pwc-archive/papers-with-abstracts huggingface dataset:
Neural-Attention-Based Deep Learning Architectures for Modeling Traffic Dynamics on Lane Graphs
Authors: Matthew A. Wright, Simon F. G. Ehlers, Roberto Horowitz
Date: 2019-04-18 00:00:00

Deep neural networks can be powerful tools, but require careful application-specific design to ensure that the most informative relationships in the data are learnable. In this paper, we apply deep neural networks to the nonlinear spatiotemporal physics problem of vehicle traffic dynamics. We consider problems of estimating macroscopic quantities (e.g., the queue at an intersection) at a lane level. First-principles modeling at the lane scale has been a challenge due to complexities in modeling social behaviors like lane changes, and those behaviors' resultant macro-scale effects. Following domain knowledge that upstream/downstream lanes and neighboring lanes affect each others' traffic flows in distinct ways, we apply a form of neural attention that allows the neural network layers to aggregate information from different lanes in different manners. Using a microscopic traffic simulator as a testbed, we obtain results showing that an attentional neural network model can use information from nearby lanes to improve predictions, and, that explicitly encoding the lane-to-lane relationship types significantly improves performance. We also demonstrate the transfer of our learned neural network to a more complex road network, discuss how its performance degradation may be attributable to new traffic behaviors induced by increased topological complexity, and motivate learning dynamics models from many road network topologies.
Deep Residual Autoencoders for Expectation Maximization-inspired Dictionary Learning
Authors: Bahareh Tolooshams, Sourav Dey, Demba Ba
Date: 2019-04-18 00:00:00
Tasks: Denoising, Dictionary Learning, Image Denoising

We introduce a neural-network architecture, termed the constrained recurrent sparse autoencoder (CRsAE), that solves convolutional dictionary learning problems, thus establishing a link between dictionary learning and neural networks. Specifically, we leverage the interpretation of the alternating-minimization algorithm for dictionary learning as an approximate Expectation-Maximization algorithm to develop autoencoders that enable the simultaneous training of the dictionary and regularization parameter (ReLU bias). The forward pass of the encoder approximates the sufficient statistics of the E-step as the solution to a sparse coding problem, using an iterative proximal gradient algorithm called FISTA. The encoder can be interpreted either as a recurrent neural network or as a deep residual network, with two-sided ReLU non-linearities in both cases. The M-step is implemented via a two-stage back-propagation. The first stage relies on a linear decoder applied to the encoder and a norm-squared loss. It parallels the dictionary update step in dictionary learning. The second stage updates the regularization parameter by applying a loss function to the encoder that includes a prior on the parameter motivated by Bayesian statistics. We demonstrate in an image-denoising task that CRsAE learns Gabor-like filters, and that the EM-inspired approach for learning biases is superior to the conventional approach. In an application to recordings of electrical activity from the brain, we demonstrate that CRsAE learns realistic spike templates and speeds up the process of identifying spike times by 900x compared to algorithms based on convex optimization.
Generating Training Data for Denoising Real RGB Images via Camera Pipeline Simulation
Authors: Ronnachai Jaroensri, Camille Biscarrat, Miika Aittala, Frédo Durand
Date: 2019-04-18 00:00:00
Tasks: Demosaicking, Denoising, Image Reconstruction

Image reconstruction techniques such as denoising often need to be applied to
the RGB output of cameras and cellphones. Unfortunately, the commonly used
additive white noise (AWGN) models do not accurately reproduce the noise and
the degradation encountered on these inputs. This is particularly important for
learning-based techniques, because the mismatch between training and real world
data will hurt their generalization. This paper aims to accurately simulate the
degradation and noise transformation performed by camera pipelines. This allows
us to generate realistic degradation in RGB images that can be used to train
machine learning models. We use our simulation to study the importance of noise
modeling for learning-based denoising. Our study shows that a realistic noise
model is required for learning to denoise real JPEG images. A neural network
trained on realistic noise outperforms the one trained with AWGN by 3 dB. An
ablation study of our pipeline shows that simulating denoising and demosaicking
is important to this improvement and that realistic demosaicking algorithms,
which have been rarely considered, is needed. We believe this simulation will
also be useful for other image reconstruction tasks, and we will distribute our
code publicly.
Uncrowded Hypervolume Improvement: COMO-CMA-ES and the Sofomore framework
Authors: Cheikh Touré, Nikolaus Hansen, Anne Auger, Dimo Brockhoff
Date: 2019-04-18 00:00:00

