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  • Weight Poisoning Attacks on Pre-trained Models

    Keita Kurita, Graham Neubig, Paul Michel , arXiv preprint ,  2020
    Jupyter Notebook

    Free

  • Weight Pruning via Adaptive Sparsity Loss

    Georgios Goumas, George Retsinas, Petros Maragos, Athena Elafrou , arXiv preprint ,  2020
    Python

    Free

  • Weight Uncertainty in Neural Networks

    Julien Cornebise, Charles Blundell, Daan Wierstra, Koray Kavukcuoglu , 32nd International Conference on Machine Learning, ICML 2015 ,  2015
    Multiple

    Free

  • Weighted Cox Regression Using the R Package coxphw

    Daniela Dunkler, Meinhard Ploner, Michael Schemper, Georg Heinze , Journal of Statistical Software ,  2018
    R

    Free

  • Weighted Distance-Based Models for Ranking Data Using the R Package rankdist

    Zhaozhi Qian, Philip L. H. Yu , Journal of Statistical Software ,  2019
    R

    Free

  • Weighted Fisher Discriminant Analysis in the Input and Feature Spaces

    Mark Crowley, Fakhri Karray, Milad Sikaroudi, Benyamin Ghojogh, H. R. Tizhoosh , Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ,  2020
    Python

    Free

  • Weighted graph coloring based pilot decontamination for multi-cell massive MIMO systems

    Xudong Zhu, Linglong Dai, Zhaocheng Wang, Xiaodong Wang , IEEE Transactions on Vehicular Technology ,  2017
    MATLAB

    Free

  • Weighted Linear Bandits for Non-Stationary Environments

    Olivier Cappé, Claire Vernade, Yoan Russac , NeurIPS 2019 12 ,  2019
    Jupyter Notebook

    Free

  • Weighted Neural Bag-of-n-grams Model: New Baselines for Text Classification

    Bofang Li, Zhe Zhao, Puwei Wang, Xiaoyong Du, Tao Liu , COLING 2016 12 ,  2016
    C

    Free

  • Weighted Nuclear Norm Minimization and Its Applications to Low Level Vision

    Shuhang Gu, Qi Xie, Deyu Meng, Wangmeng Zuo, Xiangchu Feng, and Lei Zhang , International Journal of Computer Vision ,  2017
    MATLAB

    Free

  • Weighted Nuclear Norm Minimization with Application to Image Denoising

    Shuhang Gu, Lei Zhang, Wangmeng Zuo, Xiangchu Feng , Conference on Computer Vision and Pattern Recognition 2014 ,  2014
    MATLAB

    Free

  • Weighted Random Search for CNN Hyperparameter Optimization

    Razvan Andonie, Adrian-Catalin Florea , International Journal of Computers, Communications and Control ,  2020
    Go

    Free

  • Weighted Schatten p-Norm Minimization for Image Denoising and Background Subtraction

    Yuan Xie, Shuhang Gu, Yan Liu, Wangmeng Zuo, Wensheng Zhang, and Lei Zhang , IEEE Transactions on Image Processing ,  2016
    MATLAB

    Free

  • Weighted Spectral Embedding of Graphs

    Alexandre Hollocou, Thomas Bonald, Marc Lelarge , 2018 56th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2018 ,  2018
    Jupyter Notebook

    Free

  • Weighted Speech Distortion Losses for Neural-Network-Based Real-Time Speech Enhancement

    Ross Cutler, Harishchandra Dubey, Sebastian Braun, Ivan Tashev, Chandan K. A. Reddy, Yangyang Xia , IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020 2 ,  2020
    Unspecified

    Free

  • Weighted Sum Rate Maximization for MIMO Broadcast Channels Using Dirty Paper Coding and Zero-forcing Methods

    Le-Nam Tran, Markku Juntti, Mats Bengtsson, and Bjorn Ottersten , IEEE Transactions on Communications ,  2013
    MATLAB

