pwc-archive/papers-with-abstracts huggingface dataset:
An LGMD Based Competitive Collision Avoidance Strategy for UAV
Authors: Jiannan Zhao, Xingzao Ma, Qinbing Fu, Cheng Hu, Shigang Yue
Date: 2019-04-15 00:00:00
Tasks: Collision Avoidance

Building a reliable and efficient collision avoidance system for unmanned
aerial vehicles (UAVs) is still a challenging problem. This research takes
inspiration from locusts, which can fly in dense swarms for hundreds of miles
without collision. In the locust's brain, a visual pathway of LGMD-DCMD (lobula
giant movement detector and descending contra-lateral motion detector) has been
identified as collision perception system guiding fast collision avoidance for
locusts, which is ideal for designing artificial vision systems. However, there
is very few works investigating its potential in real-world UAV applications.
In this paper, we present an LGMD based competitive collision avoidance method
for UAV indoor navigation. Compared to previous works, we divided the UAV's
field of view into four subfields each handled by an LGMD neuron. Therefore,
four individual competitive LGMDs (C-LGMD) compete for guiding the directional
collision avoidance of UAV. With more degrees of freedom compared to ground
robots and vehicles, the UAV can escape from collision along four cardinal
directions (e.g. the object approaching from the left-side triggers a rightward
shifting of the UAV). Our proposed method has been validated by both
simulations and real-time quadcopter arena experiments.
Tutorial: Safe and Reliable Machine Learning
Authors: Suchi Saria, Adarsh Subbaswamy
Date: 2019-04-15 00:00:00
Tasks: BIG-bench Machine Learning, Fairness

This document serves as a brief overview of the "Safe and Reliable Machine
Learning" tutorial given at the 2019 ACM Conference on Fairness,
Accountability, and Transparency (FAT* 2019). The talk slides can be found
here: https://bit.ly/2Gfsukp, while a video of the talk is available here:
https://youtu.be/FGLOCkC4KmE, and a complete list of references for the
tutorial here: https://bit.ly/2GdLPme.
A Discussion on Solving Partial Differential Equations using Neural Networks
Authors: Tim Dockhorn
Date: 2019-04-15 00:00:00
Tasks: Ensemble Learning

Can neural networks learn to solve partial differential equations (PDEs)? We
investigate this question for two (systems of) PDEs, namely, the Poisson
equation and the steady Navier--Stokes equations. The contributions of this
paper are five-fold. (1) Numerical experiments show that small neural networks
(< 500 learnable parameters) are able to accurately learn complex solutions for
systems of partial differential equations. (2) It investigates the influence of
random weight initialization on the quality of the neural network approximate
solution and demonstrates how one can take advantage of this non-determinism
using ensemble learning. (3) It investigates the suitability of the loss
function used in this work. (4) It studies the benefits and drawbacks of
solving (systems of) PDEs with neural networks compared to classical numerical
methods. (5) It proposes an exhaustive list of possible directions of future
work.
Exact Rate-Distortion in Autoencoders via Echo Noise
Authors: Rob Brekelmans, Daniel Moyer, Aram Galstyan, Greg Ver Steeg
Date: 2019-04-15 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Representation Learning

Compression is at the heart of effective representation learning. However, lossy compression is typically achieved through simple parametric models like Gaussian noise to preserve analytic tractability, and the limitations this imposes on learning are largely unexplored. Further, the Gaussian prior assumptions in models such as variational autoencoders (VAEs) provide only an upper bound on the compression rate in general. We introduce a new noise channel, \emph{Echo noise}, that admits a simple, exact expression for mutual information for arbitrary input distributions. The noise is constructed in a data-driven fashion that does not require restrictive distributional assumptions. With its complex encoding mechanism and exact rate regularization, Echo leads to improved bounds on log-likelihood and dominates $\beta$-VAEs across the achievable range of rate-distortion trade-offs. Further, we show that Echo noise can outperform flow-based methods without the need to train additional distributional transformations.
Comparison of statistical post-processing methods for probabilistic NWP forecasts of solar radiation
Authors: Kilian Bakker, Kirien Whan, Wouter Knap, Maurice Schmeits
Date: 2019-04-15 00:00:00
Tasks: quantile regression, regression

