pwc-archive/papers-with-abstracts huggingface dataset:
Maximizing Drift is Not Optimal for Solving OneMax
Authors: Nathan Buskulic, Carola Doerr
Date: 2019-04-16 00:00:00

It may seem very intuitive that for the maximization of the OneMax problem $\OM(x):=\sum_{i=1}^n{x_i}$ the best that an elitist unary unbiased search algorithm can do is to store a best so far solution, and to modify it with the operator that yields the best possible expected progress in function value. This assumption has been implicitly used in several empirical works. In [Doerr, Doerr, Yang: Optimal parameter choices via precise black-box analysis, TCS, 2020] it was formally proven that this approach is indeed almost optimal. In this work we prove that drift maximization is not optimal. More precisely, we show that for most fitness levels between $n/2$ and $2n/3$ the optimal mutation strengths are larger than the drift-maximizing ones. This implies that the optimal RLS is more risk-affine than the variant maximizing the step-wise expected progress. We show similar results for the mutation rates of the classic (1+1) Evolutionary Algorithm (EA) and its resampling variant, the (1+1) EA$_{>0}$. As a result of independent interest we show that the optimal mutation strengths, unlike the drift-maximizing ones, can be even.
Simion Zoo: A Workbench for Distributed Experimentation with Reinforcement Learning for Continuous Control Tasks
Authors: Borja Fernandez-Gauna, Manuel Graña, Roland S. Zimmermann
Date: 2019-04-16 00:00:00
Tasks: continuous-control, Continuous Control, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present Simion Zoo, a Reinforcement Learning (RL) workbench that provides
a complete set of tools to design, run, and analyze the results,both
statistically and visually, of RL control applications. The main features that
set apart Simion Zoo from similar software packages are its easy-to-use GUI,
its support for distributed execution including deployment over graphics
processing units (GPUs) , and the possibility to explore concurrently the RL
metaparameter space, which is key to successful RL experimentation.
Learning 3D Navigation Protocols on Touch Interfaces with Cooperative Multi-Agent Reinforcement Learning
Authors: Quentin Debard, Jilles Steeve Dibangoye, Stéphane Canu, Christian Wolf
Date: 2019-04-16 00:00:00
Tasks: Multi-agent Reinforcement Learning, Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

Using touch devices to navigate in virtual 3D environments such as computer assisted design (CAD) models or geographical information systems (GIS) is inherently difficult for humans, as the 3D operations have to be performed by the user on a 2D touch surface. This ill-posed problem is classically solved with a fixed and handcrafted interaction protocol, which must be learned by the user. We propose to automatically learn a new interaction protocol allowing to map a 2D user input to 3D actions in virtual environments using reinforcement learning (RL). A fundamental problem of RL methods is the vast amount of interactions often required, which are difficult to come by when humans are involved. To overcome this limitation, we make use of two collaborative agents. The first agent models the human by learning to perform the 2D finger trajectories. The second agent acts as the interaction protocol, interpreting and translating to 3D operations the 2D finger trajectories from the first agent. We restrict the learned 2D trajectories to be similar to a training set of collected human gestures by first performing state representation learning, prior to reinforcement learning. This state representation learning is addressed by projecting the gestures into a latent space learned by a variational auto encoder (VAE).
Online Selection of CMA-ES Variants
Authors: Diederick Vermetten, Sander van Rijn, Thomas Bäck, Carola Doerr
Date: 2019-04-16 00:00:00

