pwc-archive/papers-with-abstracts huggingface dataset:
Binarized Attributed Network Embedding
Authors: Hong Yang, Shirui Pan, Peng Zhang, Ling Chen, Defu Lian, Chengqi Zhang
Date: 2018-10-22 00:00:00
Proceeding: ICDM 2018 10
Tasks: Graph Embedding, Link Prediction, Network Embedding, Node Classification, Representation Learning

An implementation of "Binarized Attributed Network Embedding". Attributed network embedding enables joint representation learning of node links and attributes. Existing attributed network embedding models are designed in continuous Euclidean spaces which often introduce data redundancy and impose challenges to storage and computation costs. To this end, we present a Binarized Attributed Network Embedding model (BANE for short) to learn binary node representation. Specifically, we define a new Weisfeiler-Lehman proximity matrix to capture data dependence between node links and attributes by aggregating the information of node attributes and links from neighboring nodes to a given target node in a layer-wise manner. Based on the Weisfeiler-Lehman proximity matrix, we formulate a new Weisfiler-Lehman matrix factorization learning function under the binary node representation constraint. The learning problem is a mixed integer optimization and an efficient cyclic coordinate descent (CCD) algorithm is used as the solution. Node classification and link prediction experiments on real-world datasets show that the proposed BANE model outperforms the state-of-the-art network embedding methods.
Understanding Sparse JL for Feature Hashing
Authors: Meena Jagadeesan
Date: 2019-03-08 00:00:00
Proceeding: NeurIPS 2019 12

Feature hashing and other random projection schemes are commonly used to reduce the dimensionality of feature vectors. The goal is to efficiently project a high-dimensional feature vector living in $\mathbb{R}^n$ into a much lower-dimensional space $\mathbb{R}^m$, while approximately preserving Euclidean norm. These schemes can be constructed using sparse random projections, for example using a sparse Johnson-Lindenstrauss (JL) transform. A line of work introduced by Weinberger et. al (ICML '09) analyzes the accuracy of sparse JL with sparsity 1 on feature vectors with small $\ell_\infty$-to-$\ell_2$ norm ratio. Recently, Freksen, Kamma, and Larsen (NeurIPS '18) closed this line of work by proving a tight tradeoff between $\ell_\infty$-to-$\ell_2$ norm ratio and accuracy for sparse JL with sparsity $1$. In this paper, we demonstrate the benefits of using sparsity $s$ greater than $1$ in sparse JL on feature vectors. Our main result is a tight tradeoff between $\ell_\infty$-to-$\ell_2$ norm ratio and accuracy for a general sparsity $s$, that significantly generalizes the result of Freksen et. al. Our result theoretically demonstrates that sparse JL with $s > 1$ can have significantly better norm-preservation properties on feature vectors than sparse JL with $s = 1$; we also empirically demonstrate this finding.
Generating Difficult SAT Instances by Preventing Triangles
Authors: Guillaume Escamocher, Barry O'Sullivan, Steven David Prestwich
Date: 2019-03-08 00:00:00

When creating benchmarks for SAT solvers, we need SAT instances that are easy
to build but hard to solve. A recent development in the search for such methods
has led to the Balanced SAT algorithm, which can create k-SAT instances with m
clauses of high difficulty, for arbitrary k and m. In this paper we introduce
the No-Triangle SAT algorithm, a SAT instance generator based on the cluster
coefficient graph statistic. We empirically compare the two algorithms by
fixing the arity and the number of variables, but varying the number of
clauses. The hardest instances that we find are produced by No-Triangle SAT.
Furthermore, difficult instances from No-Triangle SAT have a different number
of clauses than difficult instances from Balanced SAT, potentially allowing a
combination of the two methods to find hard SAT instances for a larger array of
parameters.
Learning to Identify Object Instances by Touch: Tactile Recognition via Multimodal Matching
Authors: Justin Lin, Roberto Calandra, Sergey Levine
Date: 2019-03-08 00:00:00
Tasks: Object

