pwc-archive/papers-with-abstracts huggingface dataset:
Explanatory relations in arbitrary logics based on satisfaction systems, cutting and retraction
Authors: Marc Aiguier, Jamal Atif, Isabelle Bloch, Ramón Pino-Pérez
Date: 2018-03-05 00:00:00

The aim of this paper is to introduce a new framework for defining abductive
reasoning operators based on a notion of retraction in arbitrary logics defined
as satisfaction systems. We show how this framework leads to the design of
explanatory relations satisfying properties of abductive reasoning, and discuss
its application to several logics. This extends previous work on propositional
logics where retraction was defined as a morphological erosion. Here weaker
properties are required for retraction, leading to a larger set of suitable
operators for abduction for different logics.
Adversarial Extreme Multi-label Classification
Authors: Rohit Babbar, Bernhard Schölkopf
Date: 2018-03-05 00:00:00
Tasks: Classification, Extreme Multi-Label Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

The goal in extreme multi-label classification is to learn a classifier which
can assign a small subset of relevant labels to an instance from an extremely
large set of target labels. Datasets in extreme classification exhibit a long
tail of labels which have small number of positive training instances. In this
work, we pose the learning task in extreme classification with large number of
tail-labels as learning in the presence of adversarial perturbations. This view
motivates a robust optimization framework and equivalence to a corresponding
regularized objective.
  Under the proposed robustness framework, we demonstrate efficacy of Hamming
loss for tail-label detection in extreme classification. The equivalent
regularized objective, in combination with proximal gradient based
optimization, performs better than state-of-the-art methods on propensity
scored versions of precision@k and nDCG@k(upto 20% relative improvement over
PFastreXML - a leading tree-based approach and 60% relative improvement over
SLEEC - a leading label-embedding approach). Furthermore, we also highlight the
sub-optimality of a sparse solver in a widely used package for large-scale
linear classification, which is interesting in its own right. We also
investigate the spectral properties of label graphs for providing novel
insights towards understanding the conditions governing the performance of
Hamming loss based one-vs-rest scheme vis-\`a-vis label embedding methods.
Sliced Wasserstein Generative Models
Authors: Jiqing Wu, Zhiwu Huang, Dinesh Acharya, Wen Li, Janine Thoma, Danda Pani Paudel, Luc van Gool
Date: 2017-06-08 00:00:00
Tasks: Image Generation, Video Generation

In generative modeling, the Wasserstein distance (WD) has emerged as a useful
metric to measure the discrepancy between generated and real data
distributions. Unfortunately, it is challenging to approximate the WD of
high-dimensional distributions. In contrast, the sliced Wasserstein distance
(SWD) factorizes high-dimensional distributions into their multiple
one-dimensional marginal distributions and is thus easier to approximate. In
this paper, we introduce novel approximations of the primal and dual SWD.
Instead of using a large number of random projections, as it is done by
conventional SWD approximation methods, we propose to approximate SWDs with a
small number of parameterized orthogonal projections in an end-to-end deep
learning fashion. As concrete applications of our SWD approximations, we design
two types of differentiable SWD blocks to equip modern generative
frameworks---Auto-Encoders (AE) and Generative Adversarial Networks (GAN). In
the experiments, we not only show the superiority of the proposed generative
models on standard image synthesis benchmarks, but also demonstrate the
state-of-the-art performance on challenging high resolution image and video
generation in an unsupervised manner.
On the Existence of a Sample Mean in Dynamic Time Warping Spaces
Authors: Brijnesh J. Jain, David Schultz
Date: 2016-10-14 00:00:00
Tasks: Clustering, Dynamic Time Warping

The concept of sample mean in dynamic time warping (DTW) spaces has been
successfully applied to improve pattern recognition systems and generalize
centroid-based clustering algorithms. Its existence has neither been proved nor
challenged. This article presents sufficient conditions for existence of a
sample mean in DTW spaces. The proposed result justifies prior work on
approximate mean algorithms, sets the stage for constructing exact mean
algorithms, and is a first step towards a statistical theory of DTW spaces.
Automatic Translating between Ancient Chinese and Contemporary Chinese with Limited Aligned Corpora
Authors: Zhiyuan Zhang, Wei Li, Qi Su
Date: 2018-03-05 00:00:00
Tasks: Sentence, Translation

