pwc-archive/papers-with-abstracts huggingface dataset:
Neural Empirical Bayes
Authors: Saeed Saremi, Aapo Hyvarinen
Date: 2019-03-06 00:00:00
Tasks: Denoising, Density Estimation

We unify $\textit{kernel density estimation}$ and $\textit{empirical Bayes}$ and address a set of problems in unsupervised learning with a geometric interpretation of those methods, rooted in the $\textit{concentration of measure}$ phenomenon. Kernel density is viewed symbolically as $X\rightharpoonup Y$ where the random variable $X$ is smoothed to $Y= X+N(0,\sigma^2 I_d)$, and empirical Bayes is the machinery to denoise in a least-squares sense, which we express as $X \leftharpoondown Y$. A learning objective is derived by combining these two, symbolically captured by $X \rightleftharpoons Y$. Crucially, instead of using the original nonparametric estimators, we parametrize $\textit{the energy function}$ with a neural network denoted by $\phi$; at optimality, $\nabla \phi \approx -\nabla \log f$ where $f$ is the density of $Y$. The optimization problem is abstracted as interactions of high-dimensional spheres which emerge due to the concentration of isotropic gaussians. We introduce two algorithmic frameworks based on this machinery: (i) a "walk-jump" sampling scheme that combines Langevin MCMC (walks) and empirical Bayes (jumps), and (ii) a probabilistic framework for $\textit{associative memory}$, called NEBULA, defined \`{a} la Hopfield by the $\textit{gradient flow}$ of the learned energy to a set of attractors. We finish the paper by reporting the emergence of very rich "creative memories" as attractors of NEBULA for highly-overlapping spheres.
A heuristic approach for lactate threshold estimation for training decision-making: An accessible and easy to use solution for recreational runners
Authors: U. Etxegarai, E. Portillo, J. Irazusta, L. A. Koefoed, N. Kasabov
Date: 2019-03-06 00:00:00
Tasks: Decision Making

In this work, a heuristic as operational tool to estimate the lactate
threshold and to facilitate its integration into the training process of
recreational runners is proposed. To do so, we formalize the principles for the
lactate threshold estimation from empirical data and an iterative methodology
that enables experience based learning. This strategy arises as a robust and
adaptive approach to solve data analysis problems. We compare the results of
the heuristic with the most commonly used protocol by making a first
quantitative error analysis to show its reliability. Additionally, we provide a
computational algorithm so that this quantitative analysis can be easily
performed in other lactate threshold protocols. With this work, we have shown
that a heuristic %60 of 'endurance running speed reserve', serves for the same
purpose of the most commonly used protocol in recreational runners, but
improving its operational limitations of accessibility and consistent use.
Learning from Higher-Layer Feature Visualizations
Authors: Konstantinos Nikolaidis, Stein Kristiansen, Vera Goebel, Thomas Plagemann
Date: 2019-03-06 00:00:00
Tasks: Transfer Learning

Driven by the goal to enable sleep apnea monitoring and machine
learning-based detection at home with small mobile devices, we investigate
whether interpretation-based indirect knowledge transfer can be used to create
classifiers with acceptable performance. Interpretation-based indirect
knowledge transfer means that a classifier (student) learns from a synthetic
dataset based on the knowledge representation from an already trained Deep
Network (teacher). We use activation maximization to generate visualizations
and create a synthetic dataset to train the student classifier. This approach
has the advantage that student classifiers can be trained without access to the
original training data. With experiments we investigate the feasibility of
interpretation-based indirect knowledge transfer and its limitations. The
student achieves an accuracy of 97.8% on MNIST (teacher accuracy: 99.3%) with a
similar smaller architecture to that of the teacher. The student classifier
achieves an accuracy of 86.1% and 89.5% for a subset of the Apnea-ECG dataset
(teacher: 89.5% and 91.1%, respectively).
Video-based surgical skill assessment using 3D convolutional neural networks
Authors: Isabel Funke, Sören Torge Mees, Jürgen Weitz, Stefanie Speidel
Date: 2019-03-06 00:00:00
Tasks: Action Quality Assessment, Surgical Skills Evaluation, Video Classification