    Free

  • Weighted Sum Rate Optimization for Multicell MIMO Systems with Hardware-Impaired Transceivers

    R. Brandt, E. Björnson, M. Bengtsson , IEEE Int. Conf. Acoustics, Speech, Signal Process. (ICASSP'14) ,  2014
    MATLAB

    Free

  • Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks

    Huayan Guo, Ying-Chang Liang, J. Chen, E. Larsson , IEEE Transactions on Wireless Communications ,  2020
    MATLAB

    Free

  • Weighted Transformer Network for Machine Translation

    Richard Socher, Nitish Shirish Keskar, Karim Ahmed , ICLR 2018 1 ,  2017
    Multiple

    Free

  • Weighted-Lasso for Structured Network Inference from Time Course Data

    Camille Charbonnier, Julien Chiquet, Christophe Ambroise , Statistical applications in genetics and… ,  2010
    R

    Free

  • Weightless: Lossy Weight Encoding For Deep Neural Network Compression

    Gu-Yeon Wei, David Brooks, Robert Adolf, Brandon Reagen, Udit Gupta, Michael M. Mitzenmacher, Alexander M. Rush , ICML 2018 7 ,  2017
    Multiple

    Free

  • WeightNet: Revisiting the Design Space of Weight Networks

    Xiangyu Zhang, Jiawei Huang, Jian Sun, Ningning Ma , ECCV 2020 8 ,  2020
    Python

    Free

  • Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks

    Matthias Fey, Gaurav Rattan, Christopher Morris, Martin Grohe, William L. Hamilton, Jan Eric Lenssen, Martin Ritzert , 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 ,  2018
    Multiple

    Free

  • Well-Read Students Learn Better: On the Importance of Pre-training Compact Models

    Ming-Wei Chang, Kristina Toutanova, Kenton Lee, Iulia Turc , ICLR 2020 1 ,  2019
    Multiple

    Free

  • WERd: Using Social Text Spelling Variants for Evaluating Dialectal Speech Recognition

    Peter Bell, Ahmed Ali, Steve Renals, Preslav Nakov , 2017 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2017 - Proceedings ,  2017
    Unspecified

    Free

  • WESPE: Weakly Supervised Photo Enhancer for Digital Cameras

    Andrey Ignatov, Radu Timofte, Luc Van Gool, Nikolay Kobyshev, Kenneth Vanhoey , IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops ,  2017
    Multiple

    Free

  • WGANSing

    Merlijn Blaauw, Pritish Chandna , Interspeech 2019 3 ,  2019
    Python

    Free

  • WGCNA: an R package for weighted correlation network analysis

    Peter Langfelder, Steve Horvath , BMC Bioinformatics ,  2008
    R

    Free

  • WHAM!: Extending Speech Separation to Noisy Environments

    Gordon Wichern, Michael Flynn, Joe Antognini, Emmett McQuinn, Jonathan Le Roux, Dwight Crow, Licheng Richard Zhu, Ethan Manilow , Interspeech 2019 ,  2019
    Python

    Free

  • What Actions are Needed for Understanding Human Actions in Videos?

    Abhinav Gupta, Gunnar A. Sigurdsson, Olga Russakovsky , ICCV 2017 10 ,  2017
    Python

    Free

  • What Are People Asking About COVID-19? A Question Classification Dataset

    Jason Wei, Chengyu Huang, Soroush Vosoughi, Jerry Wei , arXiv preprint ,  2020
    Python

    Free

  • What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language models

    Allyson Ettinger , TACL 2020 1 ,  2019
    Python

    Free

  • What Can Neural Networks Reason About?

