pwc-archive/papers-with-abstracts huggingface dataset:
Nonlinear Regression without i.i.d. Assumption
Authors: Qing Xu, Xiaohua Xuan
Date: 2018-11-23 00:00:00
Tasks: BIG-bench Machine Learning, regression

In this paper, we consider a class of nonlinear regression problems without
the assumption of being independent and identically distributed. We propose a
correspondent mini-max problem for nonlinear regression and give a numerical
algorithm. Such an algorithm can be applied in regression and machine learning
problems, and yield better results than traditional least square and machine
learning methods.
Application of Machine Learning in Fiber Nonlinearity Modeling and Monitoring for Elastic Optical Networks
Authors: Qunbi Zhuge, Xiaobo Zeng, Huazhi Lun, Meng Cai, Xiaomin Liu, Weisheng Hu
Date: 2018-11-23 00:00:00
Tasks: BIG-bench Machine Learning

Fiber nonlinear interference (NLI) modeling and monitoring are the key building blocks to support elastic optical networks (EONs). In the past, they were normally developed and investigated separately. Moreover, the accuracy of the previously proposed methods still needs to be improved for heterogenous dynamic optical networks. In this paper, we present the application of machine learning (ML) in NLI modeling and monitoring. In particular, we first propose to use ML approaches to calibrate the errors of current fiber nonlinearity models. The Gaussian-noise (GN) model is used as an illustrative example, and significant improvement is demonstrated with the aid of an artificial neural network (ANN). Further, we propose to use ML to combine the modeling and monitoring schemes for a better estimation of NLI variance. The following contents are the listed errors as mentioned in the comments for reasons of withdrawal. (1) The works, as mentioned as the title, should be addressed is about the elastic optical networks(EON), however, the simulation setup and the results section are focused on the conventional wavelength division multiplexing(WDM) networks. This error may confuse some researcher, getting the misleading decision for the researches about the elastic optical networks. (2) There exists some errors in the results rection, such as, Fig.9(b) and (c) with the wrong captions may result in misleading decision. (3) The split-step-Fourier-method(SSFM) presents good accuracy if the sufficiently small steps are adopted in the calculation, however this paper has not necessary contents and efforts to optimise the step-length of SSFM. This error may confuse the accuracy of simulation results. Therefore, we decide to withdraw this paper from arXiv. The correct and complete paper with the same title was published in journal of lightwave technology with doi: 10.1109/JLT.2019.2910143.
Facilitating the Manual Annotation of Sounds When Using Large Taxonomies
Authors: Xavier Favory, Eduardo Fonseca, Frederic Font, Xavier Serra
Date: 2018-11-21 00:00:00
Tasks: Information Retrieval, Retrieval

Properly annotated multimedia content is crucial for supporting advances in
many Information Retrieval applications. It enables, for instance, the
development of automatic tools for the annotation of large and diverse
multimedia collections. In the context of everyday sounds and online
collections, the content to describe is very diverse and involves many
different types of concepts, often organised in large hierarchical structures
called taxonomies. This makes the task of manually annotating content arduous.
In this paper, we present our user-centered development of two tools for the
manual annotation of audio content from a wide range of types. We conducted a
preliminary evaluation of functional prototypes involving real users. The goal
is to evaluate them in a real context, engage in discussions with users, and
inspire new ideas. A qualitative analysis was carried out including usability
questionnaires and semi-structured interviews. This revealed interesting
aspects to consider when developing tools for the manual annotation of audio
content with labels drawn from large hierarchical taxonomies.
Image Reconstruction with Predictive Filter Flow
Authors: Shu Kong, Charless Fowlkes
Date: 2018-11-28 00:00:00
Tasks: Deblurring, Denoising, Image Reconstruction, Image Super-Resolution, Lossy-Compression Artifact Reduction, Super-Resolution