Purpose: A profound education of novice surgeons is crucial to ensure that surgical interventions are effective and safe. One important aspect is the teaching of technical skills for minimally invasive or robot-assisted procedures. This includes the objective and preferably automatic assessment of surgical skill. Recent studies presented good results for automatic, objective skill evaluation by collecting and analyzing motion data such as trajectories of surgical instruments. However, obtaining the motion data generally requires additional equipment for instrument tracking or the availability of a robotic surgery system to capture kinematic data. In contrast, we investigate a method for automatic, objective skill assessment that requires video data only. This has the advantage that video can be collected effortlessly during minimally invasive and robot-assisted training scenarios. Methods: Our method builds on recent advances in deep learning-based video classification. Specifically, we propose to use an inflated 3D ConvNet to classify snippets, i.e., stacks of a few consecutive frames, extracted from surgical video. The network is extended into a Temporal Segment Network during training. Results: We evaluate the method on the publicly available JIGSAWS dataset, which consists of recordings of basic robot-assisted surgery tasks performed on a dry lab bench-top model. Our approach achieves high skill classification accuracies ranging from 95.1% to 100.0%. Conclusions: Our results demonstrate the feasibility of deep learning-based assessment of technical skill from surgical video. Notably, the 3D ConvNet is able to learn meaningful patterns directly from the data, alleviating the need for manual feature engineering. Further evaluation will require more annotated data for training and testing.
Causal Discovery Toolbox: Uncover causal relationships in Python
Authors: Diviyan Kalainathan, Olivier Goudet
Date: 2019-03-06 00:00:00
Tasks: Causal Discovery

This paper presents a new open source Python framework for causal discovery
from observational data and domain background knowledge, aimed at causal graph
and causal mechanism modeling. The 'cdt' package implements the end-to-end
approach, recovering the direct dependencies (the skeleton of the causal graph)
and the causal relationships between variables. It includes algorithms from the
'Bnlearn' and 'Pcalg' packages, together with algorithms for pairwise causal
discovery such as ANM. 'cdt' is available under the MIT License at
https://github.com/Diviyan-Kalainathan/CausalDiscoveryToolbox.
High-Fidelity Image Generation With Fewer Labels
Authors: Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly
Date: 2019-03-06 00:00:00
Tasks: Conditional Image Generation, Image Generation, Vocal Bursts Intensity Prediction

Deep generative models are becoming a cornerstone of modern machine learning. Recent work on conditional generative adversarial networks has shown that learning complex, high-dimensional distributions over natural images is within reach. While the latest models are able to generate high-fidelity, diverse natural images at high resolution, they rely on a vast quantity of labeled data. In this work we demonstrate how one can benefit from recent work on self- and semi-supervised learning to outperform the state of the art on both unsupervised ImageNet synthesis, as well as in the conditional setting. In particular, the proposed approach is able to match the sample quality (as measured by FID) of the current state-of-the-art conditional model BigGAN on ImageNet using only 10% of the labels and outperform it using 20% of the labels.
Discourse Parsing in Videos: A Multi-modal Appraoch
Authors: Arjun R. Akula, Song-Chun Zhu
Date: 2019-03-06 00:00:00
Tasks: Discourse Parsing, Visual Dialog, Visual Storytelling

Text-level discourse parsing aims to unmask how two sentences in the text are related to each other. We propose the task of Visual Discourse Parsing, which requires understanding discourse relations among scenes in a video. Here we use the term scene to refer to a subset of video frames that can better summarize the video. In order to collect a dataset for learning discourse cues from videos, one needs to manually identify the scenes from a large pool of video frames and then annotate the discourse relations between them. This is clearly a time consuming, expensive and tedious task. In this work, we propose an approach to identify discourse cues from the videos without the need to explicitly identify and annotate the scenes. We also present a novel dataset containing 310 videos and the corresponding discourse cues to evaluate our approach. We believe that many of the multi-discipline AI problems such as Visual Dialog and Visual Storytelling would greatly benefit from the use of visual discourse cues.
Nonlinear input design as optimal control of a Hamiltonian system
Authors: Jack Umenberger, Thomas B. Schön
Date: 2019-03-06 00:00:00

