pwc-archive/papers-with-abstracts huggingface dataset:
RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN
Authors: Krzysztof Rusek, José Suárez-Varela, Paul Almasan, Pere Barlet-Ros, Albert Cabellos-Aparicio
Date: 2019-10-03 00:00:00
Tasks: Graph Neural Network

Network modeling is a key enabler to achieve efficient network operation in future self-driving Software-Defined Networks. However, we still lack functional network models able to produce accurate predictions of Key Performance Indicators (KPI) such as delay, jitter or loss at limited cost. In this paper we propose RouteNet, a novel network model based on Graph Neural Network (GNN) that is able to understand the complex relationship between topology, routing, and input traffic to produce accurate estimates of the per-source/destination per-packet delay distribution and loss. RouteNet leverages the ability of GNNs to learn and model graph-structured information and as a result, our model is able to generalize over arbitrary topologies, routing schemes and traffic intensity. In our evaluation, we show that RouteNet is able to predict accurately the delay distribution (mean delay and jitter) and loss even in topologies, routing and traffic unseen in the training (worst case MRE=15.4%). Also, we present several use cases where we leverage the KPI predictions of our GNN model to achieve efficient routing optimization and network planning.
A deep surrogate approach to efficient Bayesian inversion in PDE and integral equation models
Authors: Teo Deveney, Eike Mueller, Tony Shardlow
Date: 2019-10-03 00:00:00
Tasks: Bayesian Inference, Deep Learning

We investigate a deep learning approach to efficiently perform Bayesian inference in partial differential equation (PDE) and integral equation models over potentially high-dimensional parameter spaces. The contributions of this paper are two-fold; the first is the introduction of a neural network approach to approximating the solutions of Fredholm and Volterra integral equations of the first and second kind. The second is the development of a new, efficient deep learning-based method for Bayesian inversion applied to problems that can be described by PDEs or integral equations. To achieve this we introduce a surrogate model, and demonstrate how this allows efficient sampling from a Bayesian posterior distribution in which the likelihood depends on the solutions of PDEs or integral equations. Our method relies on the direct approximation of parametric solutions by neural networks, without need of traditional numerical solves. This deep learning approach allows the accurate and efficient approximation of parametric solutions in significantly higher dimensions than is possible using classical discretisation schemes. Since the approximated solutions can be cheaply evaluated, the solutions of Bayesian inverse problems over large parameter spaces are efficient using Markov chain Monte Carlo. We demonstrate the performance of our method using two real-world examples; these include Bayesian inference in the PDE and integral equation case for an example from electrochemistry, and Bayesian inference of a function-valued heat-transfer parameter with applications in aviation.
Efficient training of energy-based models via spin-glass control
Authors: Alejandro Pozas-Kerstjens, Gorka Muñoz-Gil, Eloy Piñol, Miguel Ángel García-March, Antonio Acín, Maciej Lewenstein, Przemysław R. Grzybowski
Date: 2019-10-03 00:00:00