The increased usage of solar energy places additional importance on forecasts of solar radiation. Solar panel power production is primarily driven by the amount of solar radiation and it is therefore important to have accurate forecasts of solar radiation. Accurate forecasts that also give information on the forecast uncertainties can help users of solar energy to make better solar radiation based decisions related to the stability of the electrical grid. To achieve this, we apply statistical post-processing techniques that determine relationships between observations of global radiation (made within the KNMI network of automatic weather stations in the Netherlands) and forecasts of various meteorological variables from the numerical weather prediction (NWP) model HARMONIE-AROME (HA) and the atmospheric composition model CAMS. Those relationships are used to produce probabilistic forecasts of global radiation. We compare 7 different statistical post-processing methods, consisting of two parametric and five non-parametric methods. We find that all methods are able to generate probabilistic forecasts that improve the raw global radiation forecast from HA according to the root mean squared error (on the median) and the potential economic value. Additionally, we show how important the predictors are in the different regression methods. We also compare the regression methods using various probabilistic scoring metrics, namely the continuous ranked probability skill score, the Brier skill score and reliability diagrams. We find that quantile regression and generalized random forests generally perform best. In (near) clear sky conditions the non-parametric methods have more skill than the parametric ones.
Explicit Spatial Encoding for Deep Local Descriptors
Authors: Arun Mukundan, Giorgos Tolias, Ondrej Chum
Date: 2019-04-15 00:00:00
Proceeding: CVPR 2019 6

We propose a kernelized deep local-patch descriptor based on efficient match
kernels of neural network activations. Response of each receptive field is
encoded together with its spatial location using explicit feature maps. Two
location parametrizations, Cartesian and polar, are used to provide robustness
to a different types of canonical patch misalignment. Additionally, we analyze
how the conventional architecture, i.e. a fully connected layer attached after
the convolutional part, encodes responses in a spatially variant way. In
contrary, explicit spatial encoding is used in our descriptor, whose potential
applications are not limited to local-patches. We evaluate the descriptor on
standard benchmarks. Both versions, encoding 32x32 or 64x64 patches,
consistently outperform all other methods on all benchmarks. The number of
parameters of the model is independent of the input patch resolution.
Reinforcement Learning with Probabilistic Guarantees for Autonomous Driving
Authors: Maxime Bouton, Jesper Karlsson, Alireza Nakhaei, Kikuo Fujimura, Mykel J. Kochenderfer, Jana Tumova
Date: 2019-04-15 00:00:00
Tasks: Autonomous Driving, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Designing reliable decision strategies for autonomous urban driving is challenging. Reinforcement learning (RL) has been used to automatically derive suitable behavior in uncertain environments, but it does not provide any guarantee on the performance of the resulting policy. We propose a generic approach to enforce probabilistic guarantees on an RL agent. An exploration strategy is derived prior to training that constrains the agent to choose among actions that satisfy a desired probabilistic specification expressed with linear temporal logic (LTL). Reducing the search space to policies satisfying the LTL formula helps training and simplifies reward design. This paper outlines a case study of an intersection scenario involving multiple traffic participants. The resulting policy outperforms a rule-based heuristic approach in terms of efficiency while exhibiting strong guarantees on safety.
Synthetic Neural Vision System Design for Motion Pattern Recognition in Dynamic Robot Scenes
Authors: Qinbing Fu, Cheng Hu, Pengcheng Liu, Shigang Yue
Date: 2019-04-15 00:00:00
Tasks: Collision Avoidance, Decision Making, Open-Ended Question Answering

