pwc-archive/papers-with-abstracts huggingface dataset:
Molecular Generation with Recurrent Neural Networks (RNNs)
Authors: Esben Jannik Bjerrum, Richard Threlfall
Date: 2017-05-12 00:00:00
Tasks: Drug Discovery

The potential number of drug like small molecules is estimated to be between
10^23 and 10^60 while current databases of known compounds are orders of
magnitude smaller with approximately 10^8 compounds. This discrepancy has led
to an interest in generating virtual libraries using hand crafted chemical
rules and fragment based methods to cover a larger area of chemical space and
generate chemical libraries for use in in silico drug discovery endeavors. Here
it is explored to what extent a recurrent neural network with long short term
memory cells can figure out sensible chemical rules and generate synthesizable
molecules by being trained on existing compounds encoded as SMILES. The
networks can to a high extent generate novel, but chemically sensible
molecules. The properties of the molecules are tuned by training on two
different datasets consisting of fragment like molecules and drug like
molecules. The produced molecules and the training databases have very similar
distributions of molar weight, predicted logP, number of hydrogen bond
acceptors and donors, number of rotatable bonds and topological polar surface
area when compared to their respective training sets. The compounds are for the
most cases synthesizable as assessed with SA score and Wiley ChemPlanner.
Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems
Authors: Maria-Florina Balcan, Vaishnavh Nagarajan, Ellen Vitercik, Colin White
Date: 2016-11-14 00:00:00
Tasks: Clustering, Learning Theory

Max-cut, clustering, and many other partitioning problems that are of
significant importance to machine learning and other scientific fields are
NP-hard, a reality that has motivated researchers to develop a wealth of
approximation algorithms and heuristics. Although the best algorithm to use
typically depends on the specific application domain, a worst-case analysis is
often used to compare algorithms. This may be misleading if worst-case
instances occur infrequently, and thus there is a demand for optimization
methods which return the algorithm configuration best suited for the given
application's typical inputs. We address this problem for clustering, max-cut,
and other partitioning problems, such as integer quadratic programming, by
designing computationally efficient and sample efficient learning algorithms
which receive samples from an application-specific distribution over problem
instances and learn a partitioning algorithm with high expected performance.
Our algorithms learn over common integer quadratic programming and clustering
algorithm families: SDP rounding algorithms and agglomerative clustering
algorithms with dynamic programming. For our sample complexity analysis, we
provide tight bounds on the pseudodimension of these algorithm classes, and
show that surprisingly, even for classes of algorithms parameterized by a
single parameter, the pseudo-dimension is superconstant. In this way, our work
both contributes to the foundations of algorithm configuration and pushes the
boundaries of learning theory, since the algorithm classes we analyze consist
of multi-stage optimization procedures and are significantly more complex than
classes typically studied in learning theory.
Trunk-Branch Ensemble Convolutional Neural Networks for Video-based Face Recognition
Authors: Changxing Ding, DaCheng Tao
Date: 2016-07-19 00:00:00
Tasks: Face Recognition, Person Recognition, Triplet

Human faces in surveillance videos often suffer from severe image blur,
dramatic pose variations, and occlusion. In this paper, we propose a
comprehensive framework based on Convolutional Neural Networks (CNN) to
overcome challenges in video-based face recognition (VFR). First, to learn
blur-robust face representations, we artificially blur training data composed
of clear still images to account for a shortfall in real-world video training
data. Using training data composed of both still images and artificially
blurred data, CNN is encouraged to learn blur-insensitive features
automatically. Second, to enhance robustness of CNN features to pose variations
and occlusion, we propose a Trunk-Branch Ensemble CNN model (TBE-CNN), which
extracts complementary information from holistic face images and patches
cropped around facial components. TBE-CNN is an end-to-end model that extracts
features efficiently by sharing the low- and middle-level convolutional layers
between the trunk and branch networks. Third, to further promote the
discriminative power of the representations learnt by TBE-CNN, we propose an
improved triplet loss function. Systematic experiments justify the
effectiveness of the proposed techniques. Most impressively, TBE-CNN achieves
state-of-the-art performance on three popular video face databases: PaSC, COX
Face, and YouTube Faces. With the proposed techniques, we also obtain the first
place in the BTAS 2016 Video Person Recognition Evaluation.
Pitfalls and Best Practices in Algorithm Configuration
Authors: Katharina Eggensperger, Marius Lindauer, Frank Hutter
Date: 2017-05-17 00:00:00
Tasks: Experimental Design, Scheduling

