pwc-archive/papers-with-abstracts huggingface dataset:
Emergence of Altruism Behavior for Multi Feeding Areas in Army Ant Social Evolutionary System
Authors: Takumi Ichimura, Takuya Uemoto, Akira Hara
Date: 2018-07-10 00:00:00

Army ants perform the altruism that an ant sacrifices its own well-being for
the benefit of another ants. Army ants build bridges using their own bodies
along the path from a food to the nest. We developed the army ant inspired
social evolutionary system which can perform the altruism. The system has 2
kinds of ant agents, `Major ant' and `Minor ant' and the ants communicate with
each other via pheromones. One ants can recognize them as the signals from the
other ants. The pheromones evaporate with the certain ratio and diffused into
the space of neighbors stochastically. If the optimal bridge is found, the path
through the bridge is the shortest route from foods to the nest. We define the
probability for an ant to leave a bridge at a low occupancy condition of ants
and propose the constructing method of the optimal route. In this paper, the
behaviors of ant under the environment with two or more feeding spots are
observed. Some experimental results show the behaviors of great interest with
respect to altruism of ants. The description in some computer simulation is
reported in this paper.
Dynamic Objects Segmentation for Visual Localization in Urban Environments
Authors: Guoxiang Zhou, Berta Bescos, Marcin Dymczyk, Mark Pfeiffer, José Neira, Roland Siegwart
Date: 2018-07-09 00:00:00
Tasks: Pose Estimation, Visual Localization, Visual Odometry

Visual localization and mapping is a crucial capability to address many
challenges in mobile robotics. It constitutes a robust, accurate and
cost-effective approach for local and global pose estimation within prior maps.
Yet, in highly dynamic environments, like crowded city streets, problems arise
as major parts of the image can be covered by dynamic objects. Consequently,
visual odometry pipelines often diverge and the localization systems
malfunction as detected features are not consistent with the precomputed 3D
model. In this work, we present an approach to automatically detect dynamic
object instances to improve the robustness of vision-based localization and
mapping in crowded environments. By training a convolutional neural network
model with a combination of synthetic and real-world data, dynamic object
instance masks are learned in a semi-supervised way. The real-world data can be
collected with a standard camera and requires minimal further post-processing.
Our experiments show that a wide range of dynamic objects can be reliably
detected using the presented method. Promising performance is demonstrated on
our own and also publicly available datasets, which also shows the
generalization capabilities of this approach.
Real-time clustering and multi-target tracking using event-based sensors
Authors: Francisco Barranco, Cornelia Fermuller, Eduardo Ros
Date: 2018-07-08 00:00:00
Tasks: Clustering, Event-based vision, object-detection, Object Detection

Clustering is crucial for many computer vision applications such as robust
tracking, object detection and segmentation. This work presents a real-time
clustering technique that takes advantage of the unique properties of
event-based vision sensors. Since event-based sensors trigger events only when
the intensity changes, the data is sparse, with low redundancy. Thus, our
approach redefines the well-known mean-shift clustering method using
asynchronous events instead of conventional frames. The potential of our
approach is demonstrated in a multi-target tracking application using Kalman
filters to smooth the trajectories. We evaluated our method on an existing
dataset with patterns of different shapes and speeds, and a new dataset that we
collected. The sensor was attached to the Baxter robot in an eye-in-hand setup
monitoring real-world objects in an action manipulation task. Clustering
accuracy achieved an F-measure of 0.95, reducing the computational cost by 88%
compared to the frame-based method. The average error for tracking was 2.5
pixels and the clustering achieved a consistent number of clusters along time.
Multi-level Attention Model for Weakly Supervised Audio Classification
Authors: Changsong Yu, Karim Said Barsim, Qiuqiang Kong, Bin Yang
Date: 2018-03-06 00:00:00
Tasks: Audio Classification

In this paper, we propose a multi-level attention model to solve the weakly
labelled audio classification problem. The objective of audio classification is
to predict the presence or absence of audio events in an audio clip. Recently,
Google published a large scale weakly labelled dataset called Audio Set, where
each audio clip contains only the presence or absence of the audio events,
without the onset and offset time of the audio events. Our multi-level
attention model is an extension to the previously proposed single-level
attention model. It consists of several attention modules applied on
intermediate neural network layers. The output of these attention modules are
concatenated to a vector followed by a multi-label classifier to make the final
prediction of each class. Experiments shown that our model achieves a mean
average precision (mAP) of 0.360, outperforms the state-of-the-art single-level
attention model of 0.327 and Google baseline of 0.314.
Visual Reinforcement Learning with Imagined Goals
Authors: Ashvin Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, Sergey Levine
Date: 2018-07-12 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