The Chinese language has evolved a lot during the long-term development. Therefore, native speakers now have trouble in reading sentences written in ancient Chinese. In this paper, we propose to build an end-to-end neural model to automatically translate between ancient and contemporary Chinese. However, the existing ancient-contemporary Chinese parallel corpora are not aligned at the sentence level and sentence-aligned corpora are limited, which makes it difficult to train the model. To build the sentence level parallel training data for the model, we propose an unsupervised algorithm that constructs sentence-aligned ancient-contemporary pairs by using the fact that the aligned sentence pair shares many of the tokens. Based on the aligned corpus, we propose an end-to-end neural model with copying mechanism and local attention to translate between ancient and contemporary Chinese. Experiments show that the proposed unsupervised algorithm achieves 99.4% F1 score for sentence alignment, and the translation model achieves 26.95 BLEU from ancient to contemporary, and 36.34 BLEU from contemporary to ancient.
Beyond Context: Exploring Semantic Similarity for Tiny Face Detection
Authors: Yue Xi, Jiangbin Zheng, Xiangjian He, Wenjing Jia, Hanhui Li
Date: 2018-03-05 00:00:00
Tasks: Face Detection, Metric Learning, Semantic Similarity, Semantic Textual Similarity

Tiny face detection aims to find faces with high degrees of variability in
scale, resolution and occlusion in cluttered scenes. Due to the very little
information available on tiny faces, it is not sufficient to detect them merely
based on the information presented inside the tiny bounding boxes or their
context. In this paper, we propose to exploit the semantic similarity among all
predicted targets in each image to boost current face detectors. To this end,
we present a novel framework to model semantic similarity as pairwise
constraints within the metric learning scheme, and then refine our predictions
with the semantic similarity by utilizing the graph cut techniques. Experiments
conducted on three widely-used benchmark datasets have demonstrated the
improvement over the-state-of-the-arts gained by applying this idea.
Relocalization, Global Optimization and Map Merging for Monocular Visual-Inertial SLAM
Authors: Tong Qin, Perliang Li, Shaojie Shen
Date: 2018-03-05 00:00:00
Tasks: global-optimization, Pose Estimation, State Estimation

The monocular visual-inertial system (VINS), which consists one camera and
one low-cost inertial measurement unit (IMU), is a popular approach to achieve
accurate 6-DOF state estimation. However, such locally accurate visual-inertial
odometry is prone to drift and cannot provide absolute pose estimation.
Leveraging history information to relocalize and correct drift has become a hot
topic. In this paper, we propose a monocular visual-inertial SLAM system, which
can relocalize camera and get the absolute pose in a previous-built map. Then
4-DOF pose graph optimization is performed to correct drifts and achieve global
consistent. The 4-DOF contains x, y, z, and yaw angle, which is the actual
drifted direction in the visual-inertial system. Furthermore, the proposed
system can reuse a map by saving and loading it in an efficient way. Current
map and previous map can be merged together by the global pose graph
optimization. We validate the accuracy of our system on public datasets and
compare against other state-of-the-art algorithms. We also evaluate the map
merging ability of our system in the large-scale outdoor environment. The
source code of map reuse is integrated into our public code, VINS-Mono.
Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect
Authors: Xiang Wei, Boqing Gong, Zixia Liu, Wei Lu, Liqiang Wang
Date: 2018-03-05 00:00:00
Proceeding: ICLR 2018 1

Despite being impactful on a variety of problems and applications, the
generative adversarial nets (GANs) are remarkably difficult to train. This
issue is formally analyzed by \cite{arjovsky2017towards}, who also propose an
alternative direction to avoid the caveats in the minmax two-player training of
GANs. The corresponding algorithm, called Wasserstein GAN (WGAN), hinges on the
1-Lipschitz continuity of the discriminator. In this paper, we propose a novel
approach to enforcing the Lipschitz continuity in the training procedure of
WGANs. Our approach seamlessly connects WGAN with one of the recent
semi-supervised learning methods. As a result, it gives rise to not only better
photo-realistic samples than the previous methods but also state-of-the-art
semi-supervised learning results. In particular, our approach gives rise to the
inception score of more than 5.0 with only 1,000 CIFAR-10 images and is the
first that exceeds the accuracy of 90% on the CIFAR-10 dataset using only 4,000
labeled images, to the best of our knowledge.
Relative Pairwise Relationship Constrained Non-negative Matrix Factorisation
Authors: Shuai Jiang, Kan Li, Richard Yida Xu
Date: 2018-03-05 00:00:00
Tasks: Clustering, Image Clustering, Recommendation Systems, Triplet