We propose a simple, interpretable framework for solving a wide range of
image reconstruction problems such as denoising and deconvolution. Given a
corrupted input image, the model synthesizes a spatially varying linear filter
which, when applied to the input image, reconstructs the desired output. The
model parameters are learned using supervised or self-supervised training. We
test this model on three tasks: non-uniform motion blur removal,
lossy-compression artifact reduction and single image super resolution. We
demonstrate that our model substantially outperforms state-of-the-art methods
on all these tasks and is significantly faster than optimization-based
approaches to deconvolution. Unlike models that directly predict output pixel
values, the predicted filter flow is controllable and interpretable, which we
demonstrate by visualizing the space of predicted filters for different tasks.
Partial Convolution based Padding
Authors: Guilin Liu, Kevin J. Shih, Ting-Chun Wang, Fitsum A. Reda, Karan Sapra, Zhiding Yu, Andrew Tao, Bryan Catanzaro
Date: 2018-11-28 00:00:00
Tasks: General Classification, Semantic Segmentation

In this paper, we present a simple yet effective padding scheme that can be
used as a drop-in module for existing convolutional neural networks. We call it
partial convolution based padding, with the intuition that the padded region
can be treated as holes and the original input as non-holes. Specifically,
during the convolution operation, the convolution results are re-weighted near
image borders based on the ratios between the padded area and the convolution
sliding window area. Extensive experiments with various deep network models on
ImageNet classification and semantic segmentation demonstrate that the proposed
padding scheme consistently outperforms standard zero padding with better
accuracy.
Asymmetric evolutionary games with environmental feedback
Authors: Christoph Hauert, Camille Saade, Alex McAvoy
Date: 2018-07-04 00:00:00

Models in evolutionary game theory traditionally assume symmetric
interactions in homogeneous environments. Here, we consider populations
evolving in a heterogeneous environment, which consists of patches of different
qualities that are occupied by one individual each. The fitness of individuals
is not only determined by interactions with others but also by environmental
quality. This heterogeneity results in asymmetric interactions where the
characteristics of the interaction may depend on an individual's location.
Interestingly, in non-varying heterogeneous environments, the long-term
dynamics are the same as for symmetric interactions in an average, homogeneous
environment. However, introducing environmental feedback between an
individual's strategy and the quality of its patch results in rich
eco-evolutionary dynamics. Thus, individuals act as ecosystem engineers. The
nature of the feedback and the rate of ecological changes can relax or
aggravate social dilemmas and promote persistent periodic oscillations of
strategy abundance and environmental quality.
Robust universal neural vocoding
Authors: Jaime Lorenzo-Trueba, Thomas Drugman, Javier Latorre, Thomas Merritt, Bartosz Putrycz, Roberto Barra-Chicote
Date: 2018-11-15 00:00:00
Tasks: text-to-speech, Text to Speech

This paper introduces a robust universal neural vocoder trained with 74
speakers (comprised of both genders) coming from 17 languages. This vocoder is
shown to be capable of generating speech of consistently good quality (98%
relative mean MUSHRA when compared to natural speech) regardless of whether the
input spectrogram comes from a speaker, style or recording condition seen
during training or from an out-of-domain scenario.
  Together with the system, we present a full text-to-speech analysis of
robustness of a number of implemented systems. The complexity of systems tested
range from a convolutional neural networks-based system conditioned on
linguistics to a recurrent neural networks-based system conditioned on
mel-spectrograms. The analysis shows that convolutional neural networks-based
systems are prone to occasional instabilities, while the recurrent approaches
are significantly more stable and capable of providing universalizing
robustness.
3D human pose estimation in video with temporal convolutions and semi-supervised training
Authors: Dario Pavllo, Christoph Feichtenhofer, David Grangier, Michael Auli
Date: 2018-11-28 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Human Pose Estimation, Monocular 3D Human Pose Estimation, Pose Estimation, Position, Weakly-supervised 3D Human Pose Estimation