Insects have tiny brains but complicated visual systems for motion
perception. A handful of insect visual neurons have been computationally
modeled and successfully applied for robotics. How different neurons
collaborate on motion perception, is an open question to date. In this paper,
we propose a novel embedded vision system in autonomous micro-robots, to
recognize motion patterns in dynamic robot scenes. Here, the basic motion
patterns are categorized into movements of looming (proximity), recession,
translation, and other irrelevant ones. The presented system is a synthetic
neural network, which comprises two complementary sub-systems with four spiking
neurons -- the lobula giant movement detectors (LGMD1 and LGMD2) in locusts for
sensing looming and recession, and the direction selective neurons (DSN-R and
DSN-L) in flies for translational motion extraction. Images are transformed to
spikes via spatiotemporal computations towards a switch function and decision
making mechanisms, in order to invoke proper robot behaviors amongst collision
avoidance, tracking and wandering, in dynamic robot scenes. Our robot
experiments demonstrated two main contributions: (1) This neural vision system
is effective to recognize the basic motion patterns corresponding to timely and
proper robot behaviors in dynamic scenes. (2) The arena tests with multi-robots
demonstrated the effectiveness in recognizing more abundant motion features for
collision detection, which is a great improvement compared with former studies.
The Landscape of the Planted Clique Problem: Dense subgraphs and the Overlap Gap Property
Authors: David Gamarnik, Ilias Zadik
Date: 2019-04-15 00:00:00
Tasks: Learning Theory

In this paper we study the computational-statistical gap of the planted clique problem, where a clique of size $k$ is planted in an Erdos Renyi graph $G(n,\frac{1}{2})$ resulting in a graph $G\left(n,\frac{1}{2},k\right)$. The goal is to recover the planted clique vertices by observing $G\left(n,\frac{1}{2},k\right)$ . It is known that the clique can be recovered as long as $k \geq \left(2+\epsilon\right)\log n $ for any $\epsilon>0$, but no polynomial-time algorithm is known for this task unless $k=\Omega\left(\sqrt{n} \right)$. Following a statistical-physics inspired point of view as an attempt to understand this computational-statistical gap, we study the landscape of the "sufficiently dense" subgraphs of $G$ and their overlap with the planted clique. Using the first moment method, we study the densest subgraph problems for subgraphs with fixed, but arbitrary, overlap size with the planted clique, and provide evidence of a phase transition for the presence of Overlap Gap Property (OGP) at $k=\Theta\left(\sqrt{n}\right)$. OGP is a concept introduced originally in spin glass theory and known to suggest algorithmic hardness when it appears. We establish the presence of OGP when $k$ is a small positive power of $n$ by using a conditional second moment method. As our main technical tool, we establish the first, to the best of our knowledge, concentration results for the $K$-densest subgraph problem for the Erdos-Renyi model $G\left(n,\frac{1}{2}\right)$ when $K=n^{0.5-\epsilon}$ for arbitrary $\epsilon>0$. Finally, to study the OGP we employ a certain form of overparametrization, which is conceptually aligned with a large body of recent work in learning theory and optimization.
Deep Iterative Surface Normal Estimation
Authors: Jan Eric Lenssen, Christian Osendorfer, Jonathan Masci
Date: 2019-04-15 00:00:00
Proceeding: CVPR 2020 6
Tasks: Deep Learning, Surface Normal Estimation, Surface Normals Estimation

This paper presents an end-to-end differentiable algorithm for robust and detail-preserving surface normal estimation on unstructured point-clouds. We utilize graph neural networks to iteratively parameterize an adaptive anisotropic kernel that produces point weights for weighted least-squares plane fitting in local neighborhoods. The approach retains the interpretability and efficiency of traditional sequential plane fitting while benefiting from adaptation to data set statistics through deep learning. This results in a state-of-the-art surface normal estimator that is robust to noise, outliers and point density variation, preserves sharp features through anisotropic kernels and equivariance through a local quaternion-based spatial transformer. Contrary to previous deep learning methods, the proposed approach does not require any hand-crafted features or preprocessing. It improves on the state-of-the-art results while being more than two orders of magnitude faster and more parameter efficient.
Graph-Based Method for Anomaly Prediction in Brain Network
Authors: Jalal Mirakhorli, Hamidreza Amindavar, Mojgan Mirakhorli
Date: 2019-04-15 00:00:00
Tasks: Anomaly Detection, Functional Connectivity, Prediction