    Keyulu Xu, Jingling Li, Mozhi Zhang, Simon S. Du, Ken-ichi Kawarabayashi, Stefanie Jegelka , ICLR 2020 1 ,  2019
    Python

    Free

  • What Deep CNNs Benefit from Global Covariance Pooling: An Optimization Perspective

    Li Zhang, Qinghua Hu, Banggu Wu, Peihua Li, Qilong Wang, Wangmeng Zuo, Dongwei Ren , CVPR 2020 6 ,  2020
    Python

    Free

  • What Do Editors Maximize? Evidence from Four Economics Journals

    David Card, Stefano DellaVigna , Review of Economics and Statistics ,  2019
    R

    Free

  • What Do Single-view 3D Reconstruction Networks Learn?

    Stephan R. Richter, Thomas Brox, Maxim Tatarchenko, Zhuwen Li, Vladlen Koltun, René Ranftl , CVPR 2019 6 ,  2019
    Jupyter Notebook

    Free

  • What do you learn from context? Probing for sentence structure in contextualized word representations

    Adam Poliak, Benjamin Van Durme, Samuel R. Bowman, Patrick Xia, Najoung Kim, Alex Wang, R Thomas McCoy, Dipanjan Das, Ellie Pavlick, Ian Tenney, Berlin Chen , ICLR 2019 5 ,  2019
    Jupyter Notebook

    Free

  • What does BERT know about books, movies and music? Probing BERT for Conversational Recommendation

    Claudia Hauff, Gustavo Penha , Fourteenth ACM Conference on Recommender Systems ,  2020
    Python

    Free

  • What Does BERT Learn about the Structure of Language?

    Ganesh Jawahar, Djam{\'e} Seddah, Beno{\^\i}t Sagot , ACL 2019 7 ,  2019
    Python

    Free

  • What Does BERT Look At? An Analysis of BERT’s Attention

    Urvashi Khandelwal, Christopher D. Manning, Kevin Clark, Omer Levy , WS 2019 8 ,  2019
    Jupyter Notebook

    Free

  • What Else Can Fool Deep Learning? Addressing Color Constancy Errors on Deep Neural Network Performance

    Michael S Brown, Mahmoud Afifi , ICCV 2019 10 ,  2019
    MATLAB

    Free

  • What Friends Are Made of: Bilateral Linkages and Domestic Drivers of Foreign Policy Alignment with China

    Georg Strüver , FOREIGN POLICY ANALYSIS ,  2014
    R

    Free

  • What Goes with Red and Blue? Mapping Partisan and Ideological Associations in the Minds of Voters

    Stephen N. Goggin, John A. Henderson, Alexander Theodoridis , POLITICAL BEHAVIOR ,  2019
    R

    Free

  • What I Like About You: Legislator Personality and Legislator Approval

    Jonathan D. Klingler, Gary E. Hollibaugh, Adam Ramey , POLITICAL BEHAVIOR ,  2019
    R

    Free

  • What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization

    Xinyi Zheng, Danai Koutra, Jilles Vreeken, Caleb Belth , Proceedings of The Web Conference 2020 ,  2020
    Python

    Free

  • What Is One Grain of Sand in the Desert? Analyzing Individual Neurons in Deep NLP Models

    Yonatan Belinkov, Nadir Durrani, Hassan Sajjad, Fahim Dalvi, Anthony Bau, James Glass , 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 ,  2018
    JavaScript

    Free

  • What is the Essence of a Claim? Cross-Domain Claim Identification

    Christian Stab, Iryna Gurevych, Johannes Daxenberger, Ivan Habernal, Steffen Eger , EMNLP 2017 9 ,  2017
    Java

    Free

  • What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption Generator?

    Marc Tanti, Albert Gatt, Kenneth P. Camilleri , WS 2017 9 ,  2017
    Multiple

    Free

  • What is the State of Neural Network Pruning?