In this work, we demonstrate that 3D poses in video can be effectively
estimated with a fully convolutional model based on dilated temporal
convolutions over 2D keypoints. We also introduce back-projection, a simple and
effective semi-supervised training method that leverages unlabeled video data.
We start with predicted 2D keypoints for unlabeled video, then estimate 3D
poses and finally back-project to the input 2D keypoints. In the supervised
setting, our fully-convolutional model outperforms the previous best result
from the literature by 6 mm mean per-joint position error on Human3.6M,
corresponding to an error reduction of 11%, and the model also shows
significant improvements on HumanEva-I. Moreover, experiments with
back-projection show that it comfortably outperforms previous state-of-the-art
results in semi-supervised settings where labeled data is scarce. Code and
models are available at https://github.com/facebookresearch/VideoPose3D
CAPNet: Continuous Approximation Projection For 3D Point Cloud Reconstruction Using 2D Supervision
Authors: Navaneet K L, Priyanka Mandikal, Mayank Agarwal, R. Venkatesh Babu
Date: 2018-11-28 00:00:00
Tasks: 3D Point Cloud Reconstruction, Point cloud reconstruction

Knowledge of 3D properties of objects is a necessity in order to build
effective computer vision systems. However, lack of large scale 3D datasets can
be a major constraint for data-driven approaches in learning such properties.
We consider the task of single image 3D point cloud reconstruction, and aim to
utilize multiple foreground masks as our supervisory data to alleviate the need
for large scale 3D datasets. A novel differentiable projection module, called
'CAPNet', is introduced to obtain such 2D masks from a predicted 3D point
cloud. The key idea is to model the projections as a continuous approximation
of the points in the point cloud. To overcome the challenges of sparse
projection maps, we propose a loss formulation termed 'affinity loss' to
generate outlier-free reconstructions. We significantly outperform the existing
projection based approaches on a large-scale synthetic dataset. We show the
utility and generalizability of such a 2D supervised approach through
experiments on a real-world dataset, where lack of 3D data can be a serious
concern. To further enhance the reconstructions, we also propose a test stage
optimization procedure to obtain reconstructions that display high
correspondence with the observed input image.
SegET: Deep Neural Network with Rich Contextual Features for Cellular Structures Segmentation in Electron Tomography Image
Authors: Enze Zhang, Fa Zhang, Zhi-Yong Liu, Xiaohua Wan, Lifa Zhu
Date: 2018-11-28 00:00:00
Tasks: Electron Tomography, Segmentation, Semantic Segmentation

Electron tomography (ET) allows high-resolution reconstructions of
macromolecular complexes at nearnative state. Cellular structures segmentation
in the reconstruction data from electron tomographic images is often required
for analyzing and visualizing biological structures, making it a powerful tool
for quantitative descriptions of whole cell structures and understanding
biological functions. However, these cellular structures are rather difficult
to automatically separate or quantify from view owing to complex molecular
environment and the limitations of reconstruction data of ET. In this paper, we
propose a single end-to-end deep fully-convolutional semantic segmentation
network dubbed SegET with rich contextual features which fully exploitsthe
multi-scale and multi-level contextual information and reduces the loss of
details of cellular structures in ET images. We trained and evaluated our
network on the electron tomogram of the CTL Immunological Synapse from Cell
Image library. Our results demonstrate that SegET can automatically segment
accurately and outperform all other baseline methods on each individual
structure in our ET dataset.
Attributed Network Embedding for Incomplete Attributed Networks
Authors: Chengbin Hou, Shan He, Ke Tang
Date: 2018-11-28 00:00:00
Tasks: Attribute, Link Prediction, Network Embedding, Node Classification

Attributed networks are ubiquitous since a network often comes with auxiliary attribute information e.g. a social network with user profiles. Attributed Network Embedding (ANE) has recently attracted considerable attention, which aims to learn unified low dimensional node embeddings while preserving both structural and attribute information. The resulting node embeddings can then facilitate various network downstream tasks e.g. link prediction. Although there are several ANE methods, most of them cannot deal with incomplete attributed networks with missing links and/or missing node attributes, which often occur in real-world scenarios. To address this issue, we propose a robust ANE method, the general idea of which is to reconstruct a unified denser network by fusing two sources of information for information enhancement, and then employ a random walks based network embedding method for learning node embeddings. The experiments of link prediction, node classification, visualization, and parameter sensitivity analysis on six real-world datasets validate the effectiveness of our method to incomplete attributed networks.
Future-State Predicting LSTM for Early Surgery Type Recognition
Authors: Siddharth Kannan, Gaurav Yengera, Didier Mutter, Jacques Marescaux, Nicolas Padoy
Date: 2018-11-28 00:00:00
Tasks: Vocal Bursts Type Prediction