Resting-state functional MRI (rs-fMRI) in functional neuroimaging techniques have improved in brain disorders, dysfunction studies via mapping the topology of the brain connections, i.e. connectopic mapping. Since, there are the slight differences between healthy and unhealthy brain regions and functions, investigation into the complex topology of functional and structural brain networks in human is a complicated task with the growth of evaluation criteria. Irregular graph deep learning applications have widely spread to understanding human cognitive functions that are linked to gene expression and related distributed spatial patterns, because the neuronal networks of the brain can hold dynamically a variety of brain solutions with different activity patterns and functional connectivity, these applications might also be involved with both node-centric and graph-centric tasks. In this paper, we performed a novel approach of individual generative model and high order graph analysis for the region of interest recognition areas of the brain which do not have a normal connection during applying certain tasks. Here, we proposed a high order framework of Graph Auto-Encoder (GAE) with a hypersphere distributer for functional data analysis in brain imaging studies that is underlying non-Euclidean structure in the learning of strong non-rigid graphs among large scale data. In addition, we distinguished the possible modes of correlations in abnormal brain connections. Our finding will show the degree of correlation between the affected regions and their simultaneous occurrence over time that can be used to diagnose brain diseases or revealing the ability of the nervous system to modify in brain topology at all angles, brain plasticity, according to input stimuli.
Are Nearby Neighbors Relatives?: Testing Deep Music Embeddings
Authors: Jaehun Kim, Julián Urbano, Cynthia C. S. Liem, Alan Hanjalic
Date: 2019-04-15 00:00:00

Deep neural networks have frequently been used to directly learn representations useful for a given task from raw input data. In terms of overall performance metrics, machine learning solutions employing deep representations frequently have been reported to greatly outperform those using hand-crafted feature representations. At the same time, they may pick up on aspects that are predominant in the data, yet not actually meaningful or interpretable. In this paper, we therefore propose a systematic way to test the trustworthiness of deep music representations, considering musical semantics. The underlying assumption is that in case a deep representation is to be trusted, distance consistency between known related points should be maintained both in the input audio space and corresponding latent deep space. We generate known related points through semantically meaningful transformations, both considering imperceptible and graver transformations. Then, we examine within- and between-space distance consistencies, both considering audio space and latent embedded space, the latter either being a result of a conventional feature extractor or a deep encoder. We illustrate how our method, as a complement to task-specific performance, provides interpretable insight into what a network may have captured from training data signals.
Copula-like Variational Inference
Authors: Marcel Hirt, Petros Dellaportas, Alain Durmus
Date: 2019-04-15 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Variational Inference

This paper considers a new family of variational distributions motivated by Sklar's theorem. This family is based on new copula-like densities on the hypercube with non-uniform marginals which can be sampled efficiently, i.e. with a complexity linear in the dimension of state space. Then, the proposed variational densities that we suggest can be seen as arising from these copula-like densities used as base distributions on the hypercube with Gaussian quantile functions and sparse rotation matrices as normalizing flows. The latter correspond to a rotation of the marginals with complexity $\mathcal{O}(d \log d)$. We provide some empirical evidence that such a variational family can also approximate non-Gaussian posteriors and can be beneficial compared to Gaussian approximations. Our method performs largely comparably to state-of-the-art variational approximations on standard regression and classification benchmarks for Bayesian Neural Networks.
Improving Human Text Comprehension through Semi-Markov CRF-based Neural Section Title Generation
Authors: Sebastian Gehrmann, Steven Layne, Franck Dernoncourt
Date: 2019-04-15 00:00:00
Proceeding: NAACL 2019 6
Tasks: Decoder, Reading Comprehension, Sentence