We present a framework to build a multiobjective algorithm from
single-objective ones. This framework addresses the $p \times n$-dimensional
problem of finding p solutions in an n-dimensional search space, maximizing an
indicator by dynamic subspace optimization. Each single-objective algorithm
optimizes the indicator function given $p - 1$ fixed solutions. Crucially,
dominated solutions minimize their distance to the empirical Pareto front
defined by these $p - 1$ solutions. We instantiate the framework with CMA-ES as
single-objective optimizer. The new algorithm, COMO-CMA-ES, is empirically
shown to converge linearly on bi-objective convex-quadratic problems and is
compared to MO-CMA-ES, NSGA-II and SMS-EMOA.
(De)Constructing Bias on Skin Lesion Datasets
Authors: Alceu Bissoto, Michel Fornaciali, Eduardo Valle, Sandra Avila
Date: 2019-04-18 00:00:00
Tasks: BIG-bench Machine Learning

Melanoma is the deadliest form of skin cancer. Automated skin lesion analysis
plays an important role for early detection. Nowadays, the ISIC Archive and the
Atlas of Dermoscopy dataset are the most employed skin lesion sources to
benchmark deep-learning based tools. However, all datasets contain biases,
often unintentional, due to how they were acquired and annotated. Those biases
distort the performance of machine-learning models, creating spurious
correlations that the models can unfairly exploit, or, contrarily destroying
cogent correlations that the models could learn. In this paper, we propose a
set of experiments that reveal both types of biases, positive and negative, in
existing skin lesion datasets. Our results show that models can correctly
classify skin lesion images without clinically-meaningful information:
disturbingly, the machine-learning model learned over images where no
information about the lesion remains, presents an accuracy above the AI
benchmark curated with dermatologists' performances. That strongly suggests
spurious correlations guiding the models. We fed models with additional
clinically meaningful information, which failed to improve the results even
slightly, suggesting the destruction of cogent correlations. Our main findings
raise awareness of the limitations of models trained and evaluated in small
datasets such as the ones we evaluated, and may suggest future guidelines for
models intended for real-world deployment.
On The Classification-Distortion-Perception Tradeoff
Authors: Dong Liu, Haochen Zhang, Zhiwei Xiong
Date: 2019-04-18 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Classification, General Classification

Signal degradation is ubiquitous and computational restoration of degraded
signal has been investigated for many years. Recently, it is reported that the
capability of signal restoration is fundamentally limited by the
perception-distortion tradeoff, i.e. the distortion and the perceptual
difference between the restored signal and the ideal `original' signal cannot
be made both minimal simultaneously. Distortion corresponds to signal fidelity
and perceptual difference corresponds to perceptual naturalness, both of which
are important metrics in practice. Besides, there is another dimension worthy
of consideration, namely the semantic quality or the utility for recognition
purpose, of the restored signal. In this paper, we extend the previous
perception-distortion tradeoff to the case of
classification-distortion-perception (CDP) tradeoff, where we introduced the
classification error rate of the restored signal in addition to distortion and
perceptual difference. Two versions of the CDP tradeoff are considered, one
using a predefined classifier and the other dealing with the optimal classifier
for the restored signal. For both versions, we can rigorously prove the
existence of the CDP tradeoff, i.e. the distortion, perceptual difference, and
classification error rate cannot be made all minimal simultaneously. Our
findings can be useful especially for computer vision researches where some
low-level vision tasks (signal restoration) serve for high-level vision tasks
(visual understanding).
Interplanetary Transfers via Deep Representations of the Optimal Policy and/or of the Value Function
Authors: Dario Izzo, Ekin Öztürk, Marcus Märtens
Date: 2019-04-18 00:00:00