Good parameter settings are crucial to achieve high performance in many areas
of artificial intelligence (AI), such as propositional satisfiability solving,
AI planning, scheduling, and machine learning (in particular deep learning).
Automated algorithm configuration methods have recently received much attention
in the AI community since they replace tedious, irreproducible and error-prone
manual parameter tuning and can lead to new state-of-the-art performance.
However, practical applications of algorithm configuration are prone to several
(often subtle) pitfalls in the experimental design that can render the
procedure ineffective. We identify several common issues and propose best
practices for avoiding them. As one possibility for automatically handling as
many of these as possible, we also propose a tool called GenericWrapper4AC.
Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps
Authors: Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
Date: 2017-05-17 00:00:00
Tasks: Earth Observation

In this work, we investigate the use of OpenStreetMap data for semantic
labeling of Earth Observation images. Deep neural networks have been used in
the past for remote sensing data classification from various sensors, including
multispectral, hyperspectral, SAR and LiDAR data. While OpenStreetMap has
already been used as ground truth data for training such networks, this
abundant data source remains rarely exploited as an input information layer. In
this paper, we study different use cases and deep network architectures to
leverage OpenStreetMap data for semantic labeling of aerial and satellite
images. Especially , we look into fusion based architectures and coarse-to-fine
segmentation to include the OpenStreetMap layer into multispectral-based deep
fully convolutional networks. We illustrate how these methods can be
successfully used on two public datasets: ISPRS Potsdam and DFC2017. We show
that OpenStreetMap data can efficiently be integrated into the vision-based
deep learning models and that it significantly improves both the accuracy
performance and the convergence speed of the networks.
On Kernelized Multi-armed Bandits
Authors: Sayak Ray Chowdhury, Aditya Gopalan
Date: 2017-04-03 00:00:00
Proceeding: ICML 2017 8
Tasks: Multi-Armed Bandits

We consider the stochastic bandit problem with a continuous set of arms, with
the expected reward function over the arms assumed to be fixed but unknown. We
provide two new Gaussian process-based algorithms for continuous bandit
optimization-Improved GP-UCB (IGP-UCB) and GP-Thomson sampling (GP-TS), and
derive corresponding regret bounds. Specifically, the bounds hold when the
expected reward function belongs to the reproducing kernel Hilbert space (RKHS)
that naturally corresponds to a Gaussian process kernel used as input by the
algorithms. Along the way, we derive a new self-normalized concentration
inequality for vector- valued martingales of arbitrary, possibly infinite,
dimension. Finally, experimental evaluation and comparisons to existing
algorithms on synthetic and real-world environments are carried out that
highlight the favorable gains of the proposed strategies in many cases.
Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques
Authors: Debarghya Ghoshdastidar, Ambedkar Dukkipati
Date: 2016-02-21 00:00:00
Tasks: Clustering, hypergraph partitioning, Stochastic Block Model

In a series of recent works, we have generalised the consistency results in
the stochastic block model literature to the case of uniform and non-uniform
hypergraphs. The present paper continues the same line of study, where we focus
on partitioning weighted uniform hypergraphs---a problem often encountered in
computer vision. This work is motivated by two issues that arise when a
hypergraph partitioning approach is used to tackle computer vision problems:
(i) The uniform hypergraphs constructed for higher-order learning contain all
edges, but most have negligible weights. Thus, the adjacency tensor is nearly
sparse, and yet, not binary. (ii) A more serious concern is that standard
partitioning algorithms need to compute all edge weights, which is
computationally expensive for hypergraphs. This is usually resolved in practice
by merging the clustering algorithm with a tensor sampling strategy---an
approach that is yet to be analysed rigorously. We build on our earlier work on
partitioning dense unweighted uniform hypergraphs (Ghoshdastidar and Dukkipati,
ICML, 2015), and address the aforementioned issues by proposing provable and
efficient partitioning algorithms. Our analysis justifies the empirical success
of practical sampling techniques. We also complement our theoretical findings
by elaborate empirical comparison of various hypergraph partitioning schemes.
Joint Positioning and Radio Map Generation Based on Stochastic Variational Bayesian Inference for FWIPS
Authors: Caifa Zhou, Yang Gu
Date: 2017-05-17 00:00:00
Tasks: Bayesian Inference, Position, Survey

