pwc-archive/papers-with-abstracts huggingface dataset:
Exploring applications of deep reinforcement learning for real-world autonomous driving systems
Authors: Victor Talpaert, Ibrahim Sobh, B Ravi Kiran, Patrick Mannion, Senthil Yogamani, Ahmad El-Sallab, Patrick Perez
Date: 2019-01-06 00:00:00
Tasks: Autonomous Driving, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep Reinforcement Learning (DRL) has become increasingly powerful in recent
years, with notable achievements such as Deepmind's AlphaGo. It has been
successfully deployed in commercial vehicles like Mobileye's path planning
system. However, a vast majority of work on DRL is focused on toy examples in
controlled synthetic car simulator environments such as TORCS and CARLA. In
general, DRL is still at its infancy in terms of usability in real-world
applications. Our goal in this paper is to encourage real-world deployment of
DRL in various autonomous driving (AD) applications. We first provide an
overview of the tasks in autonomous driving systems, reinforcement learning
algorithms and applications of DRL to AD systems. We then discuss the
challenges which must be addressed to enable further progress towards
real-world deployment.
RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials
Authors: Despoina Paschalidou, Ali Osman Ulusoy, Carolin Schmitt, Luc van Gool, Andreas Geiger
Date: 2019-01-06 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Reconstruction

In this paper, we consider the problem of reconstructing a dense 3D model
using images captured from different views. Recent methods based on
convolutional neural networks (CNN) allow learning the entire task from data.
However, they do not incorporate the physics of image formation such as
perspective geometry and occlusion. Instead, classical approaches based on
Markov Random Fields (MRF) with ray-potentials explicitly model these physical
processes, but they cannot cope with large surface appearance variations across
different viewpoints. In this paper, we propose RayNet, which combines the
strengths of both frameworks. RayNet integrates a CNN that learns
view-invariant feature representations with an MRF that explicitly encodes the
physics of perspective projection and occlusion. We train RayNet end-to-end
using empirical risk minimization. We thoroughly evaluate our approach on
challenging real-world datasets and demonstrate its benefits over a piece-wise
trained baseline, hand-crafted models as well as other learning-based
approaches.
Supervised Neural Networks for Helioseismic Ring-Diagram Inversions
Authors: Rasha Alshehhi, Chris S. Hanson, Laurent Gizon, Shravan Hanasoge
Date: 2019-01-06 00:00:00
Tasks: BIG-bench Machine Learning, CPU

The inversion of ring fit parameters to obtain subsurface flow maps in
ring-diagram analysis for 8 years of SDO observations is computationally
expensive, requiring ~3200 CPU hours. In this paper we apply machine learning
techniques to the inversion in order to speed up calculations. Specifically, we
train a predictor for subsurface flows using the mode fit parameters and the
previous inversion results, to replace future inversion requirements. We
utilize Artificial Neural Networks as a supervised learning method for
predicting the flows in 15 degree ring tiles. To demonstrate that the machine
learning results still contain the subtle signatures key to local helioseismic
studies, we use the machine learning results to study the recently discovered
solar equatorial Rossby waves. The Artificial Neural Network is computationally
efficient, able to make future flow predictions of an entire Carrington
rotation in a matter of seconds, which is much faster than the current ~31 CPU
hours. Initial training of the networks requires ~3 CPU hours. The trained
Artificial Neural Network can achieve a root mean-square error equal to
approximately half that reported for the velocity inversions, demonstrating the
accuracy of the machine learning (and perhaps the overestimation of the
original errors from the ring-diagram pipeline). We find the signature of
equatorial Rossby waves in the machine learning flows covering six years of
data, demonstrating that small-amplitude signals are maintained. The recovery
of Rossby waves in the machine learning flow maps can be achieved with only one
Carrington rotation (27.275 days) of training data. We have shown that machine
learning can be applied to, and perform more efficiently than the current
ring-diagram inversion. The computation burden of the machine learning includes
3 CPU hours for initial training, then around 0.0001 CPU hours for future
predictions.
Enhancing Sound Texture in CNN-Based Acoustic Scene Classification
Authors: Yuzhong Wu, Tan Lee
Date: 2019-01-06 00:00:00
Tasks: Acoustic Scene Classification, Classification, General Classification, Scene Classification

