pwc-archive/papers-with-abstracts huggingface dataset:
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form
Authors: Srinadh Bhojanapalli, Nicolas Boumal, Prateek Jain, Praneeth Netrapalli
Date: 2018-03-01 00:00:00
Tasks: Combinatorial Optimization, Form, Matrix Completion

Semidefinite programs (SDP) are important in learning and combinatorial
optimization with numerous applications. In pursuit of low-rank solutions and
low complexity algorithms, we consider the Burer--Monteiro factorization
approach for solving SDPs. We show that all approximate local optima are global
optima for the penalty formulation of appropriately rank-constrained SDPs as
long as the number of constraints scales sub-quadratically with the desired
rank of the optimal solution. Our result is based on a simple penalty function
formulation of the rank-constrained SDP along with a smoothed analysis to avoid
worst-case cost matrices. We particularize our results to two applications,
namely, Max-Cut and matrix completion.
Facial Expression Recognition Based on Complexity Perception Classification Algorithm
Authors: Tianyuan Chang, Guihua Wen, Yang Hu, Jiajiong Ma
Date: 2018-03-01 00:00:00
Tasks: Classification, Facial Expression Recognition, Facial Expression Recognition (FER), General Classification

Facial expression recognition (FER) has always been a challenging issue in
computer vision. The different expressions of emotion and uncontrolled
environmental factors lead to inconsistencies in the complexity of FER and
variability of between expression categories, which is often overlooked in most
facial expression recognition systems. In order to solve this problem
effectively, we presented a simple and efficient CNN model to extract facial
features, and proposed a complexity perception classification (CPC) algorithm
for FER. The CPC algorithm divided the dataset into an easy classification
sample subspace and a complex classification sample subspace by evaluating the
complexity of facial features that are suitable for classification. The
experimental results of our proposed algorithm on Fer2013 and CK-plus datasets
demonstrated the algorithm's effectiveness and superiority over other
state-of-the-art approaches.
Matching Natural Language Sentences with Hierarchical Sentence Factorization
Authors: Bang Liu, Ting Zhang, Fred X. Han, Di Niu, Kunfeng Lai, Yu Xu
Date: 2018-03-01 00:00:00
Tasks: Deep Learning, Paraphrase Identification, Sentence

Semantic matching of natural language sentences or identifying the
relationship between two sentences is a core research problem underlying many
natural language tasks. Depending on whether training data is available, prior
research has proposed both unsupervised distance-based schemes and supervised
deep learning schemes for sentence matching. However, previous approaches
either omit or fail to fully utilize the ordered, hierarchical, and flexible
structures of language objects, as well as the interactions between them. In
this paper, we propose Hierarchical Sentence Factorization---a technique to
factorize a sentence into a hierarchical representation, with the components at
each different scale reordered into a "predicate-argument" form. The proposed
sentence factorization technique leads to the invention of: 1) a new
unsupervised distance metric which calculates the semantic distance between a
pair of text snippets by solving a penalized optimal transport problem while
preserving the logical relationship of words in the reordered sentences, and 2)
new multi-scale deep learning models for supervised semantic training, based on
factorized sentence hierarchies. We apply our techniques to text-pair
similarity estimation and text-pair relationship classification tasks, based on
multiple datasets such as STSbenchmark, the Microsoft Research paraphrase
identification (MSRP) dataset, the SICK dataset, etc. Extensive experiments
show that the proposed hierarchical sentence factorization can be used to
significantly improve the performance of existing unsupervised distance-based
metrics as well as multiple supervised deep learning models based on the
convolutional neural network (CNN) and long short-term memory (LSTM).
Learning Sampling Distributions for Robot Motion Planning
Authors: Brian Ichter, James Harrison, Marco Pavone
Date: 2017-09-16 00:00:00
Tasks: Collision Avoidance, Motion Planning