    Jose Javier Gonzalez Ortiz, Davis Blalock, Jonathan Frankle, John Guttag , arXiv preprint ,  2020
    Python

    Free

  • What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis

    Junyeop Lee, Hwalsuk Lee, Seong Joon Oh, Sangdoo Yun, Sungrae Park, Jeonghun Baek, Geewook Kim, Dongyoon Han , ICCV 2019 10 ,  2019
    Jupyter Notebook

    Free

  • What it Thinks is Important is Important: Robustness Transfers through Input Gradients

    Alvin Chan, Yew-Soon Ong, Yi Tay , CVPR 2020 6 ,  2019
    Python

    Free

  • What Looks Good with my Sofa: Multimodal Search Engine for Interior Design

    Łukasz Brocki, Ivona Tautkute, Tomasz Trzciński, Wojciech Stokowiec, Aleksandra Możejko, Krzysztof Marasek , Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, FedCSIS 2017 ,  2017
    Jupyter Notebook

    Free

  • What made you do this? Understanding black-box decisions with sufficient input subsets

    Jonas Mueller, Siddhartha Jain, Brandon Carter, David Gifford , AISTATS 2019 - 22nd International Conference on Artificial Intelligence and Statistics ,  2018
    HTML

    Free

  • What Makes a Good Neighbor? Race, Place, and Norms of Political Participation

    Allison P. Anoll , AMERICAN POLITICAL SCIENCE REVIEW ,  2018
    R

    Free

  • What Makes A Good Story? Designing Composite Rewards for Visual Storytelling

    Yu Cheng, Junjie Hu, Jianfeng Gao, Zhe Gan, Jingjing Liu, Graham Neubig , Proceedings of the AAAI Conference on Artificial Intelligence ,  2019
    Python

    Free

  • What makes for prize-winning television?

    Sara Connolly, Chris Hanretty, Shaun P. Hargreaves Heap, John Street , EUROPEAN JOURNAL OF COMMUNICATION ,  2015
    R

    Free

  • What Makes Foreign Policy Teams Tick: Explaining Variation in Group Performance at Geopolitical Forecasting

    Michael Horowitz, Brandon M. Stewart, +6 authors Philip Tetlock , THE JOURNAL OF POLITICS ,  2019
    R

    Free

  • What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?

    Thomas Brox, Eddy Ilg, Daniel Cremers, Nikolaus Mayer, Philipp Fischer, Caner Hazirbas, Alexey Dosovitskiy , International Journal of Computer Vision ,  2018
    C++

    Free

  • What Makes Training Multi-Modal Classification Networks Hard?

    Weiyao Wang, Du Tran, Matt Feiszli , CVPR 2020 6 ,  2019
    Multiple

    Free

  • What Matters in Unsupervised Optical Flow

    Rico Jonschkowski, Kurt Konolige, Anelia Angelova, Jonathan T. Barron, Ariel Gordon, Austin Stone , ECCV 2020 8 ,  2020
    CODE NOT FOUND

    Free

  • What Object Should I Use? – Task Driven Object Detection

    Yaser Souri, Juergen Gall, Johann Sawatzky, Christian Grund , CVPR 2019 6 ,  2019
    Python

    Free

  • What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning

    Robert R. Tung, Dragomir R. Radev, Alexander R. Fabbri, Irene Li , arXiv preprint ,  2018
    Python

    Free

  • What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning

    Robert R. Tung, Dragomir R. Radev, Alexander R. Fabbri, Irene Li , 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 ,  2018
    Python

    Free

  • What the Constant Velocity Model Can Teach Us About Pedestrian Motion Prediction

    Vincent Aravantinos, Christoph Schöller, Florian Lay, Alois Knoll , IEEE Robotics and Automation Letters ,  2019
    Multiple

    Free

  • What they do when in doubt: a study of inductive biases in seq2seq learners

    Rahma Chaabouni, Eugene Kharitonov , arXiv preprint ,  2020
    Python

    Free

  • What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features

    YooJung Choi, Yitao Liang, Pasha Khosravi, Guy Van den Broeck , IJCAI International Joint Conference on Artificial Intelligence ,  2019
    Jupyter Notebook

    Free

  • What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment

    Mohit Bansal, Matthew R. Walter, Hongyuan Mei , NAACL 2016 6 ,  2015
    Python

    Free

  • What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

    Alex Kendall, Yarin Gal , NeurIPS 2017 12 ,  2017
    Multiple

    Free

  • What value do explicit high level concepts have in vision to language problems?