This work presents a novel approach for the early recognition of the type of a laparoscopic surgery from its video. Early recognition algorithms can be beneficial to the development of 'smart' OR systems that can provide automatic context-aware assistance, and also enable quick database indexing. The task is however ridden with challenges specific to videos belonging to the domain of laparoscopy, such as high visual similarity across surgeries and large variations in video durations. To capture the spatio-temporal dependencies in these videos, we choose as our model a combination of a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network. We then propose two complementary approaches for improving early recognition performance. The first approach is a CNN fine-tuning method that encourages surgeries to be distinguished based on the initial frames of laparoscopic videos. The second approach, referred to as 'Future-State Predicting LSTM', trains an LSTM to predict information related to future frames, which helps in distinguishing between the different types of surgeries. We evaluate our approaches on a large dataset of 425 laparoscopic videos containing 9 types of surgeries (Laparo425), and achieve on average an accuracy of 75% having observed only the first 10 minutes of a surgery. These results are quite promising from a practical standpoint and also encouraging for other types of image-guided surgeries.
CCNet: Criss-Cross Attention for Semantic Segmentation
Authors: Zilong Huang, Xinggang Wang, Yunchao Wei, Lichao Huang, Humphrey Shi, Wenyu Liu, Thomas S. Huang
Date: 2018-11-28 00:00:00
Proceeding: ICCV 2019 10
Tasks: Computational Efficiency, GPU, Human Parsing, Instance Segmentation, object-detection, Object Detection, Segmentation, Semantic Segmentation, Thermal Image Segmentation, Video Segmentation, Video Semantic Segmentation

Contextual information is vital in visual understanding problems, such as semantic segmentation and object detection. We propose a Criss-Cross Network (CCNet) for obtaining full-image contextual information in a very effective and efficient way. Concretely, for each pixel, a novel criss-cross attention module harvests the contextual information of all the pixels on its criss-cross path. By taking a further recurrent operation, each pixel can finally capture the full-image dependencies. Besides, a category consistent loss is proposed to enforce the criss-cross attention module to produce more discriminative features. Overall, CCNet is with the following merits: 1) GPU memory friendly. Compared with the non-local block, the proposed recurrent criss-cross attention module requires 11x less GPU memory usage. 2) High computational efficiency. The recurrent criss-cross attention significantly reduces FLOPs by about 85% of the non-local block. 3) The state-of-the-art performance. We conduct extensive experiments on semantic segmentation benchmarks including Cityscapes, ADE20K, human parsing benchmark LIP, instance segmentation benchmark COCO, video segmentation benchmark CamVid. In particular, our CCNet achieves the mIoU scores of 81.9%, 45.76% and 55.47% on the Cityscapes test set, the ADE20K validation set and the LIP validation set respectively, which are the new state-of-the-art results. The source codes are available at \url{https://github.com/speedinghzl/CCNet}.
Variational Selection of Features for Molecular Kinetics
Authors: Martin K. Scherer, Brooke E. Husic, Moritz Hoffmann, Fabian Paul, Hao Wu, Frank Noé
Date: 2018-11-28 00:00:00
Tasks: Model Selection

The modeling of atomistic biomolecular simulations using kinetic models such
as Markov state models (MSMs) has had many notable algorithmic advances in
recent years. The variational principle has opened the door for a nearly fully
automated toolkit for selecting models that predict the long-time kinetics from
molecular dynamics simulations. However, one yet-unoptimized step of the
pipeline involves choosing the features, or collective variables, from which
the model should be constructed. In order to build intuitive models, these
collective variables are often sought to be interpretable and familiar
features, such as torsional angles or contact distances in a protein structure.
However, previous approaches for evaluating the chosen features rely on
constructing a full MSM, which in turn requires additional hyperparameters to
be chosen, and hence leads to a computationally expensive framework. Here, we
present a method to optimize the feature choice directly, without requiring the
construction of the final kinetic model. We demonstrate our rigorous
preprocessing algorithm on a canonical set of twelve fast-folding protein
simulations, and show that our procedure leads to more efficient model
selection.
Neural probabilistic motor primitives for humanoid control
Authors: Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, Nicolas Heess
Date: 2018-11-28 00:00:00
Proceeding: ICLR 2019 5
Tasks: Humanoid Control