For an autonomous agent to fulfill a wide range of user-specified goals at
test time, it must be able to learn broadly applicable and general-purpose
skill repertoires. Furthermore, to provide the requisite level of generality,
these skills must handle raw sensory input such as images. In this paper, we
propose an algorithm that acquires such general-purpose skills by combining
unsupervised representation learning and reinforcement learning of
goal-conditioned policies. Since the particular goals that might be required at
test-time are not known in advance, the agent performs a self-supervised
"practice" phase where it imagines goals and attempts to achieve them. We learn
a visual representation with three distinct purposes: sampling goals for
self-supervised practice, providing a structured transformation of raw sensory
inputs, and computing a reward signal for goal reaching. We also propose a
retroactive goal relabeling scheme to further improve the sample-efficiency of
our method. Our off-policy algorithm is efficient enough to learn policies that
operate on raw image observations and goals for a real-world robotic system,
and substantially outperforms prior techniques.
Negative Momentum for Improved Game Dynamics
Authors: Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki, Remi Lepriol, Gabriel Huang, Simon Lacoste-Julien, Ioannis Mitliagkas
Date: 2018-07-12 00:00:00

Games generalize the single-objective optimization paradigm by introducing different objective functions for different players. Differentiable games often proceed by simultaneous or alternating gradient updates. In machine learning, games are gaining new importance through formulations like generative adversarial networks (GANs) and actor-critic systems. However, compared to single-objective optimization, game dynamics are more complex and less understood. In this paper, we analyze gradient-based methods with momentum on simple games. We prove that alternating updates are more stable than simultaneous updates. Next, we show both theoretically and empirically that alternating gradient updates with a negative momentum term achieves convergence in a difficult toy adversarial problem, but also on the notoriously difficult to train saturating GANs.
When deep learning meets security
Authors: Majd Latah
Date: 2018-07-12 00:00:00
Tasks: Deep Learning

Deep learning is an emerging research field that has proven its effectiveness
towards deploying more efficient intelligent systems. Security, on the other
hand, is one of the most essential issues in modern communication systems.
Recently many papers have shown that using deep learning models can achieve
promising results when applied to the security domain. In this work, we provide
an overview for the recent studies that apply deep learning techniques to the
field of security.
Scalable Convolutional Dictionary Learning with Constrained Recurrent Sparse Auto-encoders
Authors: Bahareh Tolooshams, Sourav Dey, Demba Ba
Date: 2018-07-12 00:00:00
Tasks: blind source separation, Decoder, Dictionary Learning, Spike Sorting

Given a convolutional dictionary underlying a set of observed signals, can a
carefully designed auto-encoder recover the dictionary in the presence of
noise? We introduce an auto-encoder architecture, termed constrained recurrent
sparse auto-encoder (CRsAE), that answers this question in the affirmative.
Given an input signal and an approximate dictionary, the encoder finds a sparse
approximation using FISTA. The decoder reconstructs the signal by applying the
dictionary to the output of the encoder. The encoder and decoder in CRsAE
parallel the sparse-coding and dictionary update steps in optimization-based
alternating-minimization schemes for dictionary learning. As such, the
parameters of the encoder and decoder are not independent, a constraint which
we enforce for the first time. We derive the back-propagation algorithm for
CRsAE. CRsAE is a framework for blind source separation that, only knowing the
number of sources (dictionary elements), and assuming sparsely-many can
overlap, is able to separate them. We demonstrate its utility in the context of
spike sorting, a source separation problem in computational neuroscience. We
demonstrate the ability of CRsAE to recover the underlying dictionary and
characterize its sensitivity as a function of SNR.
The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach
Authors: Iulian Vlad Serban, Chinnadhurai Sankar, Michael Pieper, Joelle Pineau, Yoshua Bengio
Date: 2018-07-12 00:00:00
Tasks: Deep Reinforcement Learning, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning has recently shown many impressive successes.
However, one major obstacle towards applying such methods to real-world
problems is their lack of data-efficiency. To this end, we propose the
Bottleneck Simulator: a model-based reinforcement learning method which
combines a learned, factorized transition model of the environment with rollout
simulations to learn an effective policy from few examples. The learned
transition model employs an abstract, discrete (bottleneck) state, which
increases sample efficiency by reducing the number of model parameters and by
exploiting structural properties of the environment. We provide a mathematical
analysis of the Bottleneck Simulator in terms of fixed points of the learned
policy, which reveals how performance is affected by four distinct sources of
error: an error related to the abstract space structure, an error related to
the transition model estimation variance, an error related to the transition
model estimation bias, and an error related to the transition model class bias.
Finally, we evaluate the Bottleneck Simulator on two natural language
processing tasks: a text adventure game and a real-world, complex dialogue
response selection task. On both tasks, the Bottleneck Simulator yields
excellent performance beating competing approaches.
A Large-Scale Study on Regularization and Normalization in GANs
Authors: Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, Sylvain Gelly
Date: 2018-07-12 00:00:00
Proceeding: ICLR 2019 5

Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion. While they were successfully applied to many problems, training a GAN is a notoriously challenging task and requires a significant number of hyperparameter tuning, neural architecture engineering, and a non-trivial amount of "tricks". The success in many practical applications coupled with the lack of a measure to quantify the failure modes of GANs resulted in a plethora of proposed losses, regularization and normalization schemes, as well as neural architectures. In this work we take a sober view of the current state of GANs from a practical perspective. We discuss and evaluate common pitfalls and reproducibility issues, open-source our code on Github, and provide pre-trained models on TensorFlow Hub.
Orthogonal Matching Pursuit for Text Classification
Authors: Konstantinos Skianis, Nikolaos Tziortziotis, Michalis Vazirgiannis
Date: 2018-07-12 00:00:00
Proceeding: WS 2018 11
Tasks: Classification, General Classification, text-classification, Text Classification, Variable Selection

In text classification, the problem of overfitting arises due to the high
dimensionality, making regularization essential. Although classic regularizers
provide sparsity, they fail to return highly accurate models. On the contrary,
state-of-the-art group-lasso regularizers provide better results at the expense
of low sparsity. In this paper, we apply a greedy variable selection algorithm,
called Orthogonal Matching Pursuit, for the text classification task. We also
extend standard group OMP by introducing overlapping Group OMP to handle
overlapping groups of features. Empirical analysis verifies that both OMP and
overlapping GOMP constitute powerful regularizers, able to produce effective
and very sparse models. Code and data are available online:
https://github.com/y3nk0/OMP-for-Text-Classification .
Inferring Multidimensional Rates of Aging from Cross-Sectional Data
Authors: Emma Pierson, Pang Wei Koh, Tatsunori Hashimoto, Daphne Koller, Jure Leskovec, Nicholas Eriksson, Percy Liang
Date: 2018-07-12 00:00:00
Tasks: Human Aging, Time Series, Time Series Analysis

Modeling how individuals evolve over time is a fundamental problem in the
natural and social sciences. However, existing datasets are often
cross-sectional with each individual observed only once, making it impossible
to apply traditional time-series methods. Motivated by the study of human
aging, we present an interpretable latent-variable model that learns temporal
dynamics from cross-sectional data. Our model represents each individual's
features over time as a nonlinear function of a low-dimensional,
linearly-evolving latent state. We prove that when this nonlinear function is
constrained to be order-isomorphic, the model family is identifiable solely
from cross-sectional data provided the distribution of time-independent
variation is known. On the UK Biobank human health dataset, our model
reconstructs the observed data while learning interpretable rates of aging
associated with diseases, mortality, and aging risk factors.
LandmarkBoost: Efficient Visual Context Classifiers for Robust Localization
Authors: Marcin Dymczyk, Igor Gilitschenski, Juan Nieto, Simon Lynen, Bernhard Zeisl, Roland Siegwart
Date: 2018-07-12 00:00:00
Tasks: Pose Retrieval, Retrieval

The growing popularity of autonomous systems creates a need for reliable and
efficient metric pose retrieval algorithms. Currently used approaches tend to
rely on nearest neighbor search of binary descriptors to perform the 2D-3D
matching and guarantee realtime capabilities on mobile platforms. These methods
struggle, however, with the growing size of the map, changes in viewpoint or
appearance, and visual aliasing present in the environment. The rigidly defined
descriptor patterns only capture a limited neighborhood of the keypoint and
completely ignore the overall visual context.
  We propose LandmarkBoost - an approach that, in contrast to the conventional
2D-3D matching methods, casts the search problem as a landmark classification
task. We use a boosted classifier to classify landmark observations and
directly obtain correspondences as classifier scores. We also introduce a
formulation of visual context that is flexible, efficient to compute, and can
capture relationships in the entire image plane. The original binary
descriptors are augmented with contextual information and informative features
are selected by the boosting framework. Through detailed experiments, we
evaluate the retrieval quality and performance of LandmarkBoost, demonstrating
that it outperforms common state-of-the-art descriptor matching methods.
Explorations in Homeomorphic Variational Auto-Encoding
Authors: Luca Falorsi, Pim de Haan, Tim R. Davidson, Nicola De Cao, Maurice Weiler, Patrick Forré, Taco S. Cohen
Date: 2018-07-12 00:00:00