In the field of evolutionary computation, one of the most challenging topics
is algorithm selection. Knowing which heuristics to use for which optimization
problem is key to obtaining high-quality solutions. We aim to extend this
research topic by taking a first step towards a selection method for adaptive
CMA-ES algorithms. We build upon the theoretical work done by van Rijn
\textit{et al.} [PPSN'18], in which the potential of switching between
different CMA-ES variants was quantified in the context of a modular CMA-ES
framework.
  We demonstrate in this work that their proposed approach is not very
reliable, in that implementing the suggested adaptive configurations does not
yield the predicted performance gains. We propose a revised approach, which
results in a more robust fit between predicted and actual performance. The
adaptive CMA-ES approach obtains performance gains on 18 out of 24 tested
functions of the BBOB benchmark, with stable advantages of up to 23\%. An
analysis of module activation indicates which modules are most crucial for the
different phases of optimizing each of the 24 benchmark problems. The module
activation also suggests that additional gains are possible when including the
(B)IPOP modules, which we have excluded for this present work.
Visual Relationship Detection with Language prior and Softmax
Authors: Jaewon Jung, Jongyoul Park
Date: 2019-04-16 00:00:00
Tasks: Knowledge Distillation, Relationship Detection, Visual Relationship Detection

Visual relationship detection is an intermediate image understanding task
that detects two objects and classifies a predicate that explains the
relationship between two objects in an image. The three components are
linguistically and visually correlated (e.g. "wear" is related to "person" and
"shirt", while "laptop" is related to "table" and "on") thus, the solution
space is huge because there are many possible cases between them. Language and
visual modules are exploited and a sophisticated spatial vector is proposed.
The models in this work outperformed the state of arts without costly
linguistic knowledge distillation from a large text corpus and building complex
loss functions. All experiments were only evaluated on Visual Relationship
Detection and Visual Genome dataset.
AT-GAN: An Adversarial Generator Model for Non-constrained Adversarial Examples
Authors: Xiaosen Wang, Kun He, Chuanbiao Song, Li-Wei Wang, John E. Hopcroft
Date: 2019-04-16 00:00:00
Tasks: Adversarial Attack

Despite the rapid development of adversarial machine learning, most adversarial attack and defense researches mainly focus on the perturbation-based adversarial examples, which is constrained by the input images. In comparison with existing works, we propose non-constrained adversarial examples, which are generated entirely from scratch without any constraint on the input. Unlike perturbation-based attacks, or the so-called unrestricted adversarial attack which is still constrained by the input noise, we aim to learn the distribution of adversarial examples to generate non-constrained but semantically meaningful adversarial examples. Following this spirit, we propose a novel attack framework called AT-GAN (Adversarial Transfer on Generative Adversarial Net). Specifically, we first develop a normal GAN model to learn the distribution of benign data, and then transfer the pre-trained GAN model to estimate the distribution of adversarial examples for the target model. In this way, AT-GAN can learn the distribution of adversarial examples that is very close to the distribution of real data. To our knowledge, this is the first work of building an adversarial generator model that could produce adversarial examples directly from any input noise. Extensive experiments and visualizations show that the proposed AT-GAN can very efficiently generate diverse adversarial examples that are more realistic to human perception. In addition, AT-GAN yields higher attack success rates against adversarially trained models under white-box attack setting and exhibits moderate transferability against black-box models.
A Pattern-Hierarchy Classifier for Reduced Teaching
Authors: Kieran Greer
Date: 2019-04-16 00:00:00
Tasks: Clustering

This paper describes a design that can be used for Explainable AI. The lower level is a nested ensemble of patterns created by self-organisation. The upper level is a hierarchical tree, where nodes are linked through individual concepts, so there is a transition from mixed ensemble masses to specific categories. Lower-level pattern ensembles are learned in an unsupervsised manner and then split into branches when it is clear that the category has changed. Links between the two levels define that the concepts are learned and missing links define that they are guessed only. This paper proposes some new clustering algorithms for producing the pattern ensembles, that are themselves an ensemble which converges through aggregations. Multiple solutions are also combined, to make the final result more robust. One measure of success is how coherent these ensembles are, which means that every data row in the cluster belongs to the same category. The total number of clusters is also important and the teaching phase can correct the ensemble estimates with respect to both of these. A teaching phase would then help the classifier to learn the true category for each input row. During this phase, any classifier can learn or infer correct classifications from some other classifier's knowledge, thereby reducing the required number of presentations. As the information is added, cross-referencing between the two structures allows it to be used more widely, where a unique structure can build up that would not be possible by either method separately.
The ALOS Dataset for Advert Localization in Outdoor Scenes
Authors: Soumyabrata Dev, Murhaf Hossari, Matthew Nicholson, Killian McCabe, Atul Nautiyal, Clare Conran, Jian Tang, Wei Xu, François Pitié
Date: 2019-04-16 00:00:00
Tasks: BIG-bench Machine Learning, Marketing, Position, Semantic Segmentation, Video Editing