A number of applications to interplanetary trajectories have been recently
proposed based on deep networks. These approaches often rely on the
availability of a large number of optimal trajectories to learn from. In this
paper we introduce a new method to quickly create millions of optimal
spacecraft trajectories from a single nominal trajectory. Apart from the
generation of the nominal trajectory, no additional optimal control problems
need to be solved as all the trajectories, by construction, satisfy
Pontryagin's minimum principle and the relevant transversality conditions. We
then consider deep feed forward neural networks and benchmark three learning
methods on the created dataset: policy imitation, value function learning and
value function gradient learning. Our results are shown for the case of the
interplanetary trajectory optimization problem of reaching Venus orbit, with
the nominal trajectory starting from the Earth. We find that both policy
imitation and value function gradient learning are able to learn the optimal
state feedback, while in the case of value function learning the optimal policy
is not captured, only the final value of the optimal propellant mass is.
Multi-task Learning for Influence Estimation and Maximization
Authors: George Panagopoulos, Fragkiskos D. Malliaros, Michalis Vazirgiannis
Date: 2019-04-18 00:00:00
Tasks: Multi-Task Learning, Representation Learning

We address the problem of influence maximization when the social network is accompanied by diffusion cascades. In prior works, such information is used to compute influence probabilities, which is utilized by stochastic diffusion models in influence maximization. Motivated by the recent criticism on the effectiveness of diffusion models as well as the galloping advancements in influence learning, we propose IMINFECTOR (Influence Maximization with INFluencer vECTORs), a unified approach that uses representations learned from diffusion cascades to perform model-independent influence maximization that scales in real-world datasets. The first part of our methodology is a multi-task neural network that learns embeddings of nodes that initiate cascades (influencer vectors) and embeddings of nodes that participate in them (susceptible vectors). The norm of an influencer vector captures the ability of the node to create lengthy cascades and is used to estimate the expected influence spread and reduce the number of candidate seeds. In addition, the combination of influencer and susceptible vectors form the diffusion probabilities between nodes. These are used to reformulate the network as a bipartite graph and propose a greedy solution to influence maximization that retains the theoretical guarantees.We a pply our method in three sizable networks with diffusion cascades and evaluate it using cascades from future time steps. IMINFECTOR is able to scale in all of them and outperforms various competitive algorithms and metrics from the diverse landscape of influence maximization in terms of efficiency and seed set quality.
Learning a Controller Fusion Network by Online Trajectory Filtering for Vision-based UAV Racing
Authors: Matthias Müller, Guohao Li, Vincent Casser, Neil Smith, Dominik L. Michels, Bernard Ghanem
Date: 2019-04-18 00:00:00

Autonomous UAV racing has recently emerged as an interesting research
problem. The dream is to beat humans in this new fast-paced sport. A common
approach is to learn an end-to-end policy that directly predicts controls from
raw images by imitating an expert. However, such a policy is limited by the
expert it imitates and scaling to other environments and vehicle dynamics is
difficult. One approach to overcome the drawbacks of an end-to-end policy is to
train a network only on the perception task and handle control with a PID or
MPC controller. However, a single controller must be extensively tuned and
cannot usually cover the whole state space. In this paper, we propose learning
an optimized controller using a DNN that fuses multiple controllers. The
network learns a robust controller with online trajectory filtering, which
suppresses noisy trajectories and imperfections of individual controllers. The
result is a network that is able to learn a good fusion of filtered
trajectories from different controllers leading to significant improvements in
overall performance. We compare our trained network to controllers it has
learned from, end-to-end baselines and human pilots in a realistic simulation;
our network beats all baselines in extensive experiments and approaches the
performance of a professional human pilot. A video summarizing this work is
available at https://youtu.be/hGKlE5X9Z5U
Efficient Motion Planning for Automated Lane Change based on Imitation Learning and Mixed-Integer Optimization
Authors: Chenyang Xi, Tianyu Shi, Yuankai Wu, Lijun Sun
Date: 2019-04-18 00:00:00
Tasks: Action Generation, Autonomous Driving, Imitation Learning, Motion Planning