We introduce a new family of energy-based probabilistic graphical models for efficient unsupervised learning. Its definition is motivated by the control of the spin-glass properties of the Ising model described by the weights of Boltzmann machines. We use it to learn the Bars and Stripes dataset of various sizes and the MNIST dataset, and show how they quickly achieve the performance offered by standard methods for unsupervised learning. Our results indicate that the standard initialization of Boltzmann machines with random weights equivalent to spin-glass models is an unnecessary bottleneck in the process of training. Furthermore, this new family allows for very easy access to low-energy configurations, which points to new, efficient training algorithms. The simplest variant of such algorithms approximates the negative phase of the log-likelihood gradient with no Markov chain Monte Carlo sampling costs at all, and with an accuracy sufficient to achieve good learning and generalization.
SensorDrop: A Reinforcement Learning Framework for Communication Overhead Reduction on the Edge
Authors: Pooya Khandel, Amir Hossein Rassafi, Vahid Pourahmadi, Saeed Sharifian, Rong Zheng
Date: 2019-10-03 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In IoT solutions, it is usually desirable to collect data from a large number of distributed IoT sensors at a central node in the cloud for further processing. One of the main design challenges of such solutions is the high communication overhead between the sensors and the central node (especially for multimedia data). In this paper, we aim to reduce the communication overhead and propose a method that is able to determine which sensors should send their data to the central node and which to drop data. The idea is that some sensors may have data which are correlated with others and some may have data that are not essential for the operation to be performed at the central node. As such decisions are application dependent and may change over time, they should be learned during the operation of the system, for that we propose a method based on Advantage Actor-Critic (A2C) reinforcement learning which gradually learns which sensor's data is cost-effective to be sent to the central node. The proposed approach has been evaluated on a multi-view multi-camera dataset, and we observe a significant reduction in communication overhead with marginal degradation in object classification accuracy.
Fully Convolutional Geometric Features
Authors: Christopher Choy, Jaesik Park, Vladlen Koltun
Date: 2019-10-27 00:00:00
Proceeding: International Conference on Computer vision 2019 10
Tasks: 3D Feature Matching, 3D Point Cloud Matching, 3D Shape Representation, Metric Learning, Point Cloud Registration

Extracting geometric features from 3D scans or point clouds is the first step in applications such as registration, reconstruction, and tracking. State-of-the-art methods require computing low-level features as input or extracting patch-based features with limited receptive field. In this work, we present fully-convolutional geometric features, computed in a single pass by a 3D fully-convolutional network. We also present new metric learning losses that dramatically improve performance. Fully-convolutional geometric features are compact, capture broad spatial context, and scale to large scenes. We experimentally validate our approach on both indoor and outdoor datasets. Fully-convolutional geometric features achieve state-of-the-art accuracy without requiring prepossessing, are compact (32 dimensions), and are 600 times faster than the most accurate prior method.
Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010
Authors: Berry de Bruijn, Colin Cherry, Svetlana Kiritchenko, Joel Martin, Xiaodan Zhu
Date: 2011-05-12 00:00:00
Proceeding: JAMIA 2011 5
Tasks: BIG-bench Machine Learning, Clinical Concept Extraction, Relationship Detection

Objective: As clinical text mining continues to mature, its
potential as an enabling technology for innovations in
patient care and clinical research is becoming a reality. A
critical part of that process is rigid benchmark testing of
natural language processing methods on realistic clinical
narrative. In this paper, the authors describe the design
and performance of three state-of-the-art text-mining
applications from the National Research Council of
Canada on evaluations within the 2010 i2b2 challenge.
Design: The three systems perform three key steps in
clinical information extraction: (1) extraction of medical
problems, tests, and treatments, from discharge
summaries and progress notes; (2) classification of
assertions made on the medical problems; (3)
classification of relations between medical concepts.
Machine learning systems performed these tasks using
large-dimensional bags of features, as derived from both
the text itself and from external sources: UMLS, cTAKES,
and Medline.
Measurements: Performance was measured per
subtask, using micro-averaged F-scores, as calculated by
comparing system annotations with ground-truth
annotations on a test set.
Results: The systems ranked high among all submitted
systems in the competition, with the following F-scores:
concept extraction 0.8523 (ranked first); assertion
detection 0.9362 (ranked first); relationship detection
0.7313 (ranked second).
Conclusion: For all tasks, we found that the introduction
of a wide range of features was crucial to success.
Importantly, our choice of machine learning algorithms
allowed us to be versatile in our feature design, and to
introduce a large number of features without overfitting
and without encountering computing-resource
bottlenecks.
Towards Experienced Anomaly Detector through Reinforcement Learning
Authors: Chengqiang Huang, Yulei Wu, Yuan Zuo, Ke Pei, Geyong Min
Date: 2018-04-29 00:00:00
Proceeding: Thirty-Second AAAI Conference on Artificial Intelligence 2018 4
Tasks: Anomaly Detection, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Self-Learning, Time Series, Time Series Analysis, Time Series Anomaly Detection