Acoustic scene classification is the task of identifying the scene from which
the audio signal is recorded. Convolutional neural network (CNN) models are
widely adopted with proven successes in acoustic scene classification. However,
there is little insight on how an audio scene is perceived in CNN, as what have
been demonstrated in image recognition research. In the present study, the
Class Activation Mapping (CAM) is utilized to analyze how the log-magnitude
Mel-scale filter-bank (log-Mel) features of different acoustic scenes are
learned in a CNN classifier. It is noted that distinct high-energy
time-frequency components of audio signals generally do not correspond to
strong activation on CAM, while the background sound texture are well learned
in CNN. In order to make the sound texture more salient, we propose to apply
the Difference of Gaussian (DoG) and Sobel operator to process the log-Mel
features and enhance edge information of the time-frequency image. Experimental
results on the DCASE 2017 ASC challenge show that using edge enhanced log-Mel
images as input feature of CNN significantly improves the performance of audio
scene classification.
Understanding the (un)interpretability of natural image distributions using generative models
Authors: Ryen Krusinga, Sohil Shah, Matthias Zwicker, Tom Goldstein, David Jacobs
Date: 2019-01-06 00:00:00
Tasks: Density Estimation

Probability density estimation is a classical and well studied problem, but
standard density estimation methods have historically lacked the power to model
complex and high-dimensional image distributions. More recent generative models
leverage the power of neural networks to implicitly learn and represent
probability models over complex images. We describe methods to extract explicit
probability density estimates from GANs, and explore the properties of these
image density functions. We perform sanity check experiments to provide
evidence that these probabilities are reasonable. However, we also show that
density functions of natural images are difficult to interpret and thus limited
in use. We study reasons for this lack of interpretability, and show that we
can get interpretability back by doing density estimation on latent
representations of images.
MAE: Mutual Posterior-Divergence Regularization for Variational AutoEncoders
Authors: Xuezhe Ma, Chunting Zhou, Eduard Hovy
Date: 2019-01-06 00:00:00
Proceeding: ICLR 2019 5
Tasks: Density Estimation, Image Generation, Representation Learning

Variational Autoencoder (VAE), a simple and effective deep generative model,
has led to a number of impressive empirical successes and spawned many advanced
variants and theoretical investigations. However, recent studies demonstrate
that, when equipped with expressive generative distributions (aka. decoders),
VAE suffers from learning uninformative latent representations with the
observation called KL Varnishing, in which case VAE collapses into an
unconditional generative model. In this work, we introduce mutual
posterior-divergence regularization, a novel regularization that is able to
control the geometry of the latent space to accomplish meaningful
representation learning, while achieving comparable or superior capability of
density estimation. Experiments on three image benchmark datasets demonstrate
that, when equipped with powerful decoders, our model performs well both on
density estimation and representation learning.
Channel Locality Block: A Variant of Squeeze-and-Excitation
Authors: Huayu Li
Date: 2019-01-06 00:00:00

Attention mechanism is a hot spot in deep learning field. Using channel
attention model is an effective method for improving the performance of the
convolutional neural network. Squeeze-and-Excitation block takes advantage of
the channel dependence, selectively emphasizing the important channels and
compressing the relatively useless channel. In this paper, we proposed a
variant of SE block based on channel locality. Instead of using full connection
layers to explore the global channel dependence, we adopt convolutional layers
to learn the correlation between the nearby channels. We term this new
algorithm Channel Locality(C-Local) block. We evaluate SE block and C-Local
block by applying them to different CNNs architectures on cifar-10 dataset. We
observed that our C-Local block got higher accuracy than SE block did.
What Should I Do Now? Marrying Reinforcement Learning and Symbolic Planning
Authors: Daniel Gordon, Dieter Fox, Ali Farhadi
Date: 2019-01-06 00:00:00
Tasks: Deep Reinforcement Learning, Question Answering, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Long-term planning poses a major difficulty to many reinforcement learning
algorithms. This problem becomes even more pronounced in dynamic visual
environments. In this work we propose Hierarchical Planning and Reinforcement
Learning (HIP-RL), a method for merging the benefits and capabilities of
Symbolic Planning with the learning abilities of Deep Reinforcement Learning.
We apply HIPRL to the complex visual tasks of interactive question answering
and visual semantic planning and achieve state-of-the-art results on three
challenging datasets all while taking fewer steps at test time and training in
fewer iterations. Sample results can be found at youtu.be/0TtWJ_0mPfI
LanczosNet: Multi-Scale Deep Graph Convolutional Networks
Authors: Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel
Date: 2019-01-06 00:00:00
Proceeding: ICLR 2019 5
Tasks: Node Classification