A defining feature of sampling-based motion planning is the reliance on an
implicit representation of the state space, which is enabled by a set of
probing samples. Traditionally, these samples are drawn either
probabilistically or deterministically to uniformly cover the state space. Yet,
the motion of many robotic systems is often restricted to "small" regions of
the state space, due to, for example, differential constraints or
collision-avoidance constraints. To accelerate the planning process, it is thus
desirable to devise non-uniform sampling strategies that favor sampling in
those regions where an optimal solution might lie. This paper proposes a
methodology for non-uniform sampling, whereby a sampling distribution is
learned from demonstrations, and then used to bias sampling. The sampling
distribution is computed through a conditional variational autoencoder,
allowing sample generation from the latent space conditioned on the specific
planning problem. This methodology is general, can be used in combination with
any sampling-based planner, and can effectively exploit the underlying
structure of a planning problem while maintaining the theoretical guarantees of
sampling-based approaches. Specifically, on several planning problems, the
proposed methodology is shown to effectively learn representations for the
relevant regions of the state space, resulting in an order of magnitude
improvement in terms of success rate and convergence to the optimal cost.
Towards Cooperation in Sequential Prisoner's Dilemmas: a Deep Multiagent Reinforcement Learning Approach
Authors: Weixun Wang, Jianye Hao, Yixi Wang, Matthew Taylor
Date: 2018-03-01 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The Iterated Prisoner's Dilemma has guided research on social dilemmas for
decades. However, it distinguishes between only two atomic actions: cooperate
and defect. In real-world prisoner's dilemmas, these choices are temporally
extended and different strategies may correspond to sequences of actions,
reflecting grades of cooperation. We introduce a Sequential Prisoner's Dilemma
(SPD) game to better capture the aforementioned characteristics. In this work,
we propose a deep multiagent reinforcement learning approach that investigates
the evolution of mutual cooperation in SPD games. Our approach consists of two
phases. The first phase is offline: it synthesizes policies with different
cooperation degrees and then trains a cooperation degree detection network. The
second phase is online: an agent adaptively selects its policy based on the
detected degree of opponent cooperation. The effectiveness of our approach is
demonstrated in two representative SPD 2D games: the Apple-Pear game and the
Fruit Gathering game. Experimental results show that our strategy can avoid
being exploited by exploitative opponents and achieve cooperation with
cooperative opponents.
A Class-Incremental Learning Method Based on One Class Support Vector Machine
Authors: Chengfei Yao, Jie Zou, Yanan Luo, Tao Li, Gang Bai
Date: 2018-03-01 00:00:00
Tasks: class-incremental learning, Class Incremental Learning, Incremental Learning

A method based on one class support vector machine (OCSVM) is proposed for
class incremental learning. Several OCSVM models divide the input space into
several parts. Then, the 1VS1 classifiers are constructed for the confuse part
by using the support vectors. During the class incremental learning process,
the OCSVM of the new class is trained at first. Then the support vectors of the
old classes and the support vectors of the new class are reused to train 1VS1
classifiers for the confuse part. In order to bring more information to the
certain support vectors, the support vectors are at the boundary of the
distribution of samples as much as possible when the OCSVM is built. Compared
with the traditional methods, the proposed method retains the original model
and thus reduces memory consumption and training time cost. Various experiments
on different datasets also verify the efficiency of the proposed method.
Modeling reverse thinking for machine learning
Authors: Li Huihui, Wen Guihua
Date: 2018-03-01 00:00:00
Tasks: BIG-bench Machine Learning

Human inertial thinking schemes can be formed through learning, which are
then applied to quickly solve similar problems later. However, when problems
are significantly different, inertial thinking generally presents the solutions
that are definitely imperfect. In such cases, people will apply creative
thinking, such as reverse thinking, to solve problems. Similarly, machine
learning methods also form inertial thinking schemes through learning the
knowledge from a large amount of data. However, when the testing data are
vastly difference, the formed inertial thinking schemes will inevitably
generate errors. This kind of inertial thinking is called illusion inertial
thinking. Because all machine learning methods do not consider illusion
inertial thinking, in this paper we propose a new method that uses reverse
thinking to correct illusion inertial thinking, which increases the
generalization ability of machine learning methods. Experimental results on
benchmark datasets are used to validate the proposed method.
Niching an Archive-based Gaussian Estimation of Distribution Algorithm via Adaptive Clustering
Authors: Yongsheng Liang, Zhigang Ren, Bei Pang, An Chen
Date: 2018-03-01 00:00:00
Tasks: Clustering, global-optimization