This abstract proposes a time series anomaly detector which 1) makes no assumption about the underlying mechanism of anomaly patterns, 2) refrains from the cumbersome work of threshold setting for good anomaly detection performance under specific scenarios, and 3) keeps evolving with the growth of anomaly detection experience. Essentially, the anomaly detector is powered by the Recurrent Neural Network (RNN) and adopts the Reinforcement Learning (RL) method to achieve the self-learning process. Our initial experiments demonstrate promising results of using the detector in network time series anomaly detection problems.
A Practical Deep Online Ranking System in E-commerce Recommendation
Authors: Yan Yan1, Zitao Liu2, Meng Zhao1, Wentao Guo1, Weipeng P. Yan1, and Yongjun Bao1
Date: 2018-09-01 00:00:00
Proceeding: journal 2018 9
Tasks: Recommendation Systems, Re-Ranking, Retrieval

User online shopping experience in modern e-commerce websites critically relies on real-time personalized recommendations. However, building a productionized recommender system still remains challenging due to a massive collection of items, a huge number of online
users, and requirements for recommendations to be responsive to user
actions. In this work, we present our relevant, responsive, and scalable
deep online ranking system (DORS) that we developed and deployed in
our company. DORS is implemented in a three-level architecture which
includes (1) candidate retrieval that retrieves a board set of candidates
with various business rules enforced; (2) deep neural network ranking
model that takes advantage of available user and item specific features
and their interactions; (3) multi-arm bandits based online re-ranking
that dynamically takes user real-time feedback and re-ranks the final
recommended items in scale. Given a user as a query, DORS is able to
precisely capture users’ real-time purchasing intents and help users reach
to product purchases. Both offline and online experimental results show
that DORS provides more personalized online ranking results and makes
more revenue.
Expanding functional protein sequence space using generative adversarial networks
Authors: Donatas Repec​ka​, Vykintas Jauniskis​, Laurynas Karpus​, Elzbieta Rembeza​, Jan Zrimec​, SimonaPoviloniene​, Irmantas Rokaitis​, Audrius Laurynenas​, Wissam Abuajwa​, Otto Savolainen​, RolandasMeskys​, Martin K. M. Engqvist​, Aleksej Zelezniak
Date: 2019-10-02 00:00:00
Proceeding: n/a 2019 10
Tasks: Diversity, Generative Adversarial Network, Protein Design

De novo protein design for catalysis of any desired chemical reaction is a long standing goal in proteinengineering, due to the broad spectrum of technological, scientific and medical applications. Currently,mapping protein sequence to protein function is, however, neither computationionally nor experimentally tangible​1,2​. Here we developed ProteinGAN, a specialised variant of the generative adversarial network​3that is able to 'learn' natural protein sequence diversity and enables the generation of functional proteinsequences. ProteinGAN learns the evolutionary relationships of protein sequences directly from thecomplex multidimensional amino acid sequence space and creates new, highly diverse sequence variantswith natural-like physical properties. Using malate dehydrogenase as a template enzyme, we show that24% of the ProteinGAN-generated and experimentally tested sequences are soluble and display wild-typelevel catalytic activity in the tested conditions​in vitro​, even in highly mutated (>100 mutations) sequences.ProteinGAN therefore demonstrates the potential of artificial intelligence to rapidly generate highly diversenovel functional proteins within the allowed biological constraints of the sequence space.
Software Framework for Topic Modelling with Large Corpora
Authors: Radim Řehůřek, Petr Sojka
Date: 2010-01-01 00:00:00
Proceeding: Workshop On New Challenges For NLP Frameworks 2010 1
Tasks: Topic Models