We propose the Lanczos network (LanczosNet), which uses the Lanczos algorithm to construct low rank approximations of the graph Laplacian for graph convolution. Relying on the tridiagonal decomposition of the Lanczos algorithm, we not only efficiently exploit multi-scale information via fast approximated computation of matrix power but also design learnable spectral filters. Being fully differentiable, LanczosNet facilitates both graph kernel learning as well as learning node embeddings. We show the connection between our LanczosNet and graph based manifold learning methods, especially the diffusion maps. We benchmark our model against several recent deep graph networks on citation networks and QM8 quantum chemistry dataset. Experimental results show that our model achieves the state-of-the-art performance in most tasks. Code is released at: \url{https://github.com/lrjconan/LanczosNetwork}.
Dynamic Visualization and Fast Computation for Convex Clustering via Algorithmic Regularization
Authors: Michael Weylandt, John Nagorski, Genevera I. Allen
Date: 2019-01-06 00:00:00
Tasks: Clustering

Convex clustering is a promising new approach to the classical problem of clustering, combining strong performance in empirical studies with rigorous theoretical foundations. Despite these advantages, convex clustering has not been widely adopted, due to its computationally intensive nature and its lack of compelling visualizations. To address these impediments, we introduce Algorithmic Regularization, an innovative technique for obtaining high-quality estimates of regularization paths using an iterative one-step approximation scheme. We justify our approach with a novel theoretical result, guaranteeing global convergence of the approximate path to the exact solution under easily-checked non-data-dependent assumptions. The application of algorithmic regularization to convex clustering yields the Convex Clustering via Algorithmic Regularization Paths (CARP) algorithm for computing the clustering solution path. On example data sets from genomics and text analysis, CARP delivers over a 100-fold speed-up over existing methods, while attaining a finer approximation grid than standard methods. Furthermore, CARP enables improved visualization of clustering solutions: the fine solution grid returned by CARP can be used to construct a convex clustering-based dendrogram, as well as forming the basis of a dynamic path-wise visualization based on modern web technologies. Our methods are implemented in the open-source R package clustRviz, available at https://github.com/DataSlingers/clustRviz.
Bilinear Supervised Hashing Based on 2D Image Features
Authors: Yujuan Ding, Wai Kueng Wong, Zhihui Lai, Zheng Zhang
Date: 2019-01-05 00:00:00
Tasks: Representation Learning, Retrieval

Hashing has been recognized as an efficient representation learning method to
effectively handle big data due to its low computational complexity and memory
cost. Most of the existing hashing methods focus on learning the
low-dimensional vectorized binary features based on the high-dimensional raw
vectorized features. However, studies on how to obtain preferable binary codes
from the original 2D image features for retrieval is very limited. This paper
proposes a bilinear supervised discrete hashing (BSDH) method based on 2D image
features which utilizes bilinear projections to binarize the image matrix
features such that the intrinsic characteristics in the 2D image space are
preserved in the learned binary codes. Meanwhile, the bilinear projection
approximation and vectorization binary codes regression are seamlessly
integrated together to formulate the final robust learning framework.
Furthermore, a discrete optimization strategy is developed to alternatively
update each variable for obtaining the high-quality binary codes. In addition,
two 2D image features, traditional SURF-based FVLAD feature and CNN-based
AlexConv5 feature are designed for further improving the performance of the
proposed BSDH method. Results of extensive experiments conducted on four
benchmark datasets show that the proposed BSDH method almost outperforms all
competing hashing methods with different input features by different evaluation
protocols.
Addressing Objects and Their Relations: The Conversational Entity Dialogue Model
Authors: Stefan Ultes, Paweł\ Budzianowski, Iñigo Casanueva, Lina Rojas-Barahona, Bo-Hsiang Tseng, Yen-chen Wu, Steve Young, Milica Gašić
Date: 2019-01-05 00:00:00
Proceeding: WS 2018 7
Tasks: Spoken Dialogue Systems