The manifold hypothesis states that many kinds of high-dimensional data are
concentrated near a low-dimensional manifold. If the topology of this data
manifold is non-trivial, a continuous encoder network cannot embed it in a
one-to-one manner without creating holes of low density in the latent space.
This is at odds with the Gaussian prior assumption typically made in
Variational Auto-Encoders (VAEs), because the density of a Gaussian
concentrates near a blob-like manifold.
  In this paper we investigate the use of manifold-valued latent variables.
Specifically, we focus on the important case of continuously differentiable
symmetry groups (Lie groups), such as the group of 3D rotations
$\operatorname{SO}(3)$. We show how a VAE with $\operatorname{SO}(3)$-valued
latent variables can be constructed, by extending the reparameterization trick
to compact connected Lie groups. Our experiments show that choosing
manifold-valued latent variables that match the topology of the latent data
manifold, is crucial to preserve the topological structure and learn a
well-behaved latent space.
Making Efficient Use of a Domain Expert's Time in Relation Extraction
Authors: Linara Adilova, Sven Giesselbach, Stefan Rüping
Date: 2018-07-12 00:00:00
Tasks: Active Learning, Relation, Relation Extraction

Scarcity of labeled data is one of the most frequent problems faced in
machine learning. This is particularly true in relation extraction in text
mining, where large corpora of texts exists in many application domains, while
labeling of text data requires an expert to invest much time to read the
documents. Overall, state-of-the art models, like the convolutional neural
network used in this paper, achieve great results when trained on large enough
amounts of labeled data. However, from a practical point of view the question
arises whether this is the most efficient approach when one takes the manual
effort of the expert into account. In this paper, we report on an alternative
approach where we first construct a relation extraction model using distant
supervision, and only later make use of a domain expert to refine the results.
Distant supervision provides a mean of labeling data given known relations in a
knowledge base, but it suffers from noisy labeling. We introduce an active
learning based extension, that allows our neural network to incorporate expert
feedback and report on first results on a complex data set.
Toward Convolutional Blind Denoising of Real Photographs
Authors: Shi Guo, Zifei Yan, Kai Zhang, WangMeng Zuo, Lei Zhang
Date: 2018-07-12 00:00:00
Proceeding: CVPR 2019 6
Tasks: Denoising, Image Denoising, Noise Estimation

While deep convolutional neural networks (CNNs) have achieved impressive
success in image denoising with additive white Gaussian noise (AWGN), their
performance remains limited on real-world noisy photographs. The main reason is
that their learned models are easy to overfit on the simplified AWGN model
which deviates severely from the complicated real-world noise model. In order
to improve the generalization ability of deep CNN denoisers, we suggest
training a convolutional blind denoising network (CBDNet) with more realistic
noise model and real-world noisy-clean image pairs. On the one hand, both
signal-dependent noise and in-camera signal processing pipeline is considered
to synthesize realistic noisy images. On the other hand, real-world noisy
photographs and their nearly noise-free counterparts are also included to train
our CBDNet. To further provide an interactive strategy to rectify denoising
result conveniently, a noise estimation subnetwork with asymmetric learning to
suppress under-estimation of noise level is embedded into CBDNet. Extensive
experimental results on three datasets of real-world noisy photographs clearly
demonstrate the superior performance of CBDNet over state-of-the-arts in terms
of quantitative metrics and visual quality. The code has been made available at
https://github.com/GuoShi28/CBDNet.
A Library for Constraint Consistent Learning
Authors: Yuchen Zhao, Jeevan Manavalan, Prabhakar Ray, Hsiu-Chin Lin, Matthew Howard
Date: 2018-07-12 00:00:00

This paper introduces the first, open source software library for Constraint Consistent Learning (CCL). It implements a family of data-driven methods that are capable of (i) learning state-independent and -dependent constraints, (ii) decomposing the behaviour of redundant systems into task- and null-space parts, and (iii) uncovering the underlying null space control policy. It is a tool to analyse and decompose many everyday tasks, such as wiping, reaching and drawing. The library also includes several tutorials that demonstrate its use with both simulated and real world data in a systematic way. This paper documents the implementation of the library, tutorials and associated helper methods. The software is made freely available to the community, to enable code reuse and allow users to gain in-depth experience in statistical learning in this area.
Learning to Segment Medical Images with Scribble-Supervision Alone
Authors: Yigit B. Can, Krishna Chaitanya, Basil Mustafa, Lisa M. Koch, Ender Konukoglu, Christian F. Baumgartner
Date: 2018-07-12 00:00:00
Tasks: Anatomy, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Semantic segmentation of medical images is a crucial step for the
quantification of healthy anatomy and diseases alike. The majority of the
current state-of-the-art segmentation algorithms are based on deep neural
networks and rely on large datasets with full pixel-wise annotations. Producing
such annotations can often only be done by medical professionals and requires
large amounts of valuable time. Training a medical image segmentation network
with weak annotations remains a relatively unexplored topic. In this work we
investigate training strategies to learn the parameters of a pixel-wise
segmentation network from scribble annotations alone. We evaluate the
techniques on public cardiac (ACDC) and prostate (NCI-ISBI) segmentation
datasets. We find that the networks trained on scribbles suffer from a
remarkably small degradation in Dice of only 2.9% (cardiac) and 4.5% (prostate)
with respect to a network trained on full annotations.