Fingerprinting based WLAN indoor positioning system (FWIPS) provides a
promising indoor positioning solution to meet the growing interests for indoor
location-based services (e.g., indoor way finding or geo-fencing). FWIPS is
preferred because it requires no additional infrastructure for deploying an
FWIPS and achieving the position estimation by reusing the available WLAN and
mobile devices, and capable of providing absolute position estimation. For
fingerprinting based positioning (FbP), a model is created to provide reference
values of observable features (e.g., signal strength from access point (AP)) as
a function of location during offline stage. One widely applied method to build
a complete and an accurate reference database (i.e. radio map (RM)) for FWIPS
is carrying out a site survey throughout the region of interest (RoI). Along
the site survey, the readings of received signal strength (RSS) from all
visible APs at each reference point (RP) are collected. This site survey,
however, is time-consuming and labor-intensive, especially in the case that the
RoI is large (e.g., an airport or a big mall). This bottleneck hinders the wide
commercial applications of FWIPS (e.g., proximity promotions in a shopping
center). To diminish the cost of site survey, we propose a probabilistic model,
which combines fingerprinting based positioning (FbP) and RM generation based
on stochastic variational Bayesian inference (SVBI). This SVBI based position
and RSS estimation has three properties: i) being able to predict the
distribution of the estimated position and RSS, ii) treating each observation
of RSS at each RP as an example to learn for FbP and RM generation instead of
using the whole RM as an example, and iii) requiring only one time training of
the SVBI model for both localization and RSS estimation. These benefits make it
outperforms the previous proposed approaches.
PaMM: Pose-aware Multi-shot Matching for Improving Person Re-identification
Authors: Yeong-Jun Cho, Kuk-Jin Yoon
Date: 2017-05-17 00:00:00
Tasks: Person Re-Identification

Person re-identification is the problem of recognizing people across
different images or videos with non-overlapping views. Although there has been
much progress in person re-identification over the last decade, it remains a
challenging task because appearances of people can seem extremely different
across diverse camera viewpoints and person poses. In this paper, we propose a
novel framework for person re-identification by analyzing camera viewpoints and
person poses in a so-called Pose-aware Multi-shot Matching (PaMM), which
robustly estimates people's poses and efficiently conducts multi-shot matching
based on pose information. Experimental results using public person
re-identification datasets show that the proposed methods outperform
state-of-the-art methods and are promising for person re-identification from
diverse viewpoints and pose variances.
One Shot Joint Colocalization and Cosegmentation
Authors: Abhishek Sharma
Date: 2017-05-17 00:00:00
Tasks: Clustering, Multi-Task Learning

This paper presents a novel framework in which image cosegmentation and
colocalization are cast into a single optimization problem that integrates
information from low level appearance cues with that of high level localization
cues in a very weakly supervised manner. In contrast to multi-task learning
paradigm that learns similar tasks using a shared representation, the proposed
framework leverages two representations at different levels and simultaneously
discriminates between foreground and background at the bounding box and
superpixel level using discriminative clustering. We show empirically that
constraining the two problems at different scales enables the transfer of
semantic localization cues to improve cosegmentation output whereas local
appearance based segmentation cues help colocalization. The unified framework
outperforms strong baseline approaches, of learning the two problems
separately, by a large margin on four benchmark datasets. Furthermore, it
obtains competitive results compared to the state of the art for cosegmentation
on two benchmark datasets and second best result for colocalization on Pascal
VOC 2007.
Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularization
Authors: Dong Yang, Tao Xiong, Daguang Xu, Qiangui Huang, David Liu, S. Kevin Zhou, Zhoubing Xu, Jin-Hyeong Park, Mingqing Chen, Trac. D. Tran, Sang Peter Chin, Dimitris Metaxas, Dorin Comaniciu
Date: 2017-05-17 00:00:00

