pwc-archive/papers-with-abstracts huggingface dataset:
Sampling Acquisition Functions for Batch Bayesian Optimization
Authors: Alessandro De Palma, Celestine Mendler-Dünner, Thomas Parnell, Andreea Anghel, Haralampos Pozidis
Date: 2019-03-22 00:00:00
Tasks: Bayesian Optimization, Thompson Sampling

We present Acquisition Thompson Sampling (ATS), a novel technique for batch Bayesian Optimization (BO) based on the idea of sampling multiple acquisition functions from a stochastic process. We define this process through the dependency of the acquisition functions on a set of model hyper-parameters. ATS is conceptually simple, straightforward to implement and, unlike other batch BO methods, it can be employed to parallelize any sequential acquisition function or to make existing parallel methods scale further. We present experiments on a variety of benchmark functions and on the hyper-parameter optimization of a popular gradient boosting tree algorithm. These demonstrate the advantages of ATS with respect to classical parallel Thompson Sampling for BO, its competitiveness with two state-of-the-art batch BO methods, and its effectiveness if applied to existing parallel BO algorithms.
An end-to-end Neural Network Framework for Text Clustering
Authors: Jie Zhou, Xingyi Cheng, Jinchao Zhang
Date: 2019-03-22 00:00:00
Tasks: Clustering, Representation Learning, Sentiment Analysis, Sentiment Classification, Text Clustering

The unsupervised text clustering is one of the major tasks in natural
language processing (NLP) and remains a difficult and complex problem.
Conventional \mbox{methods} generally treat this task using separated steps,
including text representation learning and clustering the representations. As
an improvement, neural methods have also been introduced for continuous
representation learning to address the sparsity problem. However, the
multi-step process still deviates from the unified optimization target.
Especially the second step of cluster is generally performed with conventional
methods such as k-Means. We propose a pure neural framework for text clustering
in an end-to-end manner. It jointly learns the text representation and the
clustering model. Our model works well when the context can be obtained, which
is nearly always the case in the field of NLP. We have our method
\mbox{evaluated} on two widely used benchmarks: IMDB movie reviews for
sentiment classification and $20$-Newsgroup for topic categorization. Despite
its simplicity, experiments show the model outperforms previous clustering
methods by a large margin. Furthermore, the model is also verified on English
wiki dataset as a large corpus.
Artificial intelligence-based process for metal scrap sorting
Authors: Maximilian Auer, Kai Osswald, Raphael Volz, Joerg Woidasky
Date: 2019-03-22 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning offers remarkable benefits for improving workplaces and
working conditions amongst others in the recycling industry. Here e.g.
hand-sorting of medium value scrap is labor intensive and requires experienced
and skilled workers. On the one hand, they have to be highly concentrated for
making proper readings and analyses of the material, but on the other hand,
this work is monotonous. Therefore, a machine learning approach is proposed for
a quick and reliable automated identification of alloys in the recycling
industry, while the mere scrap handling is regarded to be left in the hands of
the workers. To this end, a set of twelve tool and high-speed steels from the
field were selected to be identified by their spectrum induced by electric
arcs. For data acquisition, the optical emission spectrometer Thorlabs CCS 100
was used. Spectra have been post-processed to be fed into the supervised
machine learning algorithm. The development of the machine learning software is
conducted according to the steps of the VDI 2221 standard method. For
programming Python 3 as well as the python-library sklearn were used. By
systematic parameter variation, the appropriate machine learning algorithm was
selected and validated. Subsequent validation steps showed that the automated
identification process using a machine learning approach and the optical
emission spectrometry is applicable, reaching a maximum F1 score of 96.9 %.
This performance is as good as the performance of a highly trained worker using
visual grinding spark identification. The tests were based on a self-generated
set of 600 spectra per single alloy (7,200 spectra in total) which were
produced using an industry workshop device.
A constrained ICA-EMD Model for Group Level fMRI Analysis
Authors: Simon Wein, Ana Maria Tomé, Markus Goldhacker, Mark W. Greenlee, Elmar W. Lang
Date: 2019-03-22 00:00:00