We propose an input design method for a general class of parametric
probabilistic models, including nonlinear dynamical systems with process noise.
The goal of the procedure is to select inputs such that the parameter posterior
distribution concentrates about the true value of the parameters; however,
exact computation of the posterior is intractable. By representing (samples
from) the posterior as trajectories from a certain Hamiltonian system, we
transform the input design task into an optimal control problem. The method is
illustrated via numerical examples, including MRI pulse sequence design.
Distributed Policy Learning Based Random Access for Diversified QoS Requirements
Authors: Zhiyuan Jiang, Sheng Zhou, Zhisheng Niu
Date: 2019-03-06 00:00:00

Future wireless access networks need to support diversified quality of
service (QoS) metrics required by various types of Internet-of-Things (IoT)
devices, e.g., age of information (AoI) for status generating sources and ultra
low latency for safety information in vehicular networks. In this paper, a
novel inner-state driven random access (ISDA) framework is proposed based on
distributed policy learning, in particular a cross-entropy method. Conventional
random access schemes, e.g., $p$-CSMA, assume state-less terminals, and thus
assigning equal priorities to all. In ISDA, the inner-states of terminals are
described by a time-varying state vector, and the transmission probabilities of
terminals in the contention period are determined by their respective
inner-states. Neural networks are leveraged to approximate the function
mappings from inner-states to transmission probabilities, and an iterative
approach is adopted to improve these mappings in a distributed manner.
Experiment results show that ISDA can improve the QoS of heterogeneous
terminals simultaneously compared to conventional CSMA schemes.
Efficient Deep Neural Network for Photo-realistic Image Super-Resolution
Authors: Namhyuk Ahn, Byungkon Kang, Kyung-Ah Sohn
Date: 2019-03-06 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Recent progress in deep learning-based models has improved photo-realistic (or perceptual) single-image super-resolution significantly. However, despite their powerful performance, many methods are difficult to apply to real-world applications because of the heavy computational requirements. To facilitate the use of a deep model under such demands, we focus on keeping the network efficient while maintaining its performance. In detail, we design an architecture that implements a cascading mechanism on a residual network to boost the performance with limited resources via multi-level feature fusion. In addition, our proposed model adopts group convolution and recursive schemes in order to achieve extreme efficiency. We further improve the perceptual quality of the output by employing the adversarial learning paradigm and a multi-scale discriminator approach. The performance of our method is investigated through extensive internal experiments and benchmarks using various datasets. Our results show that our models outperform the recent methods with similar complexity, for both traditional pixel-based and perception-based tasks.
Positively Scale-Invariant Flatness of ReLU Neural Networks
Authors: Mingyang Yi, Qi Meng, Wei Chen, Zhi-Ming Ma, Tie-Yan Liu
Date: 2019-03-06 00:00:00

It was empirically confirmed by Keskar et al.\cite{SharpMinima} that flatter
minima generalize better. However, for the popular ReLU network, sharp minimum
can also generalize well \cite{SharpMinimacan}. The conclusion demonstrates
that the existing definitions of flatness fail to account for the complex
geometry of ReLU neural networks because they can't cover the Positively
Scale-Invariant (PSI) property of ReLU network. In this paper, we formalize the
PSI causes problem of existing definitions of flatness and propose a new
description of flatness - \emph{PSI-flatness}. PSI-flatness is defined on the
values of basis paths \cite{GSGD} instead of weights. Values of basis paths
have been shown to be the PSI-variables and can sufficiently represent the ReLU
neural networks which ensure the PSI property of PSI-flatness. Then we study
the relation between PSI-flatness and generalization theoretically and
empirically. First, we formulate a generalization bound based on PSI-flatness
which shows generalization error decreasing with the ratio between the largest
basis path value and the smallest basis path value. That is to say, the minimum
with balanced values of basis paths will more likely to be flatter and
generalize better. Finally. we visualize the PSI-flatness of loss surface
around two learned models which indicates the minimum with smaller PSI-flatness
can indeed generalize better.
Characterizing Human Behaviours Using Statistical Motion Descriptor
Authors: Eissa Jaber Alreshidi, Mohammad Bilal
Date: 2019-03-06 00:00:00
Tasks: Optical Flow Estimation