The rapid increase in the number of online videos provides the marketing and
advertising agents ample opportunities to reach out to their audience. One of
the most widely used strategies is product placement, or embedded marketing,
wherein new advertisements are integrated seamlessly into existing
advertisements in videos. Such strategies involve accurately localizing the
position of the advert in the image frame, either manually in the video editing
phase, or by using machine learning frameworks. However, these machine learning
techniques and deep neural networks need a massive amount of data for training.
In this paper, we propose and release the first large-scale dataset of
advertisement billboards, captured in outdoor scenes. We also benchmark several
state-of-the-art semantic segmentation algorithms on our proposed dataset.
Weakly Supervised Gaussian Networks for Action Detection
Authors: Basura Fernando, Cheston Tan Yin Chet, Hakan Bilen
Date: 2019-04-16 00:00:00
Tasks: Action Detection, Action Localization, Temporal Action Localization

Detecting temporal extents of human actions in videos is a challenging computer vision problem that requires detailed manual supervision including frame-level labels. This expensive annotation process limits deploying action detectors to a limited number of categories. We propose a novel method, called WSGN, that learns to detect actions from \emph{weak supervision}, using only video-level labels. WSGN learns to exploit both video-specific and dataset-wide statistics to predict relevance of each frame to an action category. This strategy leads to significant gains in action detection for two standard benchmarks THUMOS14 and Charades. Our method obtains excellent results compared to state-of-the-art methods that uses similar features and loss functions on THUMOS14 dataset. Similarly, our weakly supervised method is only 0.3% mAP behind a state-of-the-art supervised method on challenging Charades dataset for action localization.
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation
Authors: Junhao Wen, Elina Thibeau-Sutre, Mauricio Diaz-Melo, Jorge Samper-Gonzalez, Alexandre Routier, Simona Bottani, Didier Dormont, Stanley Durrleman, Ninon Burgos, Olivier Colliot
Date: 2019-04-16 00:00:00
Tasks: General Classification, Model Selection, Systematic Literature Review, Transfer Learning

Over 30 papers have proposed to use convolutional neural network (CNN) for AD classification from anatomical MRI. However, the classification performance is difficult to compare across studies due to variations in components such as participant selection, image preprocessing or validation procedure. Moreover, these studies are hardly reproducible because their frameworks are not publicly accessible and because implementation details are lacking. Lastly, some of these papers may report a biased performance due to inadequate or unclear validation or model selection procedures. In the present work, we aim to address these limitations through three main contributions. First, we performed a systematic literature review and found that more than half of the surveyed papers may have suffered from data leakage. Our second contribution is the extension of our open-source framework for classification of AD using CNN and T1-weighted MRI. Finally, we used this framework to rigorously compare different CNN architectures. The data was split into training/validation/test sets at the very beginning and only the training/validation sets were used for model selection. To avoid any overfitting, the test sets were left untouched until the end of the peer-review process. Overall, the different 3D approaches (3D-subject, 3D-ROI, 3D-patch) achieved similar performances while that of the 2D slice approach was lower. Of note, the different CNN approaches did not perform better than a SVM with voxel-based features. The different approaches generalized well to similar populations but not to datasets with different inclusion criteria or demographical characteristics.
Cryo-Electron Microscopy Image Analysis Using Multi-Frequency Vector Diffusion Maps
Authors: Yifeng Fan, Zhizhen Zhao
Date: 2019-04-16 00:00:00
Tasks: Cryogenic Electron Microscopy (cryo-EM), Denoising, image-classification, Image Classification, Image Restoration