Independent component analysis (ICA), as a data driven method, has shown to
be a powerful tool for functional magnetic resonance imaging (fMRI) data
analysis. One drawback of this multivariate approach is, that it is not
compatible to the analysis of group data in general. Therefore various
techniques have been proposed in order to overcome this limitation of ICA. In
this paper a novel ICA-based work-flow for extracting resting state networks
from fMRI group studies is proposed. An empirical mode decomposition (EMD) is
used to generate reference signals in a data driven manner, which can be
incorporated into a constrained version of ICA (cICA), what helps to eliminate
the inherent ambiguities of ICA. The results of the proposed workflow are then
compared to those obtained by a widely used group ICA approach for fMRI
analysis. In this paper it is demonstrated that intrinsic modes, extracted by
EMD, are suitable to serve as references for cICA to obtain typical resting
state patterns, which are consistent over subjects. By introducing these
reference signals into the ICA, our processing pipeline makes it transparent
for the user, how comparable activity patterns across subjects emerge. This
additionally allows adapting the trade-off between enforcing similarity across
subjects and preserving individual subject features.
Fast Bayesian Uncertainty Estimation and Reduction of Batch Normalized Single Image Super-Resolution Network
Authors: Aupendu Kar, Prabir Kumar Biswas
Date: 2019-03-22 00:00:00
Proceeding: CVPR 2021 1
Tasks: Adversarial Defense, Image Reconstruction, Image Super-Resolution, Super-Resolution

Convolutional neural network (CNN) has achieved unprecedented success in image super-resolution tasks in recent years. However, the network's performance depends on the distribution of the training sets and degrades on out-of-distribution samples. This paper adopts a Bayesian approach for estimating uncertainty associated with output and applies it in a deep image super-resolution model to address the concern mentioned above. We use the uncertainty estimation technique using the batch-normalization layer, where stochasticity of the batch mean and variance generate Monte-Carlo (MC) samples. The MC samples, which are nothing but different super-resolved images using different stochastic parameters, reconstruct the image, and provide a confidence or uncertainty map of the reconstruction. We propose a faster approach for MC sample generation, and it allows the variable image size during testing. Therefore, it will be useful for image reconstruction domain. Our experimental findings show that this uncertainty map strongly relates to the quality of reconstruction generated by the deep CNN model and explains its limitation. Furthermore, this paper proposes an approach to reduce the model's uncertainty for an input image, and it helps to defend the adversarial attacks on the image super-resolution model. The proposed uncertainty reduction technique also improves the performance of the model for out-of-distribution test images. To the best of our knowledge, we are the first to propose an adversarial defense mechanism in any image reconstruction domain.
Forecasting, Causality, and Impulse Response with Neural Vector Autoregressions
Authors: Kurt Izak Cabanilla, Kevin Thomas Go
Date: 2019-03-22 00:00:00
Tasks: Time Series, Time Series Analysis

Incorporating nonlinearity is paramount to predicting the future states of a dynamical system, its response to shocks, and its underlying causal network. However, most existing methods for causality detection and impulse response, such as Vector Autoregression (VAR), assume linearity and are thus unable to capture the complexity. Here, we introduce a vector autoencoder nonlinear autoregression neural network (VANAR) capable of both automatic time series feature extraction for its inputs and functional form estimation. We evaluate VANAR in three ways: first in terms of pure forecast accuracy, second in terms of detecting the correct causality between variables, and lastly in terms of impulse response where we model trajectories given external shocks. These tests were performed on a simulated nonlinear chaotic system and an empirical system using Philippine macroeconomic data. Results show that VANAR significantly outperforms VAR in the forecast and causality tests. VANAR has consistently superior accuracy even over state of the art models such as SARIMA and TBATS. For the impulse response test, both models fail to predict the shocked trajectories of the nonlinear chaotic system. VANAR was robust in its ability to model a wide variety of dynamics, from chaotic, high noise, and low data environments to macroeconomic systems.
Learning with Delayed Synaptic Plasticity
Authors: Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu, George Fletcher, Mykola Pechenizkiy
Date: 2019-03-22 00:00:00