Titles of short sections within long documents support readers by guiding
their focus towards relevant passages and by providing anchor-points that help
to understand the progression of the document. The positive effects of section
titles are even more pronounced when measured on readers with less developed
reading abilities, for example in communities with limited labeled text
resources.
  We, therefore, aim to develop techniques to generate section titles in
low-resource environments. In particular, we present an extractive pipeline for
section title generation by first selecting the most salient sentence and then
applying deletion-based compression. Our compression approach is based on a
Semi-Markov Conditional Random Field that leverages unsupervised
word-representations such as ELMo or BERT, eliminating the need for a complex
encoder-decoder architecture. The results show that this approach leads to
competitive performance with sequence-to-sequence models with high resources,
while strongly outperforming it with low resources. In a human-subject study
across subjects with varying reading abilities, we find that our section titles
improve the speed of completing comprehension tasks while retaining similar
accuracy.
Processsing Simple Geometric Attributes with Autoencoders
Authors: Alasdair Newson, Andrés Almansa, Yann Gousseau, Saïd Ladjal
Date: 2019-04-15 00:00:00
Tasks: Image Generation, Position

Image synthesis is a core problem in modern deep learning, and many recent
architectures such as autoencoders and Generative Adversarial networks produce
spectacular results on highly complex data, such as images of faces or
landscapes. While these results open up a wide range of new, advanced synthesis
applications, there is also a severe lack of theoretical understanding of how
these networks work. This results in a wide range of practical problems, such
as difficulties in training, the tendency to sample images with little or no
variability, and generalisation problems. In this paper, we propose to analyse
the ability of the simplest generative network, the autoencoder, to encode and
decode two simple geometric attributes : size and position. We believe that, in
order to understand more complicated tasks, it is necessary to first understand
how these networks process simple attributes. For the first property, we
analyse the case of images of centred disks with variable radii. We explain how
the autoencoder projects these images to and from a latent space of smallest
possible dimension, a scalar. In particular, we describe a closed-form solution
to the decoding training problem in a network without biases, and show that
during training, the network indeed finds this solution. We then investigate
the best regularisation approaches which yield networks that generalise well.
For the second property, position, we look at the encoding and decoding of
Dirac delta functions, also known as `one-hot' vectors. We describe a
hand-crafted filter that achieves encoding perfectly, and show that the network
naturally finds this filter during training. We also show experimentally that
the decoding can be achieved if the dataset is sampled in an appropriate
manner.
SIMCO: SIMilarity-based object COunting
Authors: Marco Godi, Christian Joppi, Andrea Giachetti, Marco Cristani
Date: 2019-04-15 00:00:00
Tasks: Clustering, Object, Object Counting, Triplet

We present SIMCO, the first agnostic multi-class object counting approach. SIMCO starts by detecting foreground objects through a novel Mask RCNN-based architecture trained beforehand (just once) on a brand-new synthetic 2D shape dataset, InShape; the idea is to highlight every object resembling a primitive 2D shape (circle, square, rectangle, etc.). Each object detected is described by a low-dimensional embedding, obtained from a novel similarity-based head branch; this latter implements a triplet loss, encouraging similar objects (same 2D shape + color and scale) to map close. Subsequently, SIMCO uses this embedding for clustering, so that different types of objects can emerge and be counted, making SIMCO the very first multi-class unsupervised counter. Experiments show that SIMCO provides state-of-the-art scores on counting benchmarks and that it can also help in many challenging image understanding tasks.
Recurrent Neural Network for (Un-)supervised Learning of Monocular VideoVisual Odometry and Depth
Authors: Rui Wang, Stephen M. Pizer, Jan-Michael Frahm
Date: 2019-04-15 00:00:00
Tasks: Depth Estimation, MULTI-VIEW LEARNING, Visual Odometry

Deep learning-based, single-view depth estimation methods have recently shown
highly promising results. However, such methods ignore one of the most
important features for determining depth in the human vision system, which is
motion. We propose a learning-based, multi-view dense depth map and odometry
estimation method that uses Recurrent Neural Networks (RNN) and trains
utilizing multi-view image reprojection and forward-backward flow-consistency
losses. Our model can be trained in a supervised or even unsupervised mode. It
is designed for depth and visual odometry estimation from video where the input
frames are temporally correlated. However, it also generalizes to single-view
depth estimation. Our method produces superior results to the state-of-the-art
approaches for single-view and multi-view learning-based depth estimation on
the KITTI driving dataset.