pwc-archive/papers-with-abstracts huggingface dataset:
Learning State Representations in Complex Systems with Multimodal Data
Authors: Pavel Solovev, Vladimir Aliev, Pavel Ostyakov, Gleb Sterkin, Elizaveta Logacheva, Stepan Troeshestov, Roman Suvorov, Anton Mashikhin, Oleg Khomenko, Sergey I. Nikolenko
Date: 2018-11-27 00:00:00
Tasks: Anomaly Detection, Disentanglement, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

Representation learning becomes especially important for complex systems with
multimodal data sources such as cameras or sensors. Recent advances in
reinforcement learning and optimal control make it possible to design control
algorithms on these latent representations, but the field still lacks a
large-scale standard dataset for unified comparison. In this work, we present a
large-scale dataset and evaluation framework for representation learning for
the complex task of landing an airplane. We implement and compare several
approaches to representation learning on this dataset in terms of the quality
of simple supervised learning tasks and disentanglement scores. The resulting
representations can be used for further tasks such as anomaly detection,
optimal control, model-based reinforcement learning, and other applications.
Combining Deep Learning and Qualitative Spatial Reasoning to Learn Complex Structures from Sparse Examples with Noise
Authors: Nikhil Krishnaswamy, Scott Friedman, James Pustejovsky
Date: 2018-11-27 00:00:00
Tasks: AI Agent, Heuristic Search, Spatial Reasoning

Many modern machine learning approaches require vast amounts of training data
to learn new concepts; conversely, human learning often requires few
examples--sometimes only one--from which the learner can abstract structural
concepts. We present a novel approach to introducing new spatial structures to
an AI agent, combining deep learning over qualitative spatial relations with
various heuristic search algorithms. The agent extracts spatial relations from
a sparse set of noisy examples of block-based structures, and trains
convolutional and sequential models of those relation sets. To create novel
examples of similar structures, the agent begins placing blocks on a virtual
table, uses a CNN to predict the most similar complete example structure after
each placement, an LSTM to predict the most likely set of remaining moves
needed to complete it, and recommends one using heuristic search. We verify
that the agent learned the concept by observing its virtual block-building
activities, wherein it ranks each potential subsequent action toward building
its learned concept. We empirically assess this approach with human
participants' ratings of the block structures. Initial results and qualitative
evaluations of structures generated by the trained agent show where it has
generalized concepts from the training data, which heuristics perform best
within the search space, and how we might improve learning and execution.
Fast Object Detection in Compressed Video
Authors: Shiyao Wang, Hongchao Lu, Zhidong Deng
Date: 2018-11-27 00:00:00
Proceeding: ICCV 2019 10
Tasks: Object, object-detection, Object Detection, Real-Time Object Detection, Video Compression

Object detection in videos has drawn increasing attention since it is more practical in real scenarios. Most of the deep learning methods use CNNs to process each decoded frame in a video stream individually. However, the free of charge yet valuable motion information already embedded in the video compression format is usually overlooked. In this paper, we propose a fast object detection method by taking advantage of this with a novel Motion aided Memory Network (MMNet). The MMNet has two major advantages: 1) It significantly accelerates the procedure of feature extraction for compressed videos. It only need to run a complete recognition network for I-frames, i.e. a few reference frames in a video, and it produces the features for the following P frames (predictive frames) with a light weight memory network, which runs fast; 2) Unlike existing methods that establish an additional network to model motion of frames, we take full advantage of both motion vectors and residual errors that are freely available in video streams. To our best knowledge, the MMNet is the first work that investigates a deep convolutional detector on compressed videos. Our method is evaluated on the large-scale ImageNet VID dataset, and the results show that it is 3x times faster than single image detector R-FCN and 10x times faster than high-performance detector MANet at a minor accuracy loss.
Understanding the Importance of Single Directions via Representative Substitution
Authors: Li Chen, Hailun Ding, Qi Li, Zhuo Li, Jian Peng, Haifeng Li
Date: 2018-11-27 00:00:00