The plasticity property of biological neural networks allows them to perform
learning and optimize their behavior by changing their configuration. Inspired
by biology, plasticity can be modeled in artificial neural networks by using
Hebbian learning rules, i.e. rules that update synapses based on the neuron
activations and reinforcement signals. However, the distal reward problem
arises when the reinforcement signals are not available immediately after each
network output to associate the neuron activations that contributed to
receiving the reinforcement signal. In this work, we extend Hebbian plasticity
rules to allow learning in distal reward cases. We propose the use of neuron
activation traces (NATs) to provide additional data storage in each synapse to
keep track of the activation of the neurons. Delayed reinforcement signals are
provided after each episode relative to the networks' performance during the
previous episode. We employ genetic algorithms to evolve delayed synaptic
plasticity (DSP) rules and perform synaptic updates based on NATs and delayed
reinforcement signals. We compare DSP with an analogous hill climbing algorithm
that does not incorporate domain knowledge introduced with the NATs, and show
that the synaptic updates performed by the DSP rules demonstrate more effective
training performance relative to the HC algorithm.
Forest structure in epigenetic landscapes
Authors: Yuriria Cortes-Poza, J. Rogelio Perez-Buendia
Date: 2019-03-22 00:00:00

Morphogenesis is the biological process that causes the emergence and changes
of patterns (tissues and organs) in living organisms. It is a robust,
self-organising mechanism, governed by Genetic Regulatory Networks (GRN), that
hasn't been thoroughly understood. In this work we propose Epigenetic Forests
as a tool to study morphogenesis and to extract valuable information from GRN.
Our method unfolds the richness and structure within the GRN.
  As a case study, we analyze the GRN during cell fate determination during the
early stages of development of the flower Arabidopsis thaliana and its spatial
dynamics. By using a genetic algorithm we optimize cell differentiation in our
model and correctly recover the architecture of the flower.
Gradient-only line searches: An Alternative to Probabilistic Line Searches
Authors: Dominic Kafka, Daniel Wilke
Date: 2019-03-22 00:00:00
Tasks: global-optimization

Step sizes in neural network training are largely determined using predetermined rules such as fixed learning rates and learning rate schedules. These require user input or expensive global optimization strategies to determine their functional form and associated hyperparameters. Line searches are capable of adaptively resolving learning rate schedules. However, due to discontinuities induced by mini-batch sub-sampling, they have largely fallen out of favour. Notwithstanding, probabilistic line searches, which use statistical surrogates over a limited spatial domain, have recently demonstrated viability in resolving learning rates for stochastic loss functions. This paper introduces an alternative paradigm, Gradient-Only Line Searches that are Inexact (GOLS-I), as an alternative strategy to automatically determine learning rates in stochastic loss functions over a range of 15 orders of magnitude without the use of surrogates. We show that GOLS-I is a competitive strategy to reliably determine step sizes, adding high value in terms of performance, while being easy to implement.
Multi-modal Probabilistic Prediction of Interactive Behavior via an Interpretable Model
Authors: Yeping Hu, Wei Zhan, Liting Sun, Masayoshi Tomizuka
Date: 2019-03-22 00:00:00
Tasks: valid

For autonomous agents to successfully operate in real world, the ability to anticipate future motions of surrounding entities in the scene can greatly enhance their safety levels since potentially dangerous situations could be avoided in advance. While impressive results have been shown on predicting each agent's behavior independently, we argue that it is not valid to consider road entities individually since transitions of vehicle states are highly coupled. Moreover, as the predicted horizon becomes longer, modeling prediction uncertainties and multi-modal distributions over future sequences will turn into a more challenging task. In this paper, we address this challenge by presenting a multi-modal probabilistic prediction approach. The proposed method is based on a generative model and is capable of jointly predicting sequential motions of each pair of interacting agents. Most importantly, our model is interpretable, which can explain the underneath logic as well as obtain more reliability to use in real applications. A complicate real-world roundabout scenario is utilized to implement and examine the proposed method.
Deep Fictitious Play for Stochastic Differential Games
Authors: Ruimeng Hu
Date: 2019-03-22 00:00:00
Tasks: Deep Learning, GPU, Learning Theory