Cryo-electron microscopy (EM) single particle reconstruction is an entirely
general technique for 3D structure determination of macromolecular complexes.
However, because the images are taken at low electron dose, it is extremely
hard to visualize the individual particle with low contrast and high noise
level. In this paper, we propose a novel approach called multi-frequency vector
diffusion maps (MFVDM) to improve the efficiency and accuracy of cryo-EM 2D
image classification and denoising. This framework incorporates different
irreducible representations of the estimated alignment between similar images.
In addition, we propose a graph filtering scheme to denoise the images using
the eigenvalues and eigenvectors of the MFVDM matrices. Through both simulated
and publicly available real data, we demonstrate that our proposed method is
efficient and robust to noise compared with the state-of-the-art cryo-EM 2D
class averaging and image restoration algorithms.
Persistence Curves: A canonical framework for summarizing persistence diagrams
Authors: Yu-Min Chung, Austin Lawson
Date: 2019-04-16 00:00:00
Tasks: BIG-bench Machine Learning, Texture Classification, Topological Data Analysis

Persistence diagrams are one of the main tools in the field of Topological Data Analysis (TDA). They contain fruitful information about the shape of data. The use of machine learning algorithms on the space of persistence diagrams proves to be challenging as the space lacks an inner product. For that reason, transforming these diagrams in a way that is compatible with machine learning is an important topic currently researched in TDA. In this paper, our main contribution consists of three components. First, we develop a general and unifying framework of vectorizing diagrams that we call the \textit{Persistence Curves} (PCs), and show that several well-known summaries, such as Persistence Landscapes, fall under the PC framework. Second, we propose several new summaries based on PC framework and provide a theoretical foundation for their stability analysis. Finally, we apply proposed PCs to two applications---texture classification and determining the parameters of a discrete dynamical system; their performances are competitive with other TDA methods.
An Evaluation Framework for Interactive Recommender System
Authors: Oznur Alkan, Elizabeth M. Daly, Adi Botea
Date: 2019-04-16 00:00:00
Tasks: Recommendation Systems

Traditional recommender systems present a relatively static list of
recommendations to a user where the feedback is typically limited to an
accept/reject or a rating model. However, these simple modes of feedback may
only provide limited insights as to why a user likes or dislikes an item and
what aspects of the item the user has considered. Interactive recommender
systems present an opportunity to engage the user in the process by allowing
them to interact with the recommendations, provide feedback and impact the
results in real-time. Evaluation of the impact of the user interaction
typically requires an extensive user study which is time consuming and gives
researchers limited opportunities to tune their solutions without having to
conduct multiple rounds of user feedback. Additionally, user experience and
design aspects can have a significant impact on the user feedback which may
result in not necessarily assessing the quality of some of the underlying
algorithmic decisions in the overall solution. As a result, we present an
evaluation framework which aims to simulate the users interacting with the
recommender. We formulate metrics to evaluate the quality of the interactive
recommenders which are outputted by the framework once simulation is completed.
While simulation along is not sufficient to evaluate a complete solution, the
results can be useful to help researchers tune their solution before moving to
the user study stage.
Kernel methods for detecting coherent structures in dynamical data
Authors: Stefan Klus, Brooke E. Husic, Mattes Mollenhauer, Frank Noé
Date: 2019-04-16 00:00:00
Tasks: Dimensionality Reduction

We illustrate relationships between classical kernel-based dimensionality reduction techniques and eigendecompositions of empirical estimates of reproducing kernel Hilbert space (RKHS) operators associated with dynamical systems. In particular, we show that kernel canonical correlation analysis (CCA) can be interpreted in terms of kernel transfer operators and that it can be obtained by optimizing the variational approach for Markov processes (VAMP) score. As a result, we show that coherent sets of particle trajectories can be computed by kernel CCA. We demonstrate the efficiency of this approach with several examples, namely the well-known Bickley jet, ocean drifter data, and a molecular dynamics problem with a time-dependent potential. Finally, we propose a straightforward generalization of dynamic mode decomposition (DMD) called coherent mode decomposition (CMD). Our results provide a generic machine learning approach to the computation of coherent sets with an objective score that can be used for cross-validation and the comparison of different methods.
Co-Separating Sounds of Visual Objects
Authors: Ruohan Gao, Kristen Grauman
Date: 2019-04-16 00:00:00
Proceeding: ICCV 2019 10
Tasks: Audio Denoising, Audio Source Separation, Denoising