Understanding the internal representations of deep neural networks (DNNs) is
crucal to explain their behavior. The interpretation of individual units, which
are neurons in MLPs or convolution kernels in convolutional networks, has been
paid much attention given their fundamental role. However, recent research
(Morcos et al. 2018) presented a counterintuitive phenomenon, which suggests
that an individual unit with high class selectivity, called interpretable
units, has poor contributions to generalization of DNNs. In this work, we
provide a new perspective to understand this counterintuitive phenomenon, which
makes sense when we introduce Representative Substitution (RS). Instead of
individually selective units with classes, the RS refers to the independence of
a unit's representations in the same layer without any annotation. Our
experiments demonstrate that interpretable units have high RS which are not
critical to network's generalization. The RS provides new insights into the
interpretation of DNNs and suggests that we need to focus on the independence
and relationship of the representations.
Dense xUnit Networks
Authors: Idan Kligvasser, Tomer Michaeli
Date: 2018-11-27 00:00:00
Tasks: Denoising, Image Restoration, Super-Resolution

Deep net architectures have constantly evolved over the past few years,
leading to significant advancements in a wide array of computer vision tasks.
However, besides high accuracy, many applications also require a low
computational load and limited memory footprint. To date, efficiency has
typically been achieved either by architectural choices at the macro level
(e.g. using skip connections or pruning techniques) or modifications at the
level of the individual layers (e.g. using depth-wise convolutions or channel
shuffle operations). Interestingly, much less attention has been devoted to the
role of the activation functions in constructing efficient nets. Recently,
Kligvasser et al. showed that incorporating spatial connections within the
activation functions, enables a significant boost in performance in image
restoration tasks, at any given budget of parameters. However, the
effectiveness of their xUnit module has only been tested on simple small
models, which are not characteristic of those used in high-level vision tasks.
In this paper, we adopt and improve the xUnit activation, show how it can be
incorporated into the DenseNet architecture, and illustrate its high
effectiveness for classification and image restoration tasks alike. While the
DenseNet architecture is extremely efficient to begin with, our dense xUnit net
(DxNet) can typically achieve the same performance with far fewer parameters.
For example, on ImageNet, our DxNet outperforms a ReLU-based DenseNet having
30% more parameters and achieves state-of-the-art results for this budget of
parameters. Furthermore, in denoising and super-resolution, DxNet significantly
improves upon all existing lightweight solutions, including the xUnit-based
nets of Kligvasser et al.
Rotting bandits are not harder than stochastic ones
Authors: Julien Seznec, Andrea Locatelli, Alexandra Carpentier, Alessandro Lazaric, Michal Valko
Date: 2018-11-27 00:00:00
Tasks: Multi-Armed Bandits, Recommendation Systems

In stochastic multi-armed bandits, the reward distribution of each arm is assumed to be stationary. This assumption is often violated in practice (e.g., in recommendation systems), where the reward of an arm may change whenever is selected, i.e., rested bandit setting. In this paper, we consider the non-parametric rotting bandit setting, where rewards can only decrease. We introduce the filtering on expanding window average (FEWA) algorithm that constructs moving averages of increasing windows to identify arms that are more likely to return high rewards when pulled once more. We prove that for an unknown horizon $T$, and without any knowledge on the decreasing behavior of the $K$ arms, FEWA achieves problem-dependent regret bound of $\widetilde{\mathcal{O}}(\log{(KT)}),$ and a problem-independent one of $\widetilde{\mathcal{O}}(\sqrt{KT})$. Our result substantially improves over the algorithm of Levine et al. (2017), which suffers regret $\widetilde{\mathcal{O}}(K^{1/3}T^{2/3})$. FEWA also matches known bounds for the stochastic bandit setting, thus showing that the rotting bandits are not harder. Finally, we report simulations confirming the theoretical improvements of FEWA.
Eliminating Exposure Bias and Loss-Evaluation Mismatch in Multiple Object Tracking
Authors: Andrii Maksai, Pascal Fua
Date: 2018-11-27 00:00:00
Tasks: Multiple Object Tracking, Object Tracking