We focus on the problem of learning a single motor module that can flexibly
express a range of behaviors for the control of high-dimensional physically
simulated humanoids. To do this, we propose a motor architecture that has the
general structure of an inverse model with a latent-variable bottleneck. We
show that it is possible to train this model entirely offline to compress
thousands of expert policies and learn a motor primitive embedding space. The
trained neural probabilistic motor primitive system can perform one-shot
imitation of whole-body humanoid behaviors, robustly mimicking unseen
trajectories. Additionally, we demonstrate that it is also straightforward to
train controllers to reuse the learned motor primitive space to solve tasks,
and the resulting movements are relatively naturalistic. To support the
training of our model, we compare two approaches for offline policy cloning,
including an experience efficient method which we call linear feedback policy
cloning. We encourage readers to view a supplementary video (
https://youtu.be/CaDEf-QcKwA ) summarizing our results.
Few-Shot Generalization Across Dialogue Tasks
Authors: Vladimir Vlasov, Akela Drissner-Schmid, Alan Nichol
Date: 2018-11-28 00:00:00

Machine-learning based dialogue managers are able to learn complex behaviors
in order to complete a task, but it is not straightforward to extend their
capabilities to new domains. We investigate different policies' ability to
handle uncooperative user behavior, and how well expertise in completing one
task (such as restaurant reservations) can be reapplied when learning a new one
(e.g. booking a hotel). We introduce the Recurrent Embedding Dialogue Policy
(REDP), which embeds system actions and dialogue states in the same vector
space. REDP contains a memory component and attention mechanism based on a
modified Neural Turing Machine, and significantly outperforms a baseline LSTM
classifier on this task. We also show that both our architecture and baseline
solve the bAbI dialogue task, achieving 100% test accuracy.
An Adversarial Approach for Explainable AI in Intrusion Detection Systems
Authors: Daniel L. Marino, Chathurika S. Wickramasinghe, Milos Manic
Date: 2018-11-28 00:00:00
Tasks: BIG-bench Machine Learning, Intrusion Detection

Despite the growing popularity of modern machine learning techniques (e.g.
Deep Neural Networks) in cyber-security applications, most of these models are
perceived as a black-box for the user. Adversarial machine learning offers an
approach to increase our understanding of these models. In this paper we
present an approach to generate explanations for incorrect classifications made
by data-driven Intrusion Detection Systems (IDSs). An adversarial approach is
used to find the minimum modifications (of the input features) required to
correctly classify a given set of misclassified samples. The magnitude of such
modifications is used to visualize the most relevant features that explain the
reason for the misclassification. The presented methodology generated
satisfactory explanations that describe the reasoning behind the
mis-classifications, with descriptions that match expert knowledge. The
advantages of the presented methodology are: 1) applicable to any classifier
with defined gradients. 2) does not require any modification of the classifier
model. 3) can be extended to perform further diagnosis (e.g. vulnerability
assessment) and gain further understanding of the system. Experimental
evaluation was conducted on the NSL-KDD99 benchmark dataset using Linear and
Multilayer perceptron classifiers. The results are shown using intuitive
visualizations in order to improve the interpretability of the results.
Shared Representational Geometry Across Neural Networks
Authors: Qihong Lu, Po-Hsuan Chen, Jonathan W. Pillow, Peter J. Ramadge, Kenneth A. Norman, Uri Hasson
Date: 2018-11-28 00:00:00

Different neural networks trained on the same dataset often learn similar
input-output mappings with very different weights. Is there some correspondence
between these neural network solutions? For linear networks, it has been shown
that different instances of the same network architecture encode the same
representational similarity matrix, and their neural activity patterns are
connected by orthogonal transformations. However, it is unclear if this holds
for non-linear networks. Using a shared response model, we show that different
neural networks encode the same input examples as different orthogonal
transformations of an underlying shared representation. We test this claim
using both standard convolutional neural networks and residual networks on
CIFAR10 and CIFAR100.