Automatic localization and labeling of vertebra in 3D medical images plays an
important role in many clinical tasks, including pathological diagnosis,
surgical planning and postoperative assessment. However, the unusual conditions
of pathological cases, such as the abnormal spine curvature, bright visual
imaging artifacts caused by metal implants, and the limited field of view,
increase the difficulties of accurate localization. In this paper, we propose
an automatic and fast algorithm to localize and label the vertebra centroids in
3D CT volumes. First, we deploy a deep image-to-image network (DI2IN) to
initialize vertebra locations, employing the convolutional encoder-decoder
architecture together with multi-level feature concatenation and deep
supervision. Next, the centroid probability maps from DI2IN are iteratively
evolved with the message passing schemes based on the mutual relation of
vertebra centroids. Finally, the localization results are refined with sparsity
regularization. The proposed method is evaluated on a public dataset of 302
spine CT volumes with various pathologies. Our method outperforms other
state-of-the-art methods in terms of localization accuracy. The run time is
around 3 seconds on average per case. To further boost the performance, we
retrain the DI2IN on additional 1000+ 3D CT volumes from different patients. To
the best of our knowledge, this is the first time more than 1000 3D CT volumes
with expert annotation are adopted in experiments for the anatomic landmark
detection tasks. Our experimental results show that training with such a large
dataset significantly improves the performance and the overall identification
rate, for the first time by our knowledge, reaches 90 %.
Localized LRR on Grassmann Manifolds: An Extrinsic View
Authors: Boyue Wang, Yongli Hu, Junbin Gao, Yanfeng Sun, Bao-Cai Yin
Date: 2017-05-17 00:00:00
Tasks: Clustering

Subspace data representation has recently become a common practice in many
computer vision tasks. It demands generalizing classical machine learning
algorithms for subspace data. Low-Rank Representation (LRR) is one of the most
successful models for clustering vectorial data according to their subspace
structures. This paper explores the possibility of extending LRR for subspace
data on Grassmann manifolds. Rather than directly embedding the Grassmann
manifolds into the symmetric matrix space, an extrinsic view is taken to build
the LRR self-representation in the local area of the tangent space at each
Grassmannian point, resulting in a localized LRR method on Grassmann manifolds.
A novel algorithm for solving the proposed model is investigated and
implemented. The performance of the new clustering algorithm is assessed
through experiments on several real-world datasets including MNIST handwritten
digits, ballet video clips, SKIG action clips, DynTex++ dataset and highway
traffic video clips. The experimental results show the new method outperforms a
number of state-of-the-art clustering methods
Frame Stacking and Retaining for Recurrent Neural Network Acoustic Model
Authors: Xu Tian, Jun Zhang, Zejun Ma, Yi He, Juan Wei
Date: 2017-05-17 00:00:00
Tasks: Decoder, General Classification

Frame stacking is broadly applied in end-to-end neural network training like
connectionist temporal classification (CTC), and it leads to more accurate
models and faster decoding. However, it is not well-suited to conventional
neural network based on context-dependent state acoustic model, if the decoder
is unchanged. In this paper, we propose a novel frame retaining method which is
applied in decoding. The system which combined frame retaining with frame
stacking could reduces the time consumption of both training and decoding. Long
short-term memory (LSTM) recurrent neural networks (RNNs) using it achieve
almost linear training speedup and reduces relative 41\% real time factor
(RTF). At the same time, recognition performance is no degradation or improves
sightly on Shenma voice search dataset in Mandarin.
REMIX: Automated Exploration for Interactive Outlier Detection
Authors: Yanjie Fu, Charu Aggarwal, Srinivasan Parthasarathy, Deepak S. Turaga, Hui Xiong
Date: 2017-05-17 00:00:00
Tasks: Meta-Learning, Outlier Detection