Intelligent motion planning is one of the core components in automated vehicles, which has received extensive interests. Traditional motion planning methods suffer from several drawbacks in terms of optimality, efficiency and generalization capability. Sampling based methods cannot guarantee the optimality of the generated trajectories. Whereas the optimization-based methods are not able to perform motion planning in real-time, and limited by the simplified formalization. In this work, we propose a learning-based approach to handle those shortcomings. Mixed Integer Quadratic Problem based optimization (MIQP) is used to generate the optimal lane-change trajectories which served as the training dataset for learning-based action generation algorithms. A hierarchical supervised learning model is devised to make the fast lane-change decision. Numerous experiments have been conducted to evaluate the optimality, efficiency, and generalization capability of the proposed approach. The experimental results indicate that the proposed model outperforms several commonly used motion planning baselines.
Evaluating the Underlying Gender Bias in Contextualized Word Embeddings
Authors: Christine Basta, Marta R. Costa-jussà, Noe Casas
Date: 2019-04-18 00:00:00
Proceeding: WS 2019 8
Tasks: Sentence, Word Embeddings

Gender bias is highly impacting natural language processing applications.
Word embeddings have clearly been proven both to keep and amplify gender biases
that are present in current data sources. Recently, contextualized word
embeddings have enhanced previous word embedding techniques by computing word
vector representations dependent on the sentence they appear in.
  In this paper, we study the impact of this conceptual change in the word
embedding computation in relation with gender bias. Our analysis includes
different measures previously applied in the literature to standard word
embeddings. Our findings suggest that contextualized word embeddings are less
biased than standard ones even when the latter are debiased.
DDNet: Cartesian-polar Dual-domain Network for the Joint Optic Disc and Cup Segmentation
Authors: Qing Liu, Xiaopeng Hong, Wei Ke, Zailiang Chen, Beiji Zou
Date: 2019-04-18 00:00:00
Tasks: Decoder, Feature Importance, Segmentation

Existing joint optic disc and cup segmentation approaches are developed
either in Cartesian or polar coordinate system. However, due to the subtle
optic cup, the contextual information exploited from the single domain even by
the prevailing CNNs is still insufficient. In this paper, we propose a novel
segmentation approach, named Cartesian-polar dual-domain network (DDNet), which
for the first time considers the complementary of the Cartesian domain and the
polar domain. We propose a two-branch of domain feature encoder and learn
translation equivariant representations on rectilinear grid from Cartesian
domain and rotation equivariant representations on polar grid from polar domain
parallelly. To fuse the features on two different grids, we propose a
dual-domain fusion module. This module builds the correspondence between two
grids by the differentiable polar transform layer and learns the feature
importance across two domains in element-wise to enhance the expressive
capability. Finally, the decoder aggregates the fused features from low-level
to high-level and makes dense predictions. We validate the state-of-the-art
segmentation performances of our DDNet on the public dataset ORIGA. According
to the segmentation masks, we estimate the commonly used clinical measure for
glaucoma, i.e., the vertical cup-to-disc ratio. The low cup-to-disc ratio
estimation error demonstrates the potential application in glaucoma screening.
Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation
Authors: Fabian Eitel, Emily Soehler, Judith Bellmann-Strobl, Alexander U. Brandt, Klemens Ruprecht, René M. Giess, Joseph Kuchling, Susanna Asseyer, Martin Weygandt, John-Dylan Haynes, Michael Scheel, Friedemann Paul, Kerstin Ritter
Date: 2019-04-18 00:00:00
Tasks: Decision Making, Diagnostic, General Classification, Holdout Set