Learning how objects sound from video is challenging, since they often heavily overlap in a single audio channel. Current methods for visually-guided audio source separation sidestep the issue by training with artificially mixed video clips, but this puts unwieldy restrictions on training data collection and may even prevent learning the properties of "true" mixed sounds. We introduce a co-separation training paradigm that permits learning object-level sounds from unlabeled multi-source videos. Our novel training objective requires that the deep neural network's separated audio for similar-looking objects be consistently identifiable, while simultaneously reproducing accurate video-level audio tracks for each source training pair. Our approach disentangles sounds in realistic test videos, even in cases where an object was not observed individually during training. We obtain state-of-the-art results on visually-guided audio source separation and audio denoising for the MUSIC, AudioSet, and AV-Bench datasets.
Sameness Entices, but Novelty Enchants in Fanfiction Online
Authors: Elise Jing, Simon DeDeo, Devin Robert Wright, Yong-Yeol Ahn
Date: 2019-04-16 00:00:00
Tasks: Language Modelling

Cultural evolution is driven by how we choose what to consume and share with others. A common belief is that the cultural artifacts that succeed are ones that balance novelty and conventionality. This balance theory suggests that people prefer works that are familiar, but not so familiar as to be boring; novel, but not so novel as to violate the expectations of their genre. We test this idea using a large dataset of fanfiction. We apply a multiple regression model and a generalized additive model to examine how the recognition a work receives varies with its novelty, estimated through a Latent Dirichlet Allocation topic model, in the context of existing works. We find the opposite pattern of what the balance theory predicts$\unicode{x2014}$overall success decline almost monotonically with novelty and exhibits a U-shaped, instead of an inverse U-shaped, curve. This puzzle is resolved by teasing out two competing forces: sameness attracts the mass whereas novelty provides enjoyment. Taken together, even though the balance theory holds in terms of expressed enjoyment, the overall success can show the opposite pattern due to the dominant role of sameness to attract the audience. Under these two forces, cultural evolution may have to work against inertia$\unicode{x2014}$the appetite for consuming the familiar$\unicode{x2014}$and may resemble a punctuated equilibrium, marked by occasional leaps.
Subjective Assessment of Text Complexity: A Dataset for German Language
Authors: Babak Naderi, Salar Mohtaj, Kaspar Ensikat, Sebastian Möller
Date: 2019-04-16 00:00:00
Tasks: Articles, Text Simplification

This paper presents TextComplexityDE, a dataset consisting of 1000 sentences
in German language taken from 23 Wikipedia articles in 3 different
article-genres to be used for developing text-complexity predictor models and
automatic text simplification in German language. The dataset includes
subjective assessment of different text-complexity aspects provided by German
learners in level A and B. In addition, it contains manual simplification of
250 of those sentences provided by native speakers and subjective assessment of
the simplified sentences by participants from the target group. The subjective
ratings were collected using both laboratory studies and crowdsourcing
approach.
Multimodal Subspace Support Vector Data Description
Authors: Fahad Sohrab, Jenni Raitoharju, Alexandros Iosifidis, Moncef Gabbouj
Date: 2019-04-16 00:00:00
Tasks: All, General Classification, One-Class Classification

In this paper, we propose a novel method for projecting data from multiple modalities to a new subspace optimized for one-class classification. The proposed method iteratively transforms the data from the original feature space of each modality to a new common feature space along with finding a joint compact description of data coming from all the modalities. For data in each modality, we define a separate transformation to map the data from the corresponding feature space to the new optimized subspace by exploiting the available information from the class of interest only. We also propose different regularization strategies for the proposed method and provide both linear and non-linear formulations. The proposed Multimodal Subspace Support Vector Data Description outperforms all the competing methods using data from a single modality or fusing data from all modalities in four out of five datasets.