Identifying human behaviors is a challenging research problem due to the
complexity and variation of appearances and postures, the variation of camera
settings, and view angles. In this paper, we try to address the problem of
human behavior identification by introducing a novel motion descriptor based on
statistical features. The method first divide the video into N number of
temporal segments. Then for each segment, we compute dense optical flow, which
provides instantaneous velocity information for all the pixels. We then compute
Histogram of Optical Flow (HOOF) weighted by the norm and quantized into 32
bins. We then compute statistical features from the obtained HOOF forming a
descriptor vector of 192- dimensions. We then train a non-linear multi-class
SVM that classify different human behaviors with the accuracy of 72.1%. We
evaluate our method by using publicly available human action data set.
Experimental results shows that our proposed method out performs state of the
art methods.
Robust Video Background Identification by Dominant Rigid Motion Estimation
Authors: Kaimo Lin, Nianjuan Jiang, Loong Fah Cheong, Jiangbo Lu, Xun Xu
Date: 2019-03-06 00:00:00
Tasks: Motion Estimation, Motion Segmentation, Segmentation, Video Stabilization

The ability to identify the static background in videos captured by a moving
camera is an important pre-requisite for many video applications (e.g. video
stabilization, stitching, and segmentation). Existing methods usually face
difficulties when the foreground objects occupy a larger area than the
background in the image. Many methods also cannot scale up to handle densely
sampled feature trajectories. In this paper, we propose an efficient
local-to-global method to identify background, based on the assumption that as
long as there is sufficient camera motion, the cumulative background features
will have the largest amount of trajectories. Our motion model at the two-frame
level is based on the epipolar geometry so that there will be no
over-segmentation problem, another issue that plagues the 2D motion
segmentation approach. Foreground objects erroneously labelled due to
intermittent motions are also taken care of by checking their global
consistency with the final estimated background motion. Lastly, by virtue of
its efficiency, our method can deal with densely sampled trajectories. It
outperforms several state-of-the-art motion segmentation methods on public
datasets, both quantitatively and qualitatively.
Dixit: Interactive Visual Storytelling via Term Manipulation
Authors: Chao-Chun Hsu, Yu-Hua Chen, Zi-Yuan Chen, Hsin-Yu Lin, Ting-Hao 'Kenneth' Huang, Lun-Wei Ku
Date: 2019-03-06 00:00:00
Tasks: Decoder, Visual Storytelling

In this paper, we introduce Dixit, an interactive visual storytelling system that the user interacts with iteratively to compose a short story for a photo sequence. The user initiates the process by uploading a sequence of photos. Dixit first extracts text terms from each photo which describe the objects (e.g., boy, bike) or actions (e.g., sleep) in the photo, and then allows the user to add new terms or remove existing terms. Dixit then generates a short story based on these terms. Behind the scenes, Dixit uses an LSTM-based model trained on image caption data and FrameNet to distill terms from each image and utilizes a transformer decoder to compose a context-coherent story. Users change images or terms iteratively with Dixit to create the most ideal story. Dixit also allows users to manually edit and rate stories. The proposed procedure opens up possibilities for interpretable and controllable visual storytelling, allowing users to understand the story formation rationale and to intervene in the generation process.
DepthwiseGANs: Fast Training Generative Adversarial Networks for Realistic Image Synthesis
Authors: Mkhuseli Ngxande, Jules-Raymond Tapamo, Michael Burke
Date: 2019-03-06 00:00:00
Tasks: Image Generation, Super-Resolution, Synthetic Data Generation