Large corpora are ubiquitous in today’s world and memory quickly becomes the limiting factor in practical applications of the Vector Space Model (VSM). In this paper, we identify a gap in existing implementations of many of the popular algorithms, which is their scalability and ease of use. We describe a Natural Language Processing software framework which is based on the idea of document streaming, i.e. processing corpora document after document, in a memory independent fashion. Within this framework, we implement several popular algorithms for topical inference, including Latent Semantic Analysis and Latent Dirichlet Allocation, in a way that makes them completely independent of the training corpus size. Particular emphasis is placed on straightforward and intuitive framework design, so that modifications and extensions of the methods and/or their application by interested practitioners are effortless. We demonstrate the usefulness of our approach on a real-world scenario of computing document similarities within an existing digital library DML-CZ. 1.
Fitting stochastic epidemic models to gene genealogies using linear noise approximation
Authors: Mingwei Tang, Gytis Dudas, Trevor Bedford, Vladimir N. Minin
Date: 2019-02-24 00:00:00

Phylodynamics is a set of population genetics tools that aim at reconstructing demographic history of a population based on molecular sequences of individuals sampled from the population of interest. One important task in phylodynamics is to estimate changes in (effective) population size. When applied to infectious disease sequences such estimation of population size trajectories can provide information about changes in the number of infections. To model changes in the number of infected individuals, current phylodynamic methods use non-parametric approaches, parametric approaches, and stochastic modeling in conjunction with likelihood-free Bayesian methods. The first class of methods yields results that are hard-to-interpret epidemiologically. The second class of methods provides estimates of important epidemiological parameters, such as infection and removal/recovery rates, but ignores variation in the dynamics of infectious disease spread. The third class of methods is the most advantageous statistically, but relies on computationally intensive particle filtering techniques that limits its applications. We propose a Bayesian model that combines phylodynamic inference and stochastic epidemic models, and achieves computational tractability by using a linear noise approximation (LNA) --- a technique that allows us to approximate probability densities of stochastic epidemic model trajectories. LNA opens the door for using modern Markov chain Monte Carlo tools to approximate the joint posterior distribution of the disease transmission parameters and of high dimensional vectors describing unobserved changes in the stochastic epidemic model compartment sizes (e.g., numbers of infectious and susceptible individuals). We apply our estimation technique to Ebola genealogies estimated using viral genetic data from the 2014 epidemic in Sierra Leone and Liberia.
A Novel Deterministic Framework for Non-probabilistic Recommender Systems
Authors: Avinash Bhat, Divya Madhav Kamath, Anitha C
Date: 2018-09-02 00:00:00
Proceeding: Emerging Technologies in Data Mining and Information Security, Proceedings of IEMIS 2018 2018 9
Tasks: Bayesian Inference, Collaborative Filtering, Recommendation Systems

Recommendation is a technique which helps  and suggests a  user,  any relevant item from  a large information space.  Current  techniques for this purpose include non-probabilistic methods like content-based filtering and collaborative filtering  (CF)  and probabilistic methods like  Bayesian inference and Case-based reasoning methods.  CF algorithms use similarity measures for calculating similarity between users.  In this paper, we propose a novel framework which deterministically switches between the  CF algorithms based on sparsity to improve accuracy of recommendation.
MLGCN: Multi-Laplacian Graph Convolutional Networks for Human Action Recognition
Authors: Ahmed Mazari, Hichem Sahbi
Date: 2019-09-11 00:00:00
Proceeding: 30th British Machine Vision Conference 2019 9
Tasks: Action Recognition, Skeleton Based Action Recognition, Temporal Action Localization

Convolutional neural networks are nowadays witnessing a major success in different pattern recognition problems. These learning models were basically designed to handle vectorial data such as images but their extension to non-vectorial and semi-structured data (namely graphs with variable sizes, topology, etc.) remains a major challenge, though a few interesting solutions are currently emerging. In this paper, we introduce MLGCN; a novel spectral Multi-Laplacian Graph Convolutional Network. The main ontribution of this method resides in a new design principle that learns graph-laplacians as convex combinations of other elementary 
laplacians – each one dedicated to a particular topology of the input graphs. We also introduce a novel pooling operator, on graphs, that proceeds in two steps: context-dependent node expansion is achieved, followed by a global average pooling; the strength of this two-step process resides in its ability to preserve the discrimination power of nodes while achieving permutation invariance. Experiments conducted on SBU and UCF-101 datasets, show the validity of our method for the challenging task of action recognition. 