    Anthony Dick, Anton van den Hengel, Qi Wu, Lingqiao Liu, Chunhua Shen , CVPR 2016 6 ,  2015
    Multiple

    Free

  • What Would You Expect? Anticipating Egocentric Actions with Rolling-Unrolling LSTMs and Modality Attention

    Giovanni Maria Farinella, Antonino Furnari , ICCV 2019 10 ,  2019
    Multiple

    Free

  • What You Say and How You Say it: Joint Modeling of Topics and Discourse in Microblog Conversations

    Jing Li, Cuiyun Gao, Michael R. Lyu, Irwin King, Jichuan Zeng, Yulan He , TACL 2019 3 ,  2019
    Python

    Free

  • What Your Username Says About You

    Mari Ostendorf, Aaron Jaech , EMNLP 2015 9 ,  2015
    Python

    Free

  • What’s Hidden in a Randomly Weighted Neural Network?

    Mohammad Rastegari, Mitchell Wortsman, Vivek Ramanujan, Ali Farhadi, Aniruddha Kembhavi , CVPR 2020 6 ,  2019
    Python

    Free

  • What’s in a Domain? Learning Domain-Robust Text Representations using Adversarial Training

    Timothy Baldwin, Trevor Cohn, Yitong Li , NAACL 2018 6 ,  2018
    Python

    Free

  • What’s in a Question: Using Visual Questions as a Form of Supervision

    Abhinav Gupta, Siddha Ganju, Olga Russakovsky , CVPR 2017 7 ,  2017
    Lua

    Free

  • What’s Missing: A Knowledge Gap Guided Approach for Multi-hop Question Answering

    Ashish Sabharwal, Peter Clark, Tushar Khot , IJCNLP 2019 11 ,  2019
    Python

    Free

  • What’s There in the Dark

    Saptakatha Adak, Sauradip Nag, Sukhendu Das , 26th IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan 2019 9 ,  2019
    Python

    Free

  • What’s Cookin’? Interpreting Cooking Videos using Text, Speech and Vision

    Kevin Murphy, Jonathan Malmaud, Andrew Rabinovich, Nicholas Johnston, Jonathan Huang, Vivek Rathod , HLT 2015 5 ,  2015
    Unspecified

    Free

  • What's Cookin'? Interpreting Cooking Videos using Text, Speech and Vision

    Kevin Murphy, Jonathan Malmaud, Nick Johnston, Andrew Rabinovich, Jonathan Huang, Vivek Rathod , NAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics Human Language Technologies, Proceedings of the Conference ,  2015
    Unspecified

    Free

  • When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?

    Graham Neubig, Ye Qi, Sarguna Janani Padmanabhan, Devendra Singh Sachan, Matthieu Felix , NAACL 2018 6 ,  2018
    Python

    Free

  • When can we trust population trends? A method for quantifying the effects of sampling interval and duration

    Hannah S. Wauchope, Tatsuya Amano, William J. Sutherland, Alison Johnston , Methods in Ecology and Evolution ,  2019
    R

    Free

  • When can we trust population trends? A method for quantifying the effects of sampling interval and duration

    Hannah S. Wauchope, Tatsuya Amano, William J. Sutherland, Alison Johnston , Methods in Ecology and Evolution ,  2019
    R

    Free

  • When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks

    Yi-Chang James Tsai, Chao-Han Huck Yang, Pin-Yu Chen, Yi-Chieh Liu, Xiaoli Ma , Proceedings - International Conference on Image Processing, ICIP ,  2019
    Jupyter Notebook