In this paper, we apply the idea of fictitious play to design deep neural networks (DNNs), and develop deep learning theory and algorithms for computing the Nash equilibrium of asymmetric $N$-player non-zero-sum stochastic differential games, for which we refer as \emph{deep fictitious play}, a multi-stage learning process. Specifically at each stage, we propose the strategy of letting individual player optimize her own payoff subject to the other players' previous actions, equivalent to solve $N$ decoupled stochastic control optimization problems, which are approximated by DNNs. Therefore, the fictitious play strategy leads to a structure consisting of $N$ DNNs, which only communicate at the end of each stage. The resulted deep learning algorithm based on fictitious play is scalable, parallel and model-free, {\it i.e.}, using GPU parallelization, it can be applied to any $N$-player stochastic differential game with different symmetries and heterogeneities ({\it e.g.}, existence of major players). We illustrate the performance of the deep learning algorithm by comparing to the closed-form solution of the linear quadratic game. Moreover, we prove the convergence of fictitious play under appropriate assumptions, and verify that the convergent limit forms an open-loop Nash equilibrium. We also discuss the extensions to other strategies designed upon fictitious play and closed-loop Nash equilibrium in the end.
Deep Hierarchical Reinforcement Learning Based Recommendations via Multi-goals Abstraction
Authors: Dongyang Zhao, Liang Zhang, Bo Zhang, Lizhou Zheng, Yongjun Bao, Weipeng Yan
Date: 2019-03-22 00:00:00
Tasks: Hierarchical Reinforcement Learning, Recommendation Systems, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The recommender system is an important form of intelligent application, which
assists users to alleviate from information redundancy. Among the metrics used
to evaluate a recommender system, the metric of conversion has become more and
more important. The majority of existing recommender systems perform poorly on
the metric of conversion due to its extremely sparse feedback signal. To tackle
this challenge, we propose a deep hierarchical reinforcement learning based
recommendation framework, which consists of two components, i.e., high-level
agent and low-level agent. The high-level agent catches long-term sparse
conversion signals, and automatically sets abstract goals for low-level agent,
while the low-level agent follows the abstract goals and interacts with
real-time environment. To solve the inherent problem in hierarchical
reinforcement learning, we propose a novel deep hierarchical reinforcement
learning algorithm via multi-goals abstraction (HRL-MG). Our proposed algorithm
contains three characteristics: 1) the high-level agent generates multiple
goals to guide the low-level agent in different stages, which reduces the
difficulty of approaching high-level goals; 2) different goals share the same
state encoder parameters, which increases the update frequency of the
high-level agent and thus accelerates the convergence of our proposed
algorithm; 3) an appreciate benefit assignment function is designed to allocate
rewards in each goal so as to coordinate different goals in a consistent
direction. We evaluate our proposed algorithm based on a real-world e-commerce
dataset and validate its effectiveness.
Few-shot Adaptive Faster R-CNN
Authors: Tao Wang, Xiaopeng Zhang, Li Yuan, Jiashi Feng
Date: 2019-03-22 00:00:00
Proceeding: CVPR 2019 6
Tasks: Domain Adaptation, object-detection, Object Detection, Unsupervised Domain Adaptation

To mitigate the detection performance drop caused by domain shift, we aim to
develop a novel few-shot adaptation approach that requires only a few target
domain images with limited bounding box annotations. To this end, we first
observe several significant challenges. First, the target domain data is highly
insufficient, making most existing domain adaptation methods ineffective.
Second, object detection involves simultaneous localization and classification,
further complicating the model adaptation process. Third, the model suffers
from over-adaptation (similar to overfitting when training with a few data
example) and instability risk that may lead to degraded detection performance
in the target domain. To address these challenges, we first introduce a pairing
mechanism over source and target features to alleviate the issue of
insufficient target domain samples. We then propose a bi-level module to adapt
the source trained detector to the target domain: 1) the split pooling based
image level adaptation module uniformly extracts and aligns paired local patch
features over locations, with different scale and aspect ratio; 2) the instance
level adaptation module semantically aligns paired object features while avoids
inter-class confusion. Meanwhile, a source model feature regularization (SMFR)
is applied to stabilize the adaptation process of the two modules. Combining
these contributions gives a novel few-shot adaptive Faster-RCNN framework,
termed FAFRCNN, which effectively adapts to target domain with a few labeled
samples. Experiments with multiple datasets show that our model achieves new
state-of-the-art performance under both the interested few-shot domain
adaptation(FDA) and unsupervised domain adaptation(UDA) setting.
Macro Action Reinforcement Learning with Sequence Disentanglement using Variational Autoencoder
Authors: Heecheol Kim, Masanori Yamada, Kosuke Miyoshi, Hiroshi Yamakawa
Date: 2019-03-22 00:00:00
Tasks: Disentanglement, General Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

