Scientific workshop "Big Data Analytics"

1 | Community detection in social networks | https://bit.ly/32u1jP2
2 | User characterization for online social networks | https://arxiv.org/pdf/1611.03971
3 | Recommendations with a Purpose | https://web-ainf.aau.at/pub/jannach/files/Conference_RecSys2016.pdf
4 | Untangling blockchain: A data processing view of blockchain systems | https://ieeexplore.ieee.org/iel7/69/4358933/08246573.pdf
5 | Deep neural networks for youtube recommendations | https://research.google/pubs/pub45530.pdf
6 | Information evolution in social networks | https://arxiv.org/pdf/1402.6792
7 | Online actions with offline impact: How online social networks influence online and offline user behavior | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5361221/

Sayed Mohammad Sajjadi wrote:

1 | Community detection in social networks | https://bit.ly/32u1jP2
2 | User characterization for online social networks | https://arxiv.org/pdf/1611.03971
3 | Recommendations with a Purpose | https://web-ainf.aau.at/pub/jannach/files/Conference_RecSys2016.pdf
4 | Untangling blockchain: A data processing view of blockchain systems | https://ieeexplore.ieee.org/iel7/69/4358933/08246573.pdf
5 | Deep neural networks for youtube recommendations | https://research.google/pubs/pub45530.pdf
6 | Information evolution in social networks | https://arxiv.org/pdf/1402.6792
7 | Online actions with offline impact: How online social networks influence online and offline user behavior | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5361221/

A survey of the recent architectures of deep convolutional neural networks https://bit.ly/3bWQY1i
Reinforcement learning applied to Forex trading https://bit.ly/33p4qXT
Federated Learning: Challenges, Methods, and Future Directions https://bit.ly/3isylEU
Review of Deep Learning Algorithms and Architectures https://bit.ly/2ZAIyIe
Financial time series forecasting model based on CEEMDAN and LSTM https://bit.ly/35z7P9l

*Developing theoretical contributions in information systems via text analytics:
https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0280-6
*Making Reproducible Research Data by Utilizing Persistent ID Graph Structure:
https://ieeexplore.ieee.org/document/9070341
*Anomaly Detection for Science DMZs Using System Performance Data:
https://ieeexplore.ieee.org/document/9049695
*Leveraging Data Science To Combat COVID-19: A Comprehensive Review:
https://www.semanticscholar.org/paper/Leveraging-Data-Science-To-Combat-COVID-19%3A-A-Latif-Usman/0751d2fa3a54cbbb4d594f2ee47c3aa7e4003a24
*Analyzing changes in the complexity of climate in the last four decades using MERRA-2 radiation data:
https://paperity.org/p/233416441/analyzing-changes-in-the-complexity-of-climate-in-the-last-four-decades-using-merra-2

Evgeniy Pavlovskiy wrote:

Record of seminar: https://youtu.be/xMWuuKEl2SI

|_.Paper|_.Link| |1 VoiceFilter from Google |https://google.github.io/speaker-id/publications/VoiceFilter/ | |2 Wavesplit - Июль 2020.  SDR 21.0 на WSJ0-mix2 |https://arxiv.org/pdf/2002.08933v2.pdf| |3 Neural Supersampling for Real-time Rendering (Facebok) |https://research.fb.com/wp-content/uploads/2020/06/Neural-Supersampling-for-Real-time-Rendering.pdf (https://neurohive.io/ru/novosti/nejroset-ot-fair-povyshaet-razreshenie-izobrazheniya-v-16-raz/) | |4 DeepFaceDrawing: Deep Generation of Face Images from Sketches |http://geometrylearning.com/paper/DeepFaceDrawing.pdf (https://neurohive.io/ru/novosti/deepfacedrawing-nejroset-generiruet-izobrazheniya-ljudej-po-sketcham/)| |5 Tacotron 2 (without wavenet) |https://github.com/NVIDIA/tacotron2 (paper: https://arxiv.org/pdf/1712.05884.pdf) | |6 DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation. |https://arxiv.org/pdf/2006.04868.pdf | |7 LoCo: Local Contrastive Representation Learning, |https://arxiv.org/abs/2008.01342 (https://arxiv.org/abs/2008.01342)| |8 3D Self-Supervised Methods for Medical Imaging |https://arxiv.org/abs/2006.03829 (https://arxiv.org/abs/2006.03829)| |9 Brain Tumor Survival Prediction using Radiomics Features, |https://arxiv.org/abs/2009.02903| |10 Multilingual Speech Recognition with Corpus Relatedness Sampling. |https://isca-speech.org/archive/Interspeech_2019/pdfs/3052.pdf| |11 Does BERT Make Any Sense? |https://arxiv.org/pdf/1909.10430.pdf| |12 Unsupervised Cross-lingual Representation Learning at Scale |https://arxiv.org/pdf/1911.02116.pdf| |13 Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness |https://arxiv.org/pdf/1905.13472.pdf| |14 The Bottom-up Evolution of Representations in the Transformer:A Study with Machine Translation and Language Modeling Objectives |https://arxiv.org/pdf/1909.01380.pdf| |15 Zero-Shot Learning - A ComprehensiveEvaluation of the Good, the Bad and the Ugly |https://arxiv.org/pdf/1707.00600.pdf| |16 Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders |https://arxiv.org/pdf/1812.01784.pdf| |17 Rethinking Generative Zero-Shot Learning: An EnsembleLearning Perspective for Recognising Visual Patches |https://arxiv.org/pdf/2007.13314.pdf| |18 Plug and Play Language Models: A Simple Approach to Controlled Text Generation |https://arxiv.org/pdf/1912.02164.pdf| |19 Deep Visual Attention Prediction