Identity Switching remains one of the main difficulties Multiple Object
Tracking (MOT) algorithms have to deal with. Many state-of-the-art approaches
now use sequence models to solve this problem but their training can be
affected by biases that decrease their efficiency. In this paper, we introduce
a new training procedure that confronts the algorithm to its own mistakes while
explicitly attempting to minimize the number of switches, which results in
better training. We propose an iterative scheme of building a rich training set
and using it to learn a scoring function that is an explicit proxy for the
target tracking metric. Whether using only simple geometric features or more
sophisticated ones that also take appearance into account, our approach
outperforms the state-of-the-art on several MOT benchmarks.
GarNet: A Two-Stream Network for Fast and Accurate 3D Cloth Draping
Authors: Erhan Gundogdu, Victor Constantin, Amrollah Seifoddini, Minh Dang, Mathieu Salzmann, Pascal Fua
Date: 2018-11-27 00:00:00
Proceeding: ICCV 2019 10
Tasks: Virtual Try-on

While Physics-Based Simulation (PBS) can accurately drape a 3D garment on a 3D body, it remains too costly for real-time applications, such as virtual try-on. By contrast, inference in a deep network, requiring a single forward pass, is much faster. Taking advantage of this, we propose a novel architecture to fit a 3D garment template to a 3D body. Specifically, we build upon the recent progress in 3D point cloud processing with deep networks to extract garment features at varying levels of detail, including point-wise, patch-wise and global features. We fuse these features with those extracted in parallel from the 3D body, so as to model the cloth-body interactions. The resulting two-stream architecture, which we call as GarNet, is trained using a loss function inspired by physics-based modeling, and delivers visually plausible garment shapes whose 3D points are, on average, less than 1 cm away from those of a PBS method, while running 100 times faster. Moreover, the proposed method can model various garment types with different cutting patterns when parameters of those patterns are given as input to the network.
Noise2Void - Learning Denoising from Single Noisy Images
Authors: Alexander Krull, Tim-Oliver Buchholz, Florian Jug
Date: 2018-11-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: Denoising, Image Denoising

The field of image denoising is currently dominated by discriminative deep
learning methods that are trained on pairs of noisy input and clean target
images. Recently it has been shown that such methods can also be trained
without clean targets. Instead, independent pairs of noisy images can be used,
in an approach known as Noise2Noise (N2N). Here, we introduce Noise2Void (N2V),
a training scheme that takes this idea one step further. It does not require
noisy image pairs, nor clean target images. Consequently, N2V allows us to
train directly on the body of data to be denoised and can therefore be applied
when other methods cannot. Especially interesting is the application to
biomedical image data, where the acquisition of training targets, clean or
noisy, is frequently not possible. We compare the performance of N2V to
approaches that have either clean target images and/or noisy image pairs
available. Intuitively, N2V cannot be expected to outperform methods that have
more information available during training. Still, we observe that the
denoising performance of Noise2Void drops in moderation and compares favorably
to training-free denoising methods.
Neural Non-Stationary Spectral Kernel
Authors: Sami Remes, Markus Heinonen, Samuel Kaski
Date: 2018-11-27 00:00:00
Tasks: Gaussian Processes