Much of the literature on robotic perception focuses on the visual modality.
Vision provides a global observation of a scene, making it broadly useful.
However, in the domain of robotic manipulation, vision alone can sometimes
prove inadequate: in the presence of occlusions or poor lighting, visual object
identification might be difficult. The sense of touch can provide robots with
an alternative mechanism for recognizing objects. In this paper, we study the
problem of touch-based instance recognition. We propose a novel framing of the
problem as multi-modal recognition: the goal of our system is to recognize,
given a visual and tactile observation, whether or not these observations
correspond to the same object. To our knowledge, our work is the first to
address this type of multi-modal instance recognition problem on such a
large-scale with our analysis spanning 98 different objects. We employ a robot
equipped with two GelSight touch sensors, one on each finger, and a
self-supervised, autonomous data collection procedure to collect a dataset of
tactile observations and images. Our experimental results show that it is
possible to accurately recognize object instances by touch alone, including
instances of novel objects that were never seen during training. Our learned
model outperforms other methods on this complex task, including that of human
volunteers.
Rates of Convergence for Sparse Variational Gaussian Process Regression
Authors: David R. Burt, Carl E. Rasmussen, Mark van der Wilk
Date: 2019-03-08 00:00:00
Tasks: Continual Learning, regression

Excellent variational approximations to Gaussian process posteriors have been developed which avoid the $\mathcal{O}\left(N^3\right)$ scaling with dataset size $N$. They reduce the computational cost to $\mathcal{O}\left(NM^2\right)$, with $M\ll N$ being the number of inducing variables, which summarise the process. While the computational cost seems to be linear in $N$, the true complexity of the algorithm depends on how $M$ must increase to ensure a certain quality of approximation. We address this by characterising the behavior of an upper bound on the KL divergence to the posterior. We show that with high probability the KL divergence can be made arbitrarily small by growing $M$ more slowly than $N$. A particular case of interest is that for regression with normally distributed inputs in D-dimensions with the popular Squared Exponential kernel, $M=\mathcal{O}(\log^D N)$ is sufficient. Our results show that as datasets grow, Gaussian process posteriors can truly be approximated cheaply, and provide a concrete rule for how to increase $M$ in continual learning scenarios.
Geometry-Aware Graph Transforms for Light Field Compact Representation
Authors: Mira Rizkallah, Xin Su, Thomas Maugey, Christine Guillemot
Date: 2019-03-08 00:00:00

The paper addresses the problem of energy compaction of dense 4D light fields
by designing geometry-aware local graph-based transforms. Local graphs are
constructed on super-rays that can be seen as a grouping of spatially and
geometry-dependent angularly correlated pixels. Both non separable and
separable transforms are considered. Despite the local support of limited size
defined by the super-rays, the Laplacian matrix of the non separable graph
remains of high dimension and its diagonalization to compute the transform
eigen vectors remains computationally expensive. To solve this problem, we then
perform the local spatio-angular transform in a separable manner. We show that
when the shape of corresponding super-pixels in the different views is not
isometric, the basis functions of the spatial transforms are not coherent,
resulting in decreased correlation between spatial transform coefficients. We
hence propose a novel transform optimization method that aims at preserving
angular correlation even when the shapes of the super-pixels are not isometric.
Experimental results show the benefit of the approach in terms of energy
compaction. A coding scheme is also described to assess the rate-distortion
perfomances of the proposed transforms and is compared to state of the art
encoders namely HEVC and JPEG Pleno VM 1.1.
Prediction and Sampling with Local Graph Transforms for Quasi-Lossless Light Field Compression
Authors: Mira Rizkallah, Thomas Maugey, Christine Guillemot
Date: 2019-03-08 00:00:00