|20 Attention Is All You Need

|21 Neural Ordinary Differential Equations

|22 Reversible Architectures for Arbitrarily Deep Residual Neural Networks

|23 DetectoRS: Detecting Objects with Recursive Feature Pyramid

and Switchable Atrous Convolution |https://arxiv.org/pdf/2006.02334v1.pdf|

new item

new item

Mask R-CNN https://arxiv.org/abs/1703.06870

Depth-Aware Video Frame Interpolation https://arxiv.org/abs/1904.00830

Combining Machine Learning and Natural Language Processing to Assess Literary Text Comprehension https://files.eric.ed.gov/fulltext/ED577127.pdf

Deep Visual-Semantic Alignments for Generating Image Descriptions https://arxiv.org/pdf/1412.2306v2.pdf

Learning a SAT Solver from Single-Bit Supervision https://arxiv.org/abs/1802.03685

1) A big data analytical architecture for the Asset Management https://www.sciencedirect.com/science/article/pii/S2212827117301634
2) Big Data Analytics and Its Applications in Supply Chain Management https://www.intechopen.com/books/new-trends-in-the-use-of-artificial-intelligence-for-the-industry-4-0/big-data-analytics-and-its-applications-in-supply-chain-management
3) Big Data analytics in oil and gas industry: An emerging trend https://www.researchgate.net/publication/329353733_Big_Data_analytics_in_oil_and_gas_industry_An_emerging_trend
4) Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities https://journalofbigdata.springeropen.com/articles/10.1186/s40537-020-00329-2
5) Using Text Mining to Estimate Schedule Delay Risk of 13 Offshore Oil and Gas EPC Case Studies During the Bidding Process https://www.researchgate.net/publication/333317949_Using_Text_Mining_to_Estimate_Schedule_Delay_Risk_of_13_Offshore_Oil_and_Gas_EPC_Case_Studies_During_the_Bidding_Process
6) Applying Data Science Techniques to Improve Information Discovery in Oil And Gas Unstructured Data https://www.onepetro.org/conference-paper/IPTC-20236-Abstract

https://arxiv.org/pdf/1708.09757.pdf
Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers /2017/59 Cit.

https://arxiv.org/pdf/1711.11240.pdf
Quantum Neuron: an elementary building block for machine learning on quantum computers /2018/49 Cit.

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8241753
Deep Learning Applications in Medical Image Analysis /2018/251 cit

Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity
https://arxiv.org/pdf/2002.10585.pdf /2019/29 cit

Convolutional Recurrent Neural Networks forPolyphonic Sound Event Detection /2017/240 cit
https://arxiv.org/pdf/1702.06286.pdf

Language Models are Few-Shot Learners (GPT-3) -https://arxiv.org/abs/2005.14165 https://openai.com/blog/openai-api/
StyleGAN2 - http://arxiv.org/abs/1912.04958 https://github.com/NVlabs/stylegan2
"Towards a Human-like Open-Domain Chatbot" https://arxiv.org/abs/2001.09977
"Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis": https://arxiv.org/abs/1806.04558
ASAPP-ASR: Multistream CNN and Self-Attentive SRU for SOTA Speech Recognition https://arxiv.org/pdf/2005.10469v1.pdf