    Free

  • When Color Constancy Goes Wrong: Correcting Improperly White-Balanced Images

    Brian Price, Mahmoud Afifi, Michael S. Brown, Scott Cohen , CVPR 2019 6 ,  2019
    MATLAB

    Free

  • When Deep Learning Meets Digital Image Correlation

    K. Abdelouahab, B. Blaysat, M. Grediac, F. Berry, S. Boukhtache, F. Sur , Optics and Lasers in Engineering ,  2020
    Python

    Free

  • When Deep Learning Met Code Search

    Koushik Sen, Jose Cambronero, Seohyun Kim, Satish Chandra, Hongyu Li , ESEC/FSE 2019 - Proceedings of the 2019 27th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering ,  2019
    Jupyter Notebook

    Free

  • When Do Renters Behave Like Homeowners? High Rent, Price Anxiety, and NIMBYism

    Michael S. Hankinson , AMERICAN POLITICAL SCIENCE REVIEW ,  2018
    R

    Free

  • When Do States Say Uncle? Network Dependence and Sanction Compliance

    Cassy L. Dorff, Shahryar Minhas , INTERNATIONAL INTERACTIONS ,  2017
    R

    Free

  • When Do the Advantaged See the Disadvantages of Others? A Quasi-Experimental Study of National Service

    Cecilia Hyunjung Mo, Katharine M. Conn , AMERICAN POLITICAL SCIENCE REVIEW ,  2018
    R

    Free

  • When Do the Rich Win

    J. Alexander Branham, Stuart Soroka, Christopher Wlezien , POLITICAL SCIENCE QUARTERLY ,  2017
    R

    Free

  • When Does Label Smoothing Help?

    Simon Kornblith, Rafael Müller, Geoffrey Hinton , NeurIPS 2019 12 ,  2019
    Python

    Free

  • When Does Self-Supervision Help Graph Convolutional Networks?

    Tianlong Chen, Yuning You, Yang Shen, Zhangyang Wang , ICML 2020 1 ,  2020
    Python

    Free

  • When Does Self-supervision Improve Few-shot Learning?

    Subhransu Maji, Bharath Hariharan, Jong-Chyi Su , ECCV 2020 8 ,  2019
    Python

    Free

  • When experts disagree: Response aggregation and Its consequences in expert surveys

    Rene Lindstaedt, Sven-Oliver Proksch, Jonathan B. Slapin , POLITICAL SCIENCE RESEARCH AND METHODS ,  2018
    R

    Free

  • When Explanations Lie: Why Many Modified BP Attributions Fail

    Tim Landgraf, Maximilian Granz, Leon Sixt , ICML 2020 1 ,  2019
    Jupyter Notebook

    Free

  • When Extremism Pays: Policy Positions, Voter Certainty, and Party Support in Postcommunist Europe

    EzrowLawrence, HomolaJonathan, TavitsMargit , THE JOURNAL OF POLITICS ,  2014
    R

    Free

  • When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach

    Xianming Liu, Ding Liu, Bihan Wen, Thomas S. Huang, Zhangyang Wang , IJCAI International Joint Conference on Artificial Intelligence ,  2017
    Multiple

    Free

  • When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks

    Ziwei Liu, Rui Xu, Dahua Lin, Yuzhe Yang, Minghao Guo , CVPR 2020 6 ,  2019
    Python

    Free

  • When Relation Networks meet GANs: Relation GANs with Triplet Loss

    Yizhou Yu, Yue Wang, Lijun Wang, Runmin Wu, Pingping Zhang, Huchuan Lu, Kunyao Zhang , arXiv preprint ,  2020
    Python

    Free

  • When size matters: advantages of weighted effect coding in observational studies.

    Manfred te Grotenhuis, Ben Pelzer, Rob Eisinga, Rense Nieuwenhuis, Alexander SchmidtCatran, Ruben Konig , INTERNATIONAL JOURNAL OF PUBLIC HEALTH ,  2017
    R

    Free

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