Graph-based transforms have been shown to be powerful tools in terms of image
energy compaction. However, when the support increases to best capture signal
dependencies, the computation of the basis functions becomes rapidly
untractable. This problem is in particular compelling for high dimensional
imaging data such as light fields. The use of local transforms with limited
supports is a way to cope with this computational difficulty. Unfortunately,
the locality of the support may not allow us to fully exploit long term signal
dependencies present in both the spatial and angular dimensions in the case of
light fields. This paper describes sampling and prediction schemes with local
graph-based transforms enabling to efficiently compact the signal energy and
exploit dependencies beyond the local graph support. The proposed approach is
investigated and is shown to be very efficient in the context of spatio-angular
transforms for quasi-lossless compression of light fields.
Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces
Authors: Adrian V. Dalca, Guha Balakrishnan, John Guttag, Mert R. Sabuncu
Date: 2019-03-08 00:00:00
Tasks: Constrained Diffeomorphic Image Registration, Deformable Medical Image Registration, Diffeomorphic Medical Image Registration, Image Registration, Medical Image Registration

Classical deformable registration techniques achieve impressive results and offer a rigorous theoretical treatment, but are computationally intensive since they solve an optimization problem for each image pair. Recently, learning-based methods have facilitated fast registration by learning spatial deformation functions. However, these approaches use restricted deformation models, require supervised labels, or do not guarantee a diffeomorphic (topology-preserving) registration. Furthermore, learning-based registration tools have not been derived from a probabilistic framework that can offer uncertainty estimates. In this paper, we build a connection between classical and learning-based methods. We present a probabilistic generative model and derive an unsupervised learning-based inference algorithm that uses insights from classical registration methods and makes use of recent developments in convolutional neural networks (CNNs). We demonstrate our method on a 3D brain registration task for both images and anatomical surfaces, and provide extensive empirical analyses. Our principled approach results in state of the art accuracy and very fast runtimes, while providing diffeomorphic guarantees. Our implementation is available at http://voxelmorph.csail.mit.edu.
Inductive Transfer for Neural Architecture Optimization
Authors: Martin Wistuba, Tejaswini Pedapati
Date: 2019-03-08 00:00:00
Tasks: GPU, image-classification, Image Classification, Neural Architecture Search

The recent advent of automated neural network architecture search led to
several methods that outperform state-of-the-art human-designed architectures.
However, these approaches are computationally expensive, in extreme cases
consuming GPU years. We propose two novel methods which aim to expedite this
optimization problem by transferring knowledge acquired from previous tasks to
new ones. First, we propose a novel neural architecture selection method which
employs this knowledge to identify strong and weak characteristics of neural
architectures across datasets. Thus, these characteristics do not need to be
rediscovered in every search, a strong weakness of current state-of-the-art
searches. Second, we propose a method for learning curve extrapolation to
determine if a training process can be terminated early. In contrast to
existing work, we propose to learn from learning curves of architectures
trained on other datasets to improve the prediction accuracy for novel
datasets. On five different image classification benchmarks, we empirically
demonstrate that both of our orthogonal contributions independently lead to an
acceleration, without any significant loss in accuracy.
Fast Prototyping a Dialogue Comprehension System for Nurse-Patient Conversations on Symptom Monitoring
Authors: Zhengyuan Liu, Hazel Lim, Nur Farah Ain Binte Suhaimi, Shao Chuen Tong, Sharon Ong, Angela Ng, Sheldon Lee, Michael R. Macdonald, Savitha Ramasamy, Pavitra Krishnaswamy, Wai Leng Chow, Nancy F. Chen
Date: 2019-03-08 00:00:00
Proceeding: NAACL 2019 6
Tasks: Retrieval