Standard kernels such as Mat\'ern or RBF kernels only encode simple monotonic
dependencies within the input space. Spectral mixture kernels have been
proposed as general-purpose, flexible kernels for learning and discovering more
complicated patterns in the data. Spectral mixture kernels have recently been
generalized into non-stationary kernels by replacing the mixture weights,
frequency means and variances by input-dependent functions. These functions
have also been modelled as Gaussian processes on their own. In this paper we
propose modelling the hyperparameter functions with neural networks, and
provide an experimental comparison between the stationary spectral mixture and
the two non-stationary spectral mixtures. Scalable Gaussian process inference
is implemented within the sparse variational framework for all the kernels
considered. We show that the neural variant of the kernel is able to achieve
the best performance, among alternatives, on several benchmark datasets.
The Fact Extraction and VERification (FEVER) Shared Task
Authors: James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, Arpit Mittal
Date: 2018-11-27 00:00:00
Proceeding: WS 2018 11

We present the results of the first Fact Extraction and VERification (FEVER)
Shared Task. The task challenged participants to classify whether human-written
factoid claims could be Supported or Refuted using evidence retrieved from
Wikipedia. We received entries from 23 competing teams, 19 of which scored
higher than the previously published baseline. The best performing system
achieved a FEVER score of 64.21%. In this paper, we present the results of the
shared task and a summary of the systems, highlighting commonalities and
innovations among participating systems.
One-Shot Item Search with Multimodal Data
Authors: Jonghwa Yim, Junghun James Kim, Daekyu Shin
Date: 2018-11-27 00:00:00
Tasks: Image Retrieval, Text Matching

In the task of near similar image search, features from Deep Neural Network
is often used to compare images and measure similarity. In the past, we only
focused visual search in image dataset without text data. However, since deep
neural network emerged, the performance of visual search becomes high enough to
apply it in many industries from 3D data to multimodal data. Compared to the
needs of multimodal search, there has not been sufficient researches.
  In this paper, we present a method of near similar search with image and text
multimodal dataset. Earlier time, similar image search, especially when
searching shopping items, treated image and text separately to search similar
items and reorder the results. This regards two tasks of image search and text
matching as two different tasks. Our method, however, explore the vast data to
compute k-nearest neighbors using both image and text.
  In our experiment of similar item search, our system using multimodal data
shows better performance than single data while it only increases minute
computing time. For the experiment, we collected more than 15 million of
accessory and six million of digital product items from online shopping
websites, in which the product item comprises item images, titles, categories,
and descriptions. Then we compare the performance of multimodal searching to
single space searching in these datasets.
MagicVO: End-to-End Monocular Visual Odometry through Deep Bi-directional Recurrent Convolutional Neural Network
Authors: Jian Jiao, Jichao Jiao, Yaokai Mo, Weilun Liu, Zhongliang Deng
Date: 2018-11-27 00:00:00
Tasks: Monocular Visual Odometry, Visual Odometry

This paper proposes a new framework to solve the problem of monocular visual
odometry, called MagicVO . Based on Convolutional Neural Network (CNN) and
Bi-directional LSTM (Bi-LSTM), MagicVO outputs a 6-DoF absolute-scale pose at
each position of the camera with a sequence of continuous monocular images as
input. It not only utilizes the outstanding performance of CNN in image feature
processing to extract the rich features of image frames fully but also learns
the geometric relationship from image sequences pre and post through Bi-LSTM to
get a more accurate prediction. A pipeline of the MagicVO is shown in Fig. 1.
The MagicVO system is end-to-end, and the results of experiments on the KITTI
dataset and the ETH-asl cla dataset show that MagicVO has a better performance
than traditional visual odometry (VO) systems in the accuracy of pose and the
generalization ability.
Dataset Distillation
Authors: Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, Alexei A. Efros
Date: 2018-11-27 00:00:00
Tasks: Dataset Distillation