Outlier detection is the identification of points in a dataset that do not
conform to the norm. Outlier detection is highly sensitive to the choice of the
detection algorithm and the feature subspace used by the algorithm. Extracting
domain-relevant insights from outliers needs systematic exploration of these
choices since diverse outlier sets could lead to complementary insights. This
challenge is especially acute in an interactive setting, where the choices must
be explored in a time-constrained manner. In this work, we present REMIX, the
first system to address the problem of outlier detection in an interactive
setting. REMIX uses a novel mixed integer programming (MIP) formulation for
automatically selecting and executing a diverse set of outlier detectors within
a time limit. This formulation incorporates multiple aspects such as (i) an
upper limit on the total execution time of detectors (ii) diversity in the
space of algorithms and features, and (iii) meta-learning for evaluating the
cost and utility of detectors. REMIX provides two distinct ways for the analyst
to consume its results: (i) a partitioning of the detectors explored by REMIX
into perspectives through low-rank non-negative matrix factorization; each
perspective can be easily visualized as an intuitive heatmap of experiments
versus outliers, and (ii) an ensembled set of outliers which combines outlier
scores from all detectors. We demonstrate the benefits of REMIX through
extensive empirical validation on real-world data.
A Continuous Opinion Dynamic Model in Co-evolving Networks--A Novel Group Decision Approach
Authors: Qingxing Dong, Xin Zhou
Date: 2017-05-17 00:00:00
Tasks: Decision Making

Opinion polarization is a ubiquitous phenomenon in opinion dynamics. In
contrast to the traditional consensus oriented group decision making (GDM)
framework, this paper proposes a framework with the co-evolution of both
opinions and relationship networks to improve the potential consensus level of
a group and help the group reach a stable state. Taking the bound of confidence
and the degree of individual's persistence into consideration, the evolution of
the opinion is driven by the relationship among the group. Meanwhile, the
antagonism or cooperation of individuals presented by the network topology also
evolve according to the dynamic opinion distances. Opinions are convergent and
the stable state will be reached in this co-evolution mechanism. We further
explored this framework through simulation experiments. The simulation results
verify the influence of the level of persistence on the time cost and indicate
the influence of group size, the initial topology of networks and the bound of
confidence on the number of opinion clusters.
A Comparative Study for the Nuclear Norms Minimization Methods
Authors: Zhiyuan Zha, Bihan Wen, Jiachao Zhang, Jiantao Zhou, Ce Zhu
Date: 2016-08-16 00:00:00
Tasks: Deblurring, Denoising, Dictionary Learning, Image Denoising, Image Inpainting

The nuclear norm minimization (NNM) is commonly used to approximate the matrix rank by shrinking all singular values equally. However, the singular values have clear physical meanings in many practical problems, and NNM may not be able to faithfully approximate the matrix rank. To alleviate the above-mentioned limitation of NNM, recent studies have suggested that the weighted nuclear norm minimization (WNNM) can achieve a better rank estimation than NNM, which heuristically set the weight being inverse to the singular values. However, it still lacks a rigorous explanation why WNNM is more effective than NMM in various applications. In this paper, we analyze NNM and WNNM from the perspective of group sparse representation (GSR). Concretely, an adaptive dictionary learning method is devised to connect the rank minimization and GSR models. Based on the proposed dictionary, we prove that NNM and WNNM are equivalent to L1-norm minimization and the weighted L1-norm minimization in GSR, respectively. Inspired by enhancing sparsity of the weighted L1-norm minimization in comparison with L1-norm minimization in sparse representation, we thus explain that WNNM is more effective than NMM. By integrating the image nonlocal self-similarity (NSS) prior with the WNNM model, we then apply it to solve the image denoising problem. Experimental results demonstrate that WNNM is more effective than NNM and outperforms several state-of-the-art methods in both objective and perceptual quality.