As a model-based evolutionary algorithm, estimation of distribution algorithm
(EDA) possesses unique characteristics and has been widely applied to global
optimization. However, traditional Gaussian EDA (GEDA) may suffer from
premature convergence and has a high risk of falling into local optimum when
dealing with multimodal problem. In this paper, we first attempts to improve
the performance of GEDA by utilizing historical solutions and develops a novel
archive-based EDA variant. The use of historical solutions not only enhances
the search efficiency of EDA to a large extent, but also significantly reduces
the population size so that a faster convergence could be achieved. Then, the
archive-based EDA is further integrated with a novel adaptive clustering
strategy for solving multimodal optimization problems. Taking the advantage of
the clustering strategy in locating different promising areas and the powerful
exploitation ability of the archive-based EDA, the resultant algorithm is
endowed with strong capability in finding multiple optima. To verify the
efficiency of the proposed algorithm, we tested it on a set of well-known
niching benchmark problems and compared it with several state-of-the-art
niching algorithms. The experimental results indicate that the proposed
algorithm is competitive.
Autoencoding topology
Authors: Eric O. Korman
Date: 2018-03-01 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Dimensionality Reduction

The problem of learning a manifold structure on a dataset is framed in terms
of a generative model, to which we use ideas behind autoencoders (namely
adversarial/Wasserstein autoencoders) to fit deep neural networks. From a
machine learning perspective, the resulting structure, an atlas of a manifold,
may be viewed as a combination of dimensionality reduction and "fuzzy"
clustering.
A Global Information Based Adaptive Threshold for Grouping Large Scale Global Optimization Problems
Authors: An Chen, Yi-Peng Zhang, Zhigang Ren, Yongsheng Liang, Bei Pang
Date: 2018-03-01 00:00:00
Tasks: global-optimization

By taking the idea of divide-and-conquer, cooperative coevolution (CC)
provides a powerful architecture for large scale global optimization (LSGO)
problems, but its efficiency relies highly on the decomposition strategy. It
has been shown that differential grouping (DG) performs well on decomposing
LSGO problems by effectively detecting the interaction among decision
variables. However, its decomposition accuracy depends highly on the threshold.
To improve the decomposition accuracy of DG, a global information based
adaptive threshold setting algorithm (GIAT) is proposed in this paper. On the
one hand, by reducing the sensitivity of the indicator in DG to the roundoff
error and the magnitude of contribution weight of subcomponent, we proposed a
new indicator for two variables which is much more sensitive to their
interaction. On the other hand, instead of setting the threshold only based on
one pair of variables, the threshold is generated from the interaction
information for all pair of variables. By conducting the experiments on two
sets of LSGO benchmark functions, the correctness and robustness of this new
indicator and GIAT were verified.
Enhancing Cooperative Coevolution for Large Scale Optimization by Adaptively Constructing Surrogate Models
Authors: Bei Pang, Zhigang Ren, Yongsheng Liang, An Chen
Date: 2018-03-01 00:00:00

It has been shown that cooperative coevolution (CC) can effectively deal with
large scale optimization problems (LSOPs) through a divide-and-conquer
strategy. However, its performance is severely restricted by the current
context-vector-based sub-solution evaluation method since this method needs to
access the original high dimensional simulation model when evaluating each
sub-solution and thus requires many computation resources. To alleviate this
issue, this study proposes an adaptive surrogate model assisted CC framework.
This framework adaptively constructs surrogate models for different
sub-problems by fully considering their characteristics. For the single
dimensional sub-problems obtained through decomposition, accurate enough
surrogate models can be obtained and used to find out the optimal solutions of
the corresponding sub-problems directly. As for the nonseparable sub-problems,
the surrogate models are employed to evaluate the corresponding sub-solutions,
and the original simulation model is only adopted to reevaluate some good
sub-solutions selected by surrogate models. By these means, the computation
cost could be greatly reduced without significantly sacrificing evaluation
quality. Empirical studies on IEEE CEC 2010 benchmark functions show that the
concrete algorithm based on this framework is able to find much better
solutions than the conventional CC algorithms and a non-CC algorithm even with
much fewer computation resources.
Deep Learning for Causal Inference
Authors: Vikas Ramachandra
Date: 2018-03-01 00:00:00
Tasks: Causal Inference, Deep Learning, Dimensionality Reduction, Econometrics, regression

