame-to-model fusion
and model-to-model loop closure. The performance of the method is demonstrated
using an ex-vivo porcine stomach model. Across four trajectories of varying
speed and complexity, and across three cameras, the root mean square
localization errors range from 0.42 to 1.92 cm, and the root mean square
surface reconstruction errors range from 1.23 to 2.39 cm.
End-to-End Video Classification with Knowledge Graphs
Authors: Fang Yuan, Zhe Wang, Jie Lin, Luis Fernando D'Haro, Kim Jung Jae, Zeng Zeng, Vijay Chandrasekhar
Date: 2017-11-06 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, Knowledge Graphs, Video Classification, Video Understanding

Video understanding has attracted much research attention especially since
the recent availability of large-scale video benchmarks. In this paper, we
address the problem of multi-label video classification. We first observe that
there exists a significant knowledge gap between how machines and humans learn.
That is, while current machine learning approaches including deep neural
networks largely focus on the representations of the given data, humans often
look beyond the data at hand and leverage external knowledge to make better
decisions. Towards narrowing the gap, we propose to incorporate external
knowledge graphs into video classification. In particular, we unify traditional
"knowledgeless" machine learning models and knowledge graphs in a novel
end-to-end framework. The framework is flexible to work with most existing
video classification algorithms including state-of-the-art deep models.
Finally, we conduct extensive experiments on the largest public video dataset
YouTube-8M. The results are promising across the board, improving mean average
precision by up to 2.9%.
RoboCupSimData: A RoboCup soccer research dataset
Authors: Olivia Michael, Oliver Obst, Falk Schmidsberger, Frieder Stolzenburg
Date: 2017-11-06 00:00:00

RoboCup is an international scientific robot competition in which teams of
multiple robots compete against each other. Its different leagues provide many
sources of robotics data, that can be used for further analysis and application
of machine learning. This paper describes a large dataset from games of some of
the top teams (from 2016 and 2017) in RoboCup Soccer Simulation League (2D),
where teams of 11 robots (agents) compete against each other. Overall, we used
10 different teams to play each other, resulting in 45 unique pairings. For
each pairing, we ran 25 matches (of 10mins), leading to 1125 matches or more
than 180 hours of game play. The generated CSV files are 17GB of data (zipped),
or 229GB (unzipped). The dataset is unique in the sense that it contains both
the ground truth data (global, complete, noise-free information of all objects
on the field), as well as the noisy, local and incomplete percepts of each
robot. These data are made available as CSV files, as well as in the original
soccer simulator formats.
Distributed Representation for Traditional Chinese Medicine Herb via Deep Learning Models
Authors: Wei Li, Zheng Yang
Date: 2017-11-06 00:00:00
Tasks: Language Modeling, Language Modelling

Traditional Chinese Medicine (TCM) has accumulated a big amount of precious
resource in the long history of development. TCM prescriptions that consist of
TCM herbs are an important form of TCM treatment, which are similar to natural
language documents, but in a weakly ordered fashion. Directly adapting language
modeling style methods to learn the embeddings of the herbs can be problematic
as the herbs are not strictly in order, the herbs in the front of the
prescription can be connected to the very last ones. In this paper, we propose
to represent TCM herbs with distributed representations via Prescription Level
Language Modeling (PLLM). In one of our experiments, the correlation between
our calculated similarity between medicines and the judgment of professionals
achieves a Spearman score of 55.35 indicating a strong correlation, which
surpasses human beginners (TCM related field bachelor student) by a big margin
(over 10%).
Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo
Authors: Rong Ge, Holden Lee, Andrej Risteski
Date: 2017-10-07 00:00:00
Proceeding: NeurIPS 2018 12

A key task in Bayesian statistics is sampling from distributions that are
only specified up to a partition function (i.e., constant of proportionality).
However, without any assumptions, sampling (even approximately) can be #P-hard,
and few works have provided "beyond worst-case" guarantees for such settings.
  For log-concave distributions, classical results going back to Bakry and
\'Emery (1985) show that natural continuous-time Markov chains called Langevin
diffusions mix in polynomial time. The most salient feature of log-concavity
violated in practice is uni-modality: commonly, the distributions we wish to
sample from are multi-modal. In the presence of multiple deep and
well-separated modes, Langevin diffusion suffers from torpid mixing.
  We address this problem by combining Langevin diffusion with simulated
tempering. The result is a Markov chain that mixes more rapidly by
transitioning between different temperatures of the distribution. We analyze
this Markov chain for the canonical multi-modal distribution: a mixture of
gaussians (of equal variance). The algorithm based on our Markov chain provably
samples from distributions that are close to mixtures of gaussians, given
access to the gradient of the log-pdf. For the analysis, we use a spectral
decomposition theorem for graphs (Gharan and Trevisan, 2014) and a Markov chain
decomposition technique (Madras and Randall, 2002).
A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment
Authors: Rafael Reisenhofer, Sebastian Bosse, Gitta Kutyniok, Thomas Wiegand
Date: 2016-07-20 00:00:00
Tasks: Denoising, Full reference image quality assessment, Full-Reference Image Quality Assessment, Image Quality Assessment, SSIM, Video Restoration

In most practical situations, the compression or transmission of images and
videos creates distortions that will eventually be perceived by a human
observer. Vice versa, image and video restoration techniques, such as
inpainting or denoising, aim to enhance the quality of experience of human
viewers. Correctly assessing the similarity between an image and an undistorted
reference image as subjectively experienced by a human viewer can thus lead to
significant improvements in any transmission, compression, or restoration
system. This paper introduces the Haar wavelet-based perceptual similarity
index (HaarPSI), a novel and computationally inexpensive similarity measure for
full reference image quality assessment. The HaarPSI utilizes the coefficients
obtained from a Haar wavelet decomposition to assess local similarities between
two images, as well as the relative importance of image areas. The consistency
of the HaarPSI with the human quality of experience was validated on four large
benchmark databases containing thousands of differently distorted images. On
these databases, the HaarPSI achieves higher correlations with human opinion
scores than state-of-the-art full reference similarity measures like the
structural similarity index (SSIM), the feature similarity index (FSIM), and
the visual saliency-based index (VSI). Along with the simple computational
structure and the short execution time, these experimental results suggest a
high applicability of the HaarPSI in real world tasks.