Non-negative Matrix Factorisation (NMF) has been extensively used in machine
learning and data analytics applications. Most existing variations of NMF only
consider how each row/column vector of factorised matrices should be shaped,
and ignore the relationship among pairwise rows or columns. In many cases, such
pairwise relationship enables better factorisation, for example, image
clustering and recommender systems. In this paper, we propose an algorithm
named, Relative Pairwise Relationship constrained Non-negative Matrix
Factorisation (RPR-NMF), which places constraints over relative pairwise
distances amongst features by imposing penalties in a triplet form. Two
distance measures, squared Euclidean distance and Symmetric divergence, are
used, and exponential and hinge loss penalties are adopted for the two measures
respectively. It is well known that the so-called "multiplicative update rules"
result in a much faster convergence than gradient descend for matrix
factorisation. However, applying such update rules to RPR-NMF and also proving
its convergence is not straightforward. Thus, we use reasonable approximations
to relax the complexity brought by the penalties, which are practically
verified. Experiments on both synthetic datasets and real datasets demonstrate
that our algorithms have advantages on gaining close approximation, satisfying
a high proportion of expected constraints, and achieving superior performance
compared with other algorithms.
Path Aggregation Network for Instance Segmentation
Authors: Shu Liu, Lu Qi, Haifang Qin, Jianping Shi, Jiaya Jia
Date: 2018-03-05 00:00:00
Proceeding: CVPR 2018 6
Tasks: Instance Segmentation, object-detection, Object Detection, Segmentation, Semantic Segmentation

The way that information propagates in neural networks is of great
importance. In this paper, we propose Path Aggregation Network (PANet) aiming
at boosting information flow in proposal-based instance segmentation framework.
Specifically, we enhance the entire feature hierarchy with accurate
localization signals in lower layers by bottom-up path augmentation, which
shortens the information path between lower layers and topmost feature. We
present adaptive feature pooling, which links feature grid and all feature
levels to make useful information in each feature level propagate directly to
following proposal subnetworks. A complementary branch capturing different
views for each proposal is created to further improve mask prediction. These
improvements are simple to implement, with subtle extra computational overhead.
Our PANet reaches the 1st place in the COCO 2017 Challenge Instance
Segmentation task and the 2nd place in Object Detection task without
large-batch training. It is also state-of-the-art on MVD and Cityscapes. Code
is available at https://github.com/ShuLiu1993/PANet
LSTD: A Low-Shot Transfer Detector for Object Detection
Authors: Hao Chen, Yali Wang, Guoyou Wang, Yu Qiao
Date: 2018-03-05 00:00:00
Tasks: Few-Shot Object Detection, Object, object-detection, Object Detection, Transfer Learning

Recent advances in object detection are mainly driven by deep learning with
large-scale detection benchmarks. However, the fully-annotated training set is
often limited for a target detection task, which may deteriorate the
performance of deep detectors. To address this challenge, we propose a novel
low-shot transfer detector (LSTD) in this paper, where we leverage rich
source-domain knowledge to construct an effective target-domain detector with
very few training examples. The main contributions are described as follows.
First, we design a flexible deep architecture of LSTD to alleviate transfer
difficulties in low-shot detection. This architecture can integrate the
advantages of both SSD and Faster RCNN in a unified deep framework. Second, we
introduce a novel regularized transfer learning framework for low-shot
detection, where the transfer knowledge (TK) and background depression (BD)
regularizations are proposed to leverage object knowledge respectively from
source and target domains, in order to further enhance fine-tuning with a few
target images. Finally, we examine our LSTD on a number of challenging low-shot
detection experiments, where LSTD outperforms other state-of-the-art
approaches. The results demonstrate that LSTD is a preferable deep detector for
low-shot scenarios.
Blind Channel Equalization using Variational Autoencoders
Authors: Avi Caciularu, David Burshtein
Date: 2018-03-05 00:00:00