Statistical spoken dialogue systems usually rely on a single- or multi-domain
dialogue model that is restricted in its capabilities of modelling complex
dialogue structures, e.g., relations. In this work, we propose a novel dialogue
model that is centred around entities and is able to model relations as well as
multiple entities of the same type. We demonstrate in a prototype
implementation benefits of relation modelling on the dialogue level and show
that a trained policy using these relations outperforms the multi-domain
baseline. Furthermore, we show that by modelling the relations on the dialogue
level, the system is capable of processing relations present in the user input
and even learns to address them in the system response.
Rethinking the Artificial Neural Networks: A Mesh of Subnets with a Central Mechanism for Storing and Predicting the Data
Authors: Usman Ahmad, Hong Song, Awais Bilal, Shahid Mahmood, Asad Ullah, Uzair Saeed
Date: 2019-01-05 00:00:00

The Artificial Neural Networks (ANNs) have been originally designed to
function like a biological neural network, but does an ANN really work in the
same way as a biological neural network? As we know, the human brain holds
information in its memory cells, so if the ANNs use the same model as our
brains, they should store datasets in a similar manner. The most popular type
of ANN architecture is based on a layered structure of neurons, whereas a human
brain has trillions of complex interconnections of neurons continuously
establishing new connections, updating existing ones, and removing the
irrelevant connections across different parts of the brain. In this paper, we
propose a novel approach to building ANNs which are truly inspired by the
biological network containing a mesh of subnets controlled by a central
mechanism. A subnet is a network of neurons that hold the dataset values. We
attempt to address the following fundamental questions: (1) What is the
architecture of the ANN model? Whether the layered architecture is the most
appropriate choice? (2) Whether a neuron is a process or a memory cell? (3)
What is the best way of interconnecting neurons and what weight-assignment
mechanism should be used? (4) How to incorporate prior knowledge, bias, and
generalizations for features extraction and prediction? Our proposed ANN
architecture leverages the accuracy on textual data and our experimental
findings confirm the effectiveness of our model. We also collaborate with the
construction of the ANN model for storing and processing the images.
Early Prediction of Alzheimer's Disease Dementia Based on Baseline Hippocampal MRI and 1-Year Follow-Up Cognitive Measures Using Deep Recurrent Neural Networks
Authors: Hongming Li, Yong Fan
Date: 2019-01-05 00:00:00
Tasks: General Classification

Multi-modal biological, imaging, and neuropsychological markers have
demonstrated promising performance for distinguishing Alzheimer's disease (AD)
patients from cognitively normal elders. However, it remains difficult to early
predict when and which mild cognitive impairment (MCI) individuals will convert
to AD dementia. Informed by pattern classification studies which have
demonstrated that pattern classifiers built on longitudinal data could achieve
better classification performance than those built on cross-sectional data, we
develop a deep learning model based on recurrent neural networks (RNNs) to
learn informative representation and temporal dynamics of longitudinal
cognitive measures of individual subjects and combine them with baseline
hippocampal MRI for building a prognostic model of AD dementia progression.
Experimental results on a large cohort of MCI subjects have demonstrated that
the deep learning model could learn informative measures from longitudinal data
for characterizing the progression of MCI subjects to AD dementia, and the
prognostic model could early predict AD progression with high accuracy.
Deep Convolutional Neural Networks for Imaging Data Based Survival Analysis of Rectal Cancer
Authors: Hongming Li, Pamela Boimel, James Janopaul-Naylor, Haoyu Zhong, Ying Xiao, Edgar Ben-Josef, Yong Fan
Date: 2019-01-05 00:00:00
Tasks: Deep Learning, Survival Analysis, Survival Prediction