Data for human-human spoken dialogues for research and development are
currently very limited in quantity, variety, and sources; such data are even
scarcer in healthcare. In this work, we investigate fast prototyping of a
dialogue comprehension system by leveraging on minimal nurse-to-patient
conversations. We propose a framework inspired by nurse-initiated clinical
symptom monitoring conversations to construct a simulated human-human dialogue
dataset, embodying linguistic characteristics of spoken interactions like
thinking aloud, self-contradiction, and topic drift. We then adopt an
established bidirectional attention pointer network on this simulated dataset,
achieving more than 80% F1 score on a held-out test set from real-world
nurse-to-patient conversations. The ability to automatically comprehend
conversations in the healthcare domain by exploiting only limited data has
implications for improving clinical workflows through red flag symptom
detection and triaging capabilities. We demonstrate the feasibility for
efficient and effective extraction, retrieval and comprehension of symptom
checking information discussed in multi-turn human-human spoken conversations.
Application of Genetic Algorithms to the Multiple Team Formation Problem
Authors: Jose G. M. Esgario, Iago E. da Silva, Renato A. Krohling
Date: 2019-03-08 00:00:00
Tasks: Decision Making

Allocating of people in multiple projects is an important issue considering
the efficiency of groups from the point of view of social interaction. In this
paper, based on previous works, the Multiple Team Formation Problem (MTFP)
based on sociometric techniques is formulated as an optimization problem taking
into account the social interaction among team members. To solve the resulting
optimization problem we propose a Genetic Algorithm due to the NP-hard nature
of the problem. The social cohesion is an important issue that directly impacts
the productivity of the work environment. So, maintaining an appropriate level
of cohesion keeps a group together, which will bring positive impacts on the
results of a project. The aim of the proposal is to ensure the best possible
effectiveness from the point of view of social interaction. In this way, the
presented algorithm serves as a decision-making tool for managers to build
teams of people in multiple projects. In order to analyze the performance of
the proposed method, computational experiments with benchmarks were performed
and compared with the exhaustive method. The results are promising and show
that the algorithm generally obtains near-optimal results within a short
computational time.
DSM Building Shape Refinement from Combined Remote Sensing Images based on Wnet-cGANs
Authors: Ksenia Bittner, Marco Körner, Peter Reinartz
Date: 2019-03-08 00:00:00
Tasks: Generative Adversarial Network

We describe the workflow of a digital surface models (DSMs) refinement
algorithm using a hybrid conditional generative adversarial network (cGAN)
where the generative part consists of two parallel networks merged at the last
stage forming a WNet architecture. The inputs to the so-called WNet-cGAN are
stereo DSMs and panchromatic (PAN) half-meter resolution satellite images.
Fusing these helps to propagate fine detailed information from a spectral image
and complete the missing 3D knowledge from a stereo DSM about building shapes.
Besides, it refines the building outlines and edges making them more
rectangular and sharp.
OpenCL-based FPGA accelerator for disparity map generation with stereoscopic event cameras
Authors: David Castells-Rufas, Jordi Carrabina
Date: 2019-03-08 00:00:00
Tasks: Stereo Matching, Stereo Matching Hand

Although event-based cameras are already commercially available. Vision
algorithms based on them are still not common. As a consequence, there are few
Hardware Accelerators for them. In this work we present some experiments to
create FPGA accelerators for a well-known vision algorithm using event-based
cameras. We present a stereo matching algorithm to create a stream of disparity
events disparity map and implement several accelerators using the Intel FPGA
OpenCL tool-chain. The results show that multiple designs can be easily tested
and that a performance speedup of more than 8x can be achieved with simple code
transformations.
Unsupervised Data Imputation via Variational Inference of Deep Subspaces
Authors: Adrian V. Dalca, John Guttag, Mert R. Sabuncu
Date: 2019-03-08 00:00:00
Tasks: Imputation, Variational Inference