A new maximum likelihood estimation approach for blind channel equalization,
using variational autoencoders (VAEs), is introduced. Significant and
consistent improvements in the error rate of the reconstructed symbols,
compared to constant modulus equalizers, are demonstrated. In fact, for the
channels that were examined, the performance of the new VAE blind channel
equalizer was close to the performance of a nonblind adaptive linear minimum
mean square error equalizer. The new equalization method enables a
significantly lower latency channel acquisition compared to the constant
modulus algorithm (CMA). The VAE uses a convolutional neural network with two
layers and a very small number of free parameters. Although the computational
complexity of the new equalizer is higher compared to CMA, it is still
reasonable, and the number of free parameters to estimate is small.
A new stereo formulation not using pixel and disparity models
Authors: Kiyoshi Oguri, Yuichiro Shibata
Date: 2018-03-05 00:00:00
Tasks: GPU

We introduce a new stereo formulation which does not use pixel and disparity
models. Many problems in vision are treated as assigning each pixel a label.
Disparities are labels for stereo. Such pixel-labeling problems are naturally
represented in terms of energy minimization, where the energy function has two
terms: one term penalizes solutions that inconsistent with the observed data,
the other term enforces spatial smoothness. Graph cuts are one of the effi-
cient methods for solving energy minimization. However, exact minimization of
multi labeling problems can be performed by graph cuts only for the case with
convex smoothness terms. In pixel-disparity formulation, convex smoothness
terms do not generate well reconstructed 3D results. Thus, truncated linear or
quadratic smoothness terms, etc. are used, where approximate energy
minimization is necessary. In this paper, we introduce a new site-labeling
formulation, where the sites are not pixels but lines in 3D space, labels are
not disparities but depth numbers. For this formulation, visibility reasoning
is naturally included in the energy function. In addition, this formulation
allows us to use a small smoothness term, which does not affect the 3D results
much. This makes the optimization step very simple, so we could develop an
approximation method for graph cut itself (not for energy minimization) and a
high performance GPU graph cut program. For Tsukuba stereo pair in Middlebury
data set, we got the result in 5ms using GTX1080GPU, 19ms using GTX660GPU.
Cross-Paced Representation Learning with Partial Curricula for Sketch-based Image Retrieval
Authors: Dan Xu, Xavier Alameda-Pineda, Jingkuan Song, Elisa Ricci, Nicu Sebe
Date: 2018-03-05 00:00:00
Tasks: Image Retrieval, Representation Learning, Retrieval, Sketch-Based Image Retrieval

In this paper we address the problem of learning robust cross-domain
representations for sketch-based image retrieval (SBIR). While most SBIR
approaches focus on extracting low- and mid-level descriptors for direct
feature matching, recent works have shown the benefit of learning coupled
feature representations to describe data from two related sources. However,
cross-domain representation learning methods are typically cast into non-convex
minimization problems that are difficult to optimize, leading to unsatisfactory
performance. Inspired by self-paced learning, a learning methodology designed
to overcome convergence issues related to local optima by exploiting the
samples in a meaningful order (i.e. easy to hard), we introduce the cross-paced
partial curriculum learning (CPPCL) framework. Compared with existing
self-paced learning methods which only consider a single modality and cannot
deal with prior knowledge, CPPCL is specifically designed to assess the
learning pace by jointly handling data from dual sources and modality-specific
prior information provided in the form of partial curricula. Additionally,
thanks to the learned dictionaries, we demonstrate that the proposed CPPCL
embeds robust coupled representations for SBIR. Our approach is extensively
evaluated on four publicly available datasets (i.e. CUFS, Flickr15K, QueenMary
SBIR and TU-Berlin Extension datasets), showing superior performance over
competing SBIR methods.
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
Authors: Dong Yin, Yudong Chen, Kannan Ramchandran, Peter Bartlett
Date: 2018-03-05 00:00:00
Proceeding: ICML 2018 7