One problem in the application of reinforcement learning to real-world problems is the curse of dimensionality on the action space. Macro actions, a sequence of primitive actions, have been studied to diminish the dimensionality of the action space with regard to the time axis. However, previous studies relied on humans defining macro actions or assumed macro actions as repetitions of the same primitive actions. We present Factorized Macro Action Reinforcement Learning (FaMARL) which autonomously learns disentangled factor representation of a sequence of actions to generate macro actions that can be directly applied to general reinforcement learning algorithms. FaMARL exhibits higher scores than other reinforcement learning algorithms on environments that require an extensive amount of search.
3D Face Reconstruction from A Single Image Assisted by 2D Face Images in the Wild
Authors: Xiaoguang Tu, Jian Zhao, Zi-Hang Jiang, Yao Luo, Mei Xie, Yang Zhao, Linxiao He, Zheng Ma, Jiashi Feng
Date: 2019-03-22 00:00:00
Tasks: 3D Face Reconstruction, Face Alignment, Face Model, Face Reconstruction, Self-Supervised Learning

3D face reconstruction from a single 2D image is a challenging problem with broad applications. Recent methods typically aim to learn a CNN-based 3D face model that regresses coefficients of 3D Morphable Model (3DMM) from 2D images to render 3D face reconstruction or dense face alignment. However, the shortage of training data with 3D annotations considerably limits performance of those methods. To alleviate this issue, we propose a novel 2D-assisted self-supervised learning (2DASL) method that can effectively use "in-the-wild" 2D face images with noisy landmark information to substantially improve 3D face model learning. Specifically, taking the sparse 2D facial landmarks as additional information, 2DSAL introduces four novel self-supervision schemes that view the 2D landmark and 3D landmark prediction as a self-mapping process, including the 2D and 3D landmark self-prediction consistency, cycle-consistency over the 2D landmark prediction and self-critic over the predicted 3DMM coefficients based on landmark predictions. Using these four self-supervision schemes, the 2DASL method significantly relieves demands on the the conventional paired 2D-to-3D annotations and gives much higher-quality 3D face models without requiring any additional 3D annotations. Experiments on multiple challenging datasets show that our method outperforms state-of-the-arts for both 3D face reconstruction and dense face alignment by a large margin.
Binary Space Partitioning Forests
Authors: Xuhui Fan, Bin Li, Scott Anthony Sisson
Date: 2019-03-22 00:00:00
Tasks: regression

The Binary Space Partitioning~(BSP)-Tree process is proposed to produce
flexible 2-D partition structures which are originally used as a Bayesian
nonparametric prior for relational modelling. It can hardly be applied to other
learning tasks such as regression trees because extending the BSP-Tree process
to a higher dimensional space is nontrivial. This paper is the first attempt to
extend the BSP-Tree process to a d-dimensional (d>2) space. We propose to
generate a cutting hyperplane, which is assumed to be parallel to d-2
dimensions, to cut each node in the d-dimensional BSP-tree. By designing a
subtle strategy to sample two free dimensions from d dimensions, the extended
BSP-Tree process can inherit the essential self-consistency property from the
original version. Based on the extended BSP-Tree process, an ensemble model,
which is named the BSP-Forest, is further developed for regression tasks.
Thanks to the retained self-consistency property, we can thus significantly
reduce the geometric calculations in the inference stage. Compared to its
counterpart, the Mondrian Forest, the BSP-Forest can achieve similar
performance with fewer cuts due to its flexibility. The BSP-Forest also
outperforms other (Bayesian) regression forests on a number of real-world data
sets.