In this paper, we propose deep learning techniques for econometrics,
specifically for causal inference and for estimating individual as well as
average treatment effects. The contribution of this paper is twofold: 1. For
generalized neighbor matching to estimate individual and average treatment
effects, we analyze the use of autoencoders for dimensionality reduction while
maintaining the local neighborhood structure among the data points in the
embedding space. This deep learning based technique is shown to perform better
than simple k nearest neighbor matching for estimating treatment effects,
especially when the data points have several features/covariates but reside in
a low dimensional manifold in high dimensional space. We also observe better
performance than manifold learning methods for neighbor matching. 2. Propensity
score matching is one specific and popular way to perform matching in order to
estimate average and individual treatment effects. We propose the use of deep
neural networks (DNNs) for propensity score matching, and present a network
called PropensityNet for this. This is a generalization of the logistic
regression technique traditionally used to estimate propensity scores and we
show empirically that DNNs perform better than logistic regression at
propensity score matching. Code for both methods will be made available shortly
on Github at: https://github.com/vikas84bf
Learning to Detect Human-Object Interactions
Authors: Yu-Wei Chao, Yunfan Liu, Xieyang Liu, Huayi Zeng, Jia Deng
Date: 2017-02-17 00:00:00
Tasks: General Classification, Human-Object Interaction Detection, Object

We study the problem of detecting human-object interactions (HOI) in static
images, defined as predicting a human and an object bounding box with an
interaction class label that connects them. HOI detection is a fundamental
problem in computer vision as it provides semantic information about the
interactions among the detected objects. We introduce HICO-DET, a new large
benchmark for HOI detection, by augmenting the current HICO classification
benchmark with instance annotations. To solve the task, we propose Human-Object
Region-based Convolutional Neural Networks (HO-RCNN). At the core of our
HO-RCNN is the Interaction Pattern, a novel DNN input that characterizes the
spatial relations between two bounding boxes. Experiments on HICO-DET
demonstrate that our HO-RCNN, by exploiting human-object spatial relations
through Interaction Patterns, significantly improves the performance of HOI
detection over baseline approaches.
Regularized Evolution for Image Classifier Architecture Search
Authors: Esteban Real, Alok Aggarwal, Yanping Huang, Quoc V. Le
Date: 2018-02-05 00:00:00
Tasks: Evolutionary Algorithms, Image Classification, Neural Architecture Search, Reinforcement Learning

The effort devoted to hand-crafting neural network image classifiers has
motivated the use of architecture search to discover them automatically.
Although evolutionary algorithms have been repeatedly applied to neural network
topologies, the image classifiers thus discovered have remained inferior to
human-crafted ones. Here, we evolve an image classifier---AmoebaNet-A---that
surpasses hand-designs for the first time. To do this, we modify the tournament
selection evolutionary algorithm by introducing an age property to favor the
younger genotypes. Matching size, AmoebaNet-A has comparable accuracy to
current state-of-the-art ImageNet models discovered with more complex
architecture-search methods. Scaled to larger size, AmoebaNet-A sets a new
state-of-the-art 83.9% / 96.6% top-5 ImageNet accuracy. In a controlled
comparison against a well known reinforcement learning algorithm, we give
evidence that evolution can obtain results faster with the same hardware,
especially at the earlier stages of the search. This is relevant when fewer
compute resources are available. Evolution is, thus, a simple method to
effectively discover high-quality architectures.
Does mitigating ML's impact disparity require treatment disparity?
Authors: Zachary C. Lipton, Alexandra Chouldechova, Julian McAuley
Date: 2017-11-19 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Decision Making, Fairness