A wide range of systems exhibit high dimensional incomplete data. Accurate
estimation of the missing data is often desired, and is crucial for many
downstream analyses. Many state-of-the-art recovery methods involve supervised
learning using datasets containing full observations. In contrast, we focus on
unsupervised estimation of missing image data, where no full observations are
available - a common situation in practice. Unsupervised imputation methods for
images often employ a simple linear subspace to capture correlations between
data dimensions, omitting more complex relationships. In this work, we
introduce a general probabilistic model that describes sparse high dimensional
imaging data as being generated by a deep non-linear embedding. We derive a
learning algorithm using a variational approximation based on convolutional
neural networks and discuss its relationship to linear imputation models, the
variational auto encoder, and deep image priors. We introduce sparsity-aware
network building blocks that explicitly model observed and missing data. We
analyze proposed sparsity-aware network building blocks, evaluate our method on
public domain imaging datasets, and conclude by showing that our method enables
imputation in an important real-world problem involving medical images. The
code is freely available as part of the \verb|neuron| library at
http://github.com/adalca/neuron.
A Three-Player GAN: Generating Hard Samples To Improve Classification Networks
Authors: Simon Vandenhende, Bert de Brabandere, Davy Neven, Luc van Gool
Date: 2019-03-08 00:00:00
Tasks: Classification, General Classification, Generative Adversarial Network, Traffic Sign Recognition

We propose a Three-Player Generative Adversarial Network to improve
classification networks. In addition to the game played between the
discriminator and generator, a competition is introduced between the generator
and the classifier. The generator's objective is to synthesize samples that are
both realistic and hard to label for the classifier. Even though we make no
assumptions on the type of augmentations to learn, we find that the model is
able to synthesize realistically looking examples that are hard for the
classification model. Furthermore, the classifier becomes more robust when
trained on these difficult samples. The method is evaluated on a public dataset
for traffic sign recognition.
Improving Skin Condition Classification with a Visual Symptom Checker Trained using Reinforcement Learning
Authors: Mohamed Akrout, Amir-Massoud Farahmand, Tory Jarmain, Latif Abid
Date: 2019-03-08 00:00:00
Tasks: General Classification, Question Answering, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present a visual symptom checker that combines a pre-trained Convolutional Neural Network (CNN) with a Reinforcement Learning (RL) agent as a Question Answering (QA) model. This method increases the classification confidence and accuracy of the visual symptom checker, and decreases the average number of questions asked to narrow down the differential diagnosis. A Deep Q-Network (DQN)-based RL agent learns how to ask the patient about the presence of symptoms in order to maximize the probability of correctly identifying the underlying condition. The RL agent uses the visual information provided by CNN in addition to the answers to the asked questions to guide the QA system. We demonstrate that the RL-based approach increases the accuracy more than 20% compared to the CNN-only approach, which only uses the visual information to predict the condition. Moreover, the increased accuracy is up to 10% compared to the approach that uses the visual information provided by CNN along with a conventional decision tree-based QA system. We finally show that the RL-based approach not only outperforms the decision tree-based approach, but also narrows down the diagnosis faster in terms of the average number of asked questions.
Research on the pixel-based and object-oriented methods of urban feature extraction with GF-2 remote-sensing images
Authors: Dong-dong Zhang, Lei Zhang, Vladimir Zaborovsky, Feng Xie, Yan-wen Wu, Ting-ting Lu
Date: 2019-03-08 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Management, Object, Vector Graphics

During the rapid urbanization construction of China, acquisition of urban geographic information and timely data updating are important and fundamental tasks for the refined management of cities. With the development of domestic remote sensing technology, the application of Gaofen-2 (GF-2) high-resolution remote sensing images can greatly improve the accuracy of information extraction. This paper introduces an approach using object-oriented classification methods for urban feature extraction based on GF-2 satellite data. A combination of spectral, spatial attributes and membership functions was employed for mapping the urban features of Qinhuai District, Nanjing. The data preprocessing is carried out by ENVI software, and the subsequent data is exported into the eCognition software for object-oriented classification and extraction of urban feature information. Finally, the obtained raster image classification results are vectorized using the ARCGIS software, and the vector graphics are stored in the library, which can be used for further analysis and modeling. Accuracy assessment was performed using ground truth data acquired by visual interpretation and from other reliable secondary data sources. Compared with the result of pixel-based supervised (neural net) classification, the developed object-oriented method can significantly improve extraction accuracy, and after manual interpretation, an overall accuracy of 95.44% can be achieved, with a Kappa coefficient of 0.9405, which objectively confirmed the superiority of the object-oriented method and the feasibility of the utilization of GF-2 satellite data.