Model distillation aims to distill the knowledge of a complex model into a simpler one. In this paper, we consider an alternative formulation called dataset distillation: we keep the model fixed and instead attempt to distill the knowledge from a large training dataset into a small one. The idea is to synthesize a small number of data points that do not need to come from the correct data distribution, but will, when given to the learning algorithm as training data, approximate the model trained on the original data. For example, we show that it is possible to compress 60,000 MNIST training images into just 10 synthetic distilled images (one per class) and achieve close to original performance with only a few gradient descent steps, given a fixed network initialization. We evaluate our method in various initialization settings and with different learning objectives. Experiments on multiple datasets show the advantage of our approach compared to alternative methods.
A Bayesian model of acquisition and clearance of bacterial colonization
Authors: Marko Järvenpää, Mohamad R. Abdul Sater, Georgia K. Lagoudas, Paul C. Blainey, Loren G. Miller, James A. McKinnell, Susan S. Huang, Yonatan H. Grad, Pekka Marttinen
Date: 2018-11-27 00:00:00

Bacterial populations that colonize a host play important roles in host
health, including serving as a reservoir that transmits to other hosts and from
which invasive strains emerge, thus emphasizing the importance of understanding
rates of acquisition and clearance of colonizing populations. Studies of
colonization dynamics have been based on assessment of whether serial samples
represent a single population or distinct colonization events. A common
solution to estimate acquisition and clearance rates is to use a fixed genetic
distance threshold. However, this approach is often inadequate to account for
the diversity of the underlying within-host evolving population, the time
intervals between consecutive measurements, and the uncertainty in the
estimated acquisition and clearance rates. Here, we summarize recently
submitted work \cite{jarvenpaa2018named} and present a Bayesian model that
provides probabilities of whether two strains should be considered the same,
allowing to determine bacterial clearance and acquisition from genomes sampled
over time. We explicitly model the within-host variation using population
genetic simulation, and the inference is done by combining information from
multiple data sources by using a combination of Approximate Bayesian
Computation (ABC) and Markov Chain Monte Carlo (MCMC). We use the method to
analyse a collection of methicillin resistant Staphylococcus aureus (MRSA)
isolates.
Predicting the Flu from Instagram
Authors: Oguzhan Gencoglu, Miikka Ermes
Date: 2018-11-27 00:00:00

Conventional surveillance systems for monitoring infectious diseases, such as
influenza, face challenges due to shortage of skilled healthcare professionals,
remoteness of communities and absence of communication infrastructures.
Internet-based approaches for surveillance are appealing logistically as well
as economically. Search engine queries and Twitter have been the primarily used
data sources in such approaches. The aim of this study is to assess the
predictive power of an alternative data source, Instagram. By using 317 weeks
of publicly available data from Instagram, we trained several machine learning
algorithms to both nowcast and forecast the number of official influenza-like
illness incidents in Finland where population-wide official statistics about
the weekly incidents are available. In addition to date and hashtag count
features of online posts, we were able to utilize also the visual content of
the posted images with the help of deep convolutional neural networks. Our best
nowcasting model reached a mean absolute error of 11.33 incidents per week and
a correlation coefficient of 0.963 on the test data. Forecasting models for
predicting 1 week and 2 weeks ahead showed statistical significance as well by
reaching correlation coefficients of 0.903 and 0.862, respectively. This study
demonstrates how social media and in particular, digital photographs shared in
them, can be a valuable source of information for the field of infodemiology.
Reliable Semi-Supervised Learning when Labels are Missing at Random
Authors: Xiuming Liu, Dave Zachariah, Johan Wågberg, Thomas B. Schön
Date: 2018-11-27 00:00:00
Tasks: General Classification

Semi-supervised learning methods are motivated by the availability of large datasets with unlabeled features in addition to labeled data. Unlabeled data is, however, not guaranteed to improve classification performance and has in fact been reported to impair the performance in certain cases. A fundamental source of error arises from restrictive assumptions about the unlabeled features, which result in unreliable classifiers that underestimate their prediction error probabilities. In this paper, we develop a semi-supervised learning approach that relaxes such assumptions and is capable of providing classifiers that reliably quantify the label uncertainty. The approach is applicable using any generative model with a supervised learning algorithm. We illustrate the approach using both handwritten digit and cloth classification data where the labels are missing at random.