Recent radiomic studies have witnessed promising performance of deep learning
techniques in learning radiomic features and fusing multimodal imaging data.
Most existing deep learning based radiomic studies build predictive models in a
setting of pattern classification, not appropriate for survival analysis
studies where some data samples have incomplete observations. To improve
existing survival analysis techniques whose performance is hinged on imaging
features, we propose a deep learning method to build survival regression models
by optimizing imaging features with deep convolutional neural networks (CNNs)
in a proportional hazards model. To make the CNNs applicable to tumors with
varied sizes, a spatial pyramid pooling strategy is adopted. Our method has
been validated based on a simulated imaging dataset and a FDG-PET/CT dataset of
rectal cancer patients treated for locally advanced rectal cancer. Compared
with survival prediction models built upon hand-crafted radiomic features using
Cox proportional hazards model and random survival forests, our method achieved
competitive prediction performance.
Forensic shoe-print identification: a brief survey
Authors: Imad Rida, Lunke Fei, Hugo Proença, Amine Nait-ali, Abdenour Hadid
Date: 2019-01-05 00:00:00
Tasks: Survey

As an advanced research topic in forensics science, automatic shoe-print identification has been extensively studied in the last two decades, since shoe marks are the clues most frequently left in a crime scene. Hence, these impressions provide a pertinent evidence for the proper progress of investigations in order to identify the potential criminals. The main goal of this survey is to provide a cohesive overview of the research carried out in forensic shoe-print identification and its basic background. Apart defining the problem and describing the phases that typically compose the processing chain of shoe-print identification, we provide a summary/comparison of the state-of-the-art approaches, in order to guide the neophyte and help to advance the research topic. This is done through introducing simple and basic taxonomies as well as summaries of the state-of-the-art performance. Lastly, we discuss the current open problems and challenges in this research topic, point out for promising directions in this field.
Poincaré Wasserstein Autoencoder
Authors: Ivan Ovinnikov
Date: 2019-01-05 00:00:00
Proceeding: ICLR 2020 1
Tasks: Link Prediction

This work presents a reformulation of the recently proposed Wasserstein autoencoder framework on a non-Euclidean manifold, the Poincar\'e ball model of the hyperbolic space. By assuming the latent space to be hyperbolic, we can use its intrinsic hierarchy to impose structure on the learned latent space representations. We demonstrate the model in the visual domain to analyze some of its properties and show competitive results on a graph link prediction task.
Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene Understanding
Authors: Dengxin Dai, Christos Sakaridis, Simon Hecker, Luc van Gool
Date: 2019-01-05 00:00:00
Tasks: Domain Adaptation, Scene Understanding, Segmentation, Semantic Segmentation

This work addresses the problem of semantic scene understanding under fog.
Although marked progress has been made in semantic scene understanding, it is
mainly concentrated on clear-weather scenes. Extending semantic segmentation
methods to adverse weather conditions such as fog is crucial for outdoor
applications. In this paper, we propose a novel method, named Curriculum Model
Adaptation (CMAda), which gradually adapts a semantic segmentation model from
light synthetic fog to dense real fog in multiple steps, using both labeled
synthetic foggy data and unlabeled real foggy data. The method is based on the
fact that the results of semantic segmentation in moderately adverse conditions
(light fog) can be bootstrapped to solve the same problem in highly adverse
conditions (dense fog). CMAda is extensible to other adverse conditions and
provides a new paradigm for learning with synthetic data and unlabeled real
data. In addition, we present three other main stand-alone contributions: 1) a
novel method to add synthetic fog to real, clear-weather scenes using semantic
input; 2) a new fog density estimator; 3) a novel fog densification method to
densify the fog in real foggy scenes without using depth; and 4) the Foggy
Zurich dataset comprising 3808 real foggy images, with pixel-level semantic
annotations for 40 images under dense fog. Our experiments show that 1) our fog
simulation and fog density estimator outperform their state-of-the-art
counterparts with respect to the task of semantic foggy scene understanding
(SFSU); 2) CMAda improves the performance of state-of-the-art models for SFSU
significantly, benefiting both from our synthetic and real foggy data. The
datasets and code are available at the project website.