Recent work has shown significant progress in the direction of synthetic data
generation using Generative Adversarial Networks (GANs). GANs have been applied
in many fields of computer vision including text-to-image conversion, domain
transfer, super-resolution, and image-to-video applications. In computer
vision, traditional GANs are based on deep convolutional neural networks.
However, deep convolutional neural networks can require extensive computational
resources because they are based on multiple operations performed by
convolutional layers, which can consist of millions of trainable parameters.
Training a GAN model can be difficult and it takes a significant amount of time
to reach an equilibrium point. In this paper, we investigate the use of
depthwise separable convolutions to reduce training time while maintaining data
generation performance. Our results show that a DepthwiseGAN architecture can
generate realistic images in shorter training periods when compared to a
StarGan architecture, but that model capacity still plays a significant role in
generative modelling. In addition, we show that depthwise separable
convolutions perform best when only applied to the generator. For quality
evaluation of generated images, we use the Fr\'echet Inception Distance (FID),
which compares the similarity between the generated image distribution and that
of the training dataset.
Training in Task Space to Speed Up and Guide Reinforcement Learning
Authors: Guillaume Bellegarda, Katie Byl
Date: 2019-03-06 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent breakthroughs in the reinforcement learning (RL) community have made
significant advances towards learning and deploying policies on real world
robotic systems. However, even with the current state-of-the-art algorithms and
computational resources, these algorithms are still plagued with high sample
complexity, and thus long training times, especially for high degree of freedom
(DOF) systems. There are also concerns arising from lack of perceived stability
or robustness guarantees from emerging policies. This paper aims at mitigating
these drawbacks by: (1) modeling a complex, high DOF system with a
representative simple one, (2) making explicit use of forward and inverse
kinematics without forcing the RL algorithm to "learn" them on its own, and (3)
learning locomotion policies in Cartesian space instead of joint space. In this
paper these methods are applied to JPL's Robosimian, but can be readily used on
any system with a base and end effector(s). These locomotion policies can be
produced in just a few minutes, trained on a single laptop. We compare the
robustness of the resulting learned policies to those of other control methods.
An accompanying video for this paper can be found at
https://youtu.be/xDxxSw5ahnc .
Transfer feature generating networks with semantic classes structure for zero-shot learning
Authors: Guangfeng Lin, Wanjun Chen, Kaiyang Liao, Xiaobing Kang, Caixia Fan
Date: 2019-03-06 00:00:00
Tasks: General Classification, Zero-Shot Learning

Feature generating networks face to the most important question, which is the fitting difference (inconsistence) of the distribution between the generated feature and the real data. This inconsistence further influence the performance of the networks model, because training samples from seen classes is disjointed with testing samples from unseen classes in zero-shot learning (ZSL). In generalization zero-shot learning (GZSL), testing samples come from not only seen classes but also unseen classes for closer to the practical situation. Therefore, most of feature generating networks difficultly obtain satisfactory performance for the challenging GZSL by adversarial learning the distribution of semantic classes. To alleviate the negative influence of this inconsistence for ZSL and GZSL, transfer feature generating networks with semantic classes structure (TFGNSCS) is proposed to construct networks model for improving the performance of ZSL and GZSL. TFGNSCS can not only consider the semantic structure relationship between seen and unseen classes, but also learn the difference of generating features by transferring classification model information from seen to unseen classes in networks. The proposed method can integrate the transfer loss, the classification loss and the Wasserstein distance loss to generate enough CNN features, on which softmax classifiers are trained for ZSL and GZSL. Experiments demonstrate that the performance of TFGNSCS outperforms that of the state of the arts on four challenging datasets, which are CUB,FLO,SUN, AWA in GZSL.
Proving the NP-completeness of optimal moral graph triangulation
Authors: Yang Li, Lloyd Allison, Kevin Korb
Date: 2019-03-06 00:00:00

Moral graphs were introduced in the 1980s as an intermediate step when
transforming a Bayesian network to a junction tree, on which exact belief
propagation can be efficiently done. The moral graph of a Bayesian network can
be trivially obtained by connecting non-adjacent parents for each node in the
Bayesian network and dropping the direction of each edge. Perhaps because the
moralization process looks simple, there has been little attention on the
properties of moral graphs and their impact in belief propagation on Bayesian
networks. This paper addresses the mistaken claim that it has been previously
proved that optimal moral graph triangulation with the constraints of minimum
fill-in, treewidth or total states is NP-complete. The problems are in fact
NP-complete, but they have not previously been proved. We now prove these.