In large-scale distributed learning, security issues have become increasingly important. Particularly in a decentralized environment, some computing units may behave abnormally, or even exhibit Byzantine failures -- arbitrary and potentially adversarial behavior. In this paper, we develop distributed learning algorithms that are provably robust against such failures, with a focus on achieving optimal statistical performance. A main result of this work is a sharp analysis of two robust distributed gradient descent algorithms based on median and trimmed mean operations, respectively. We prove statistical error rates for three kinds of population loss functions: strongly convex, non-strongly convex, and smooth non-convex. In particular, these algorithms are shown to achieve order-optimal statistical error rates for strongly convex losses. To achieve better communication efficiency, we further propose a median-based distributed algorithm that is provably robust, and uses only one communication round. For strongly convex quadratic loss, we show that this algorithm achieves the same optimal error rate as the robust distributed gradient descent algorithms.
Recurrent Predictive State Policy Networks
Authors: Ahmed Hefny, Zita Marinho, Wen Sun, Siddhartha Srinivasa, Geoffrey Gordon
Date: 2018-03-05 00:00:00
Proceeding: ICML 2018 7
Tasks: OpenAI Gym, Reinforcement Learning

We introduce Recurrent Predictive State Policy (RPSP) networks, a recurrent
architecture that brings insights from predictive state representations to
reinforcement learning in partially observable environments. Predictive state
policy networks consist of a recursive filter, which keeps track of a belief
about the state of the environment, and a reactive policy that directly maps
beliefs to actions, to maximize the cumulative reward. The recursive filter
leverages predictive state representations (PSRs) (Rosencrantz and Gordon,
2004; Sun et al., 2016) by modeling predictive state-- a prediction of the
distribution of future observations conditioned on history and future actions.
This representation gives rise to a rich class of statistically consistent
algorithms (Hefny et al., 2018) to initialize the recursive filter. Predictive
state serves as an equivalent representation of a belief state. Therefore, the
policy component of the RPSP-network can be purely reactive, simplifying
training while still allowing optimal behaviour. Moreover, we use the PSR
interpretation during training as well, by incorporating prediction error in
the loss function. The entire network (recursive filter and reactive policy) is
still differentiable and can be trained using gradient based methods. We
optimize our policy using a combination of policy gradient based on rewards
(Williams, 1992) and gradient descent based on prediction error. We show the
efficacy of RPSP-networks under partial observability on a set of robotic
control tasks from OpenAI Gym. We empirically show that RPSP-networks perform
well compared with memory-preserving networks such as GRUs, as well as finite
memory models, being the overall best performing method.
Modeling Recovery Curves With Application to Prostatectomy
Authors: Fulton Wang, Tyler H. McCormick, Cynthia Rudin, John Gore
Date: 2015-04-27 00:00:00

We propose a Bayesian model that predicts recovery curves based on
information available before the disruptive event. A recovery curve of interest
is the quantified sexual function of prostate cancer patients after
prostatectomy surgery. We illustrate the utility of our model as a
pre-treatment medical decision aid, producing personalized predictions that are
both interpretable and accurate. We uncover covariate relationships that agree
with and supplement that in existing medical literature.
Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization Perspective
Authors: Junliang Guo, Linli Xu, Xunpeng Huang, Enhong Chen
Date: 2017-11-11 00:00:00
Tasks: Link Prediction, Network Embedding, Node Classification

Recent advances in the field of network embedding have shown the
low-dimensional network representation is playing a critical role in network
analysis. However, most of the existing principles of network embedding do not
incorporate auxiliary information such as content and labels of nodes flexibly.
In this paper, we take a matrix factorization perspective of network embedding,
and incorporate structure, content and label information of the network
simultaneously. For structure, we validate that the matrix we construct
preserves high-order proximities of the network. Label information can be
further integrated into the matrix via the process of random walk sampling to
enhance the quality of embedding in an unsupervised manner, i.e., without
leveraging downstream classifiers. In addition, we generalize the Skip-Gram
Negative Sampling model to integrate the content of the network in a matrix
factorization framework. As a consequence, network embedding can be learned in
a unified framework integrating network structure and node content as well as
label information simultaneously. We demonstrate the efficacy of the proposed
model with the tasks of semi-supervised node classification and link prediction
on a variety of real-world benchmark network datasets.