Machine learning-based imaging diagnostics has recently reached or even
superseded the level of clinical experts in several clinical domains. However,
classification decisions of a trained machine learning system are typically
non-transparent, a major hindrance for clinical integration, error tracking or
knowledge discovery. In this study, we present a transparent deep learning
framework relying on convolutional neural networks (CNNs) and layer-wise
relevance propagation (LRP) for diagnosing multiple sclerosis (MS). MS is
commonly diagnosed utilizing a combination of clinical presentation and
conventional magnetic resonance imaging (MRI), specifically the occurrence and
presentation of white matter lesions in T2-weighted images. We hypothesized
that using LRP in a naive predictive model would enable us to uncover relevant
image features that a trained CNN uses for decision-making. Since imaging
markers in MS are well-established this would enable us to validate the
respective CNN model. First, we pre-trained a CNN on MRI data from the
Alzheimer's Disease Neuroimaging Initiative (n = 921), afterwards specializing
the CNN to discriminate between MS patients and healthy controls (n = 147).
Using LRP, we then produced a heatmap for each subject in the holdout set
depicting the voxel-wise relevance for a particular classification decision.
The resulting CNN model resulted in a balanced accuracy of 87.04% and an area
under the curve of 96.08% in a receiver operating characteristic curve. The
subsequent LRP visualization revealed that the CNN model focuses indeed on
individual lesions, but also incorporates additional information such as lesion
location, non-lesional white matter or gray matter areas such as the thalamus,
which are established conventional and advanced MRI markers in MS. We conclude
that LRP and the proposed framework have the capability to make diagnostic
decisions of...
4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks
Authors: Christopher Choy, JunYoung Gwak, Silvio Savarese
Date: 2019-04-18 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Semantic Segmentation, 4D Spatio Temporal Semantic Segmentation, Robust 3D Semantic Segmentation, Semantic Segmentation

In many robotics and VR/AR applications, 3D-videos are readily-available sources of input (a continuous sequence of depth images, or LIDAR scans). However, those 3D-videos are processed frame-by-frame either through 2D convnets or 3D perception algorithms. In this work, we propose 4-dimensional convolutional neural networks for spatio-temporal perception that can directly process such 3D-videos using high-dimensional convolutions. For this, we adopt sparse tensors and propose the generalized sparse convolution that encompasses all discrete convolutions. To implement the generalized sparse convolution, we create an open-source auto-differentiation library for sparse tensors that provides extensive functions for high-dimensional convolutional neural networks. We create 4D spatio-temporal convolutional neural networks using the library and validate them on various 3D semantic segmentation benchmarks and proposed 4D datasets for 3D-video perception. To overcome challenges in the 4D space, we propose the hybrid kernel, a special case of the generalized sparse convolution, and the trilateral-stationary conditional random field that enforces spatio-temporal consistency in the 7D space-time-chroma space. Experimentally, we show that convolutional neural networks with only generalized 3D sparse convolutions can outperform 2D or 2D-3D hybrid methods by a large margin. Also, we show that on 3D-videos, 4D spatio-temporal convolutional neural networks are robust to noise, outperform 3D convolutional neural networks and are faster than the 3D counterpart in some cases.
A Progressive Visual Analytics Tool for Incremental Experimental Evaluation
Authors: Fabio Giachelle, Gianmaria Silvello
Date: 2019-04-18 00:00:00
Tasks: Retrieval

This paper presents a visual tool, AVIATOR, that integrates the progressive
visual analytics paradigm in the IR evaluation process. This tool serves to
speed-up and facilitate the performance assessment of retrieval models enabling
a result analysis through visual facilities. AVIATOR goes one step beyond the
common "compute wait visualize" analytics paradigm, introducing a continuous
evaluation mechanism that minimizes human and computational resource
consumption.
edGNN: a Simple and Powerful GNN for Directed Labeled Graphs
Authors: Guillaume Jaume, An-phi Nguyen, María Rodríguez Martínez, Jean-Philippe Thiran, Maria Gabrani
Date: 2019-04-18 00:00:00
Tasks: Graph Classification, Graph Neural Network

The ability of a graph neural network (GNN) to leverage both the graph topology and graph labels is fundamental to building discriminative node and graph embeddings. Building on previous work, we theoretically show that edGNN, our model for directed labeled graphs, is as powerful as the Weisfeiler-Lehman algorithm for graph isomorphism. Our experiments support our theoretical findings, confirming that graph neural networks can be used effectively for inference problems on directed graphs with both node and edge labels. Code available at https://github.com/guillaumejaume/edGNN.