Supplementary :  https://bit.ly/2ku2lYv
On Geometric Features for Skeleton-Based Action Recognition using Multilayer LSTM Networks
Authors: Songyang Zhang, Xiaoming Liu, Jun Xiao
Date: 2017-03-01 00:00:00
Proceeding: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) 2017 3
Tasks: Action Recognition, Skeleton Based Action Recognition

RNN-based approaches have achieved outstanding performance on action recognition with skeleton inputs. Currently these methods limit their inputs to coordinates of joints and improve the accuracy mainly by extending RNN models to spatial domains in various ways. While such models explore relations between different parts directly from joint coordinates, we provide a simple universal spatial modeling method perpendicular to the RNN model enhancement. Specifically, we select a set of simple geometric features, motivated by the evolution of previous work. With experiments on a 3-layer LSTM framework, we observe that the geometric relational features based on distances between joints and selected lines outperform other features and achieve state-of-art results on four datasets. Further, we show the sparsity of input gate weights in the first LSTM layer trained by geometric features and demonstrate that utilizing joint-line distances as input require less data for training.
Convolutional Sequence Generation for Skeleton-Based Action Synthesis
Authors: Sijie Yan, Zhizhong Li, Yuanjun Xiong, Huahan Yan
Date: 2019-10-27 00:00:00
Proceeding: ICCV 2019 2019 10
Tasks: Human action generation

In this work, we aim to generate long actions represented as sequences of skeletons. The generated sequences must demonstrate continuous, meaningful human actions, while maintaining coherence among body parts. Instead of generating skeletons sequentially following an autoregressive model, we propose a framework that generates the entire sequence altogether by 
 ransforming from a sequence of latent vectors sampled from a Gaussian process (GP). This framework, named Convolutional Sequence Generation Network (CSGN), jointly models structures in temporal and spatial dimensions. It captures the temporal 
structure at multiple scales through the GP prior and the temporal convolutions; and establishes the spatial connection between the latent vectors and the skeleton graphs via a novel graph refining scheme. It is noteworthy that CSGN allows bidirectional transforms between the latent and the observed spaces, thus enabling semantic manipulation of the action sequences in various forms. We conducted empirical studies on multiple datasets, including a set of high-quality dancing sequences collected by us. The results show that our framework can produce long action sequences that are coherent across time steps and among body parts.
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks
Authors: Sanjeev Arora, Simon S. Du, Zhiyuan Li, Ruslan Salakhutdinov, Ruosong Wang, Dingli Yu
Date: 2019-10-03 00:00:00
Proceeding: ICLR 2020 1
Tasks: Few-Shot Image Classification, General Classification, image-classification, Image Classification, regression, Small Data Image Classification, Transfer Learning

Recent research shows that the following two models are equivalent: (a) infinitely wide neural networks (NNs) trained under l2 loss by gradient descent with infinitesimally small learning rate (b) kernel regression with respect to so-called Neural Tangent Kernels (NTKs) (Jacot et al., 2018). An efficient algorithm to compute the NTK, as well as its convolutional counterparts, appears in Arora et al. (2019a), which allowed studying performance of infinitely wide nets on datasets like CIFAR-10. However, super-quadratic running time of kernel methods makes them best suited for small-data tasks. We report results suggesting neural tangent kernels perform strongly on low-data tasks. 1. On a standard testbed of classification/regression tasks from the UCI database, NTK SVM beats the previous gold standard, Random Forests (RF), and also the corresponding finite nets. 2. On CIFAR-10 with 10 - 640 training samples, Convolutional NTK consistently beats ResNet-34 by 1% - 3%. 3. On VOC07 testbed for few-shot image classification tasks on ImageNet with transfer learning (Goyal et al., 2019), replacing the linear SVM currently used with a Convolutional NTK SVM consistently improves performance. 4. Comparing the performance of NTK with the finite-width net it was derived from, NTK behavior starts at lower net widths than suggested by theoretical analysis(Arora et al., 2019a). NTK's efficacy may trace to lower variance of output.