Deep learning
https://www.nature.com/articles/nature14539

Generative Adversarial Networks
https://arxiv.org/abs/1406.2661

Data mining with big data
https://ieeexplore.ieee.org/abstract/document/6547630

Scalable Nearest Neighbor Algorithms for High Dimensional Data
https://ieeexplore.ieee.org/document/6809191

An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics
https://www.sciencedirect.com/science/article/pii/S0020025513005124

QBSO-FS: A Reinforcement Learning Based Bee Swarm Optimization Metaheuristic for Feature Selection
https://sci-hub.tw/https://doi.org/10.1007/978-3-030-20518-8_65

Machine Learning for Medical Imaging
https://pubs.rsna.org/doi/pdf/10.1148/rg.2017160130

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://arxiv.org/abs/1905.11946

MixMatch: A Holistic Approach to Semi-Supervised Learning
http://papers.nips.cc/paper/8749-mixmatch-a-holistic-approach-to-semi-supervised-learning.pdf

Adversarial Machine Learning at Scale
https://arxiv.org/abs/1611.01236

Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy
https://sci-hub.tw/10.1109/MIS.2017.40

|19 Deep Visual Attention Prediction

https://arxiv.org/pdf/1705.02544.pdf

|20 Attention Is All You Need

https://arxiv.org/pdf/1706.03762.pdf

|21 Neural Ordinary Differential Equations

https://arxiv.org/pdf/1806.07366.pdf

|22 Reversible Architectures for Arbitrarily Deep Residual Neural Networks

https://arxiv.org/pdf/1709.03698.pdf

|23 DetectoRS: Detecting Objects with Recursive Feature Pyramid

and Switchable Atrous Convolution |https://arxiv.org/pdf/2006.02334v1.pdf|

Virgilio Espina wrote:

A survey of the recent architectures of deep convolutional neural networks https://bit.ly/3bWQY1i
Reinforcement learning applied to Forex trading https://bit.ly/33p4qXT
Federated Learning: Challenges, Methods, and Future Directions https://bit.ly/3isylEU
Review of Deep Learning Algorithms and Architectures https://bit.ly/2ZAIyIe
Financial time series forecasting model based on CEEMDAN and LSTM https://bit.ly/35z7P9l

Emerging trends in geospatial artificial intelligence (geoAI): potential applications for environmental epidemiology https://bit.ly/3kgU9ny

1. Generative Dual Adversarial Network for Generalized Zero-Shot Learning - CVPR 2019 - cited by 39
https://paperswithcode.com/paper/generative-dual-adversarial-network-for

2. Attribute Attention for Semantic Disambiguation in Zero-Shot Learning - ICCV 2019 - cited by 10
https://paperswithcode.com/paper/attribute-attention-for-semantic

3. Zero-Shot Semantic Segmentation - NeurIPS 2019 - cited by 11
https://paperswithcode.com/paper/190600817

4. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks - IJCNLP 2019 - cited by 194
https://paperswithcode.com/paper/sentence-bert-sentence-embeddings-using

5. (Booked for presentation by Nikolaev N.) Attention-based deep residual learning network for entity relation extraction in Chinese EMRs - BMC Medical Informatics and Decision Making volume 19, Article number: 55 (2019)
https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-019-0769-0

Classification is a Strong Baseline for Deep Metric Learning https://arxiv.org/abs/1811.12649v2
CTRL: A Conditional Transformer Language Model for Controllable Generation https://arxiv.org/abs/1909.05858v2
LSTM Pose Machines https://arxiv.org/abs/1712.06316v4
Image Super-Resolution Using Very Deep Residual Channel Attention Networks https://arxiv.org/abs/1807.02758v2
Transformer-OCR https://github.com/fengxinjie/Transformer-OCR

1 Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning
Cite as: Phys. Fluids 32, 053605 (2020); https://doi.org/10.1063/5.0006492
2 Accelerating deep reinforcement learning strategies of flow control through a multi-environment approach
Cite as: Phys. Fluids 31, 094105 (2019); https://doi.org/10.1063/1.5116415
3 Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control. J. Fluid Mech. (2019), vol. 865, pp. 281–302. doi:10.1017/jfm.2019.62