Following related work in law and policy, two notions of disparity have come
to shape the study of fairness in algorithmic decision-making. Algorithms
exhibit treatment disparity if they formally treat members of protected
subgroups differently; algorithms exhibit impact disparity when outcomes differ
across subgroups, even if the correlation arises unintentionally. Naturally, we
can achieve impact parity through purposeful treatment disparity. In one thread
of technical work, papers aim to reconcile the two forms of parity proposing
disparate learning processes (DLPs). Here, the learning algorithm can see group
membership during training but produce a classifier that is group-blind at test
time. In this paper, we show theoretically that: (i) When other features
correlate to group membership, DLPs will (indirectly) implement treatment
disparity, undermining the policy desiderata they are designed to address; (ii)
When group membership is partly revealed by other features, DLPs induce
within-class discrimination; and (iii) In general, DLPs provide a suboptimal
trade-off between accuracy and impact parity. Based on our technical analysis,
we argue that transparent treatment disparity is preferable to occluded methods
for achieving impact parity. Experimental results on several real-world
datasets highlight the practical consequences of applying DLPs vs. per-group
thresholds.
Ring loss: Convex Feature Normalization for Face Recognition
Authors: Yutong Zheng, Dipan K. Pal, Marios Savvides
Date: 2018-02-28 00:00:00
Proceeding: CVPR 2018 6
Tasks: Face Identification, Face Recognition, Face Verification

We motivate and present Ring loss, a simple and elegant feature normalization
approach for deep networks designed to augment standard loss functions such as
Softmax. We argue that deep feature normalization is an important aspect of
supervised classification problems where we require the model to represent each
class in a multi-class problem equally well. The direct approach to feature
normalization through the hard normalization operation results in a non-convex
formulation. Instead, Ring loss applies soft normalization, where it gradually
learns to constrain the norm to the scaled unit circle while preserving
convexity leading to more robust features. We apply Ring loss to large-scale
face recognition problems and present results on LFW, the challenging protocols
of IJB-A Janus, Janus CS3 (a superset of IJB-A Janus), Celebrity
Frontal-Profile (CFP) and MegaFace with 1 million distractors. Ring loss
outperforms strong baselines, matches state-of-the-art performance on IJB-A
Janus and outperforms all other results on the challenging Janus CS3 thereby
achieving state-of-the-art. We also outperform strong baselines in handling
extremely low resolution face matching.
SalientDSO: Bringing Attention to Direct Sparse Odometry
Authors: Huai-Jen Liang, Nitin J. Sanket, Cornelia Fermüller, Yiannis Aloimonos
Date: 2018-02-28 00:00:00
Tasks: feature selection, Scene Parsing, Visual Odometry

Although cluttered indoor scenes have a lot of useful high-level semantic
information which can be used for mapping and localization, most Visual
Odometry (VO) algorithms rely on the usage of geometric features such as
points, lines and planes. Lately, driven by this idea, the joint optimization
of semantic labels and obtaining odometry has gained popularity in the robotics
community. The joint optimization is good for accurate results but is generally
very slow. At the same time, in the vision community, direct and sparse
approaches for VO have stricken the right balance between speed and accuracy.
  We merge the successes of these two communities and present a way to
incorporate semantic information in the form of visual saliency to Direct
Sparse Odometry - a highly successful direct sparse VO algorithm. We also
present a framework to filter the visual saliency based on scene parsing. Our
framework, SalientDSO, relies on the widely successful deep learning based
approaches for visual saliency and scene parsing which drives the feature
selection for obtaining highly-accurate and robust VO even in the presence of
as few as 40 point features per frame. We provide extensive quantitative
evaluation of SalientDSO on the ICL-NUIM and TUM monoVO datasets and show that
we outperform DSO and ORB-SLAM - two very popular state-of-the-art approaches
in the literature. We also collect and publicly release a CVL-UMD dataset which
contains two indoor cluttered sequences on which we show qualitative
evaluations. To our knowledge this is the first paper to use visual saliency
and scene parsing to drive the feature selection in direct VO.