The Win AI Group was established to promote activities delivered by women in the field of computer science in general and AI in particular, in order to create a more equitable reality between men and women in academia and industry.
Graduate women students who wish to advance AI research are invited to leave details and participate in activities we organize.
We invite data scientists (women) who are active in the AI field to submit their research to us.
As part of our first event, we will host a group of graduate women students who will present to us their latest work received at the world’s leading AI conferences during 2019.
Looking forward to meeting you all,
TalkSumm: A Dataset and Scalable Annotation Method for Scientific Paper Summarization Based on Conference Talks (ACL 2019) Guy Lev , Michal Shmueli-Scheuer , Jonathan Herzig , Achiya Jerbi , David Konopnicki
TalkSumm-A-Dataset-and-Scalable-Annotation-Method-for-Scientific-Paper-Summarization-Based-on-Conference-Talks-P19-1204
Momen(e)t: Flavor the Moments in Learning to Classify Shapes (ICCV 2019 Workshop) Mor Joseph-Rivlin , Alon Zvirin , Ron Kimmel
Momenet-Flavor-the-Moments-in-Learning-to-Classify-Shapes-ICCVW2019
MeshCNN: A Network with an Edge (Siggraph 2019) Rana Hanocka , Amir Hertz , Noa Fish , Raja Giryes , Shachar Fleishman , Daniel Cohen-Or CODE SITE
MeshCNN-A-Network-with-an-Edge-Siggraph-2019-1809.05910
MeshCNN-A-Network-with-an-Edge-Siggraph-2019-Slides
Dimensionality reduction: bridging the gap between theory and practice (NeurIPS 2019) Yair Bartal, Ofer Neiman, Nova Ora Fandina CODE SITE
Dimensionality-Reduction-Theoretical-Peprspective-on-Practical-Measures-NeurIPS2019
Dimensionality-Reduction-Theoretical-Peprspective-on-Practical-Measures-NeurIPS2019-Slides
Optimal Algorithm for Bayesian Incentive-Compatible Exploration between theory and practice (EC 2019) Lee Cohen , Yishay Mansour arXiv TAU
Optimal-Algorithm-for-Bayesian-Incentive-Compatible-Exploration-between-theory-and-practice-EC-2019-1810.10304
Aligning Vector-spaces with Noisy Supervised Lexicons (NAACL 2019 ) Noa Yehezkel Lubin, Jacob Goldberger, Yoav Goldberg arXiv CODE ACL
Aligning-Vector-spaces-with-Noisy-Supervised-Lexicons-NAACL-2019-1903.10238
Unsupervised Learning of Dense Shape Correspondence (CVPR 2019) Oshri Halimi, Or Litany, Emanuele Rodolà, Alex Bronstein, Ron Kimmel arXiv CODE CVF Supplementary Material
Unsupervised-Learning-of-Dense-Shape-Correspondence-CVPR-2019-1812.02415
VIDEO
SEGA: Searching efficiently for new generator architectures (NeurIPS 2019 Workshop) Sivan Doveh, Raja Giryes MetaLearn 2019
NeurIPS-2019-Workshop-Book
A Local Block Coordinate Descent Algorithm for the Convolutional Sparse Coding Model (CVPR 2019) Ev Zisselman, Jeremias Sulam, Michael Elad arXiv CODE CVF Supplementary Material
A-Local-Block-Coordinate-Descent-Algorithm-for-the-Convolutional-Sparse-Coding-Model-CVPR-2019-1811.00312
Informative Object Annotations: Tell Me Something I Don’t Know (CVPR 2019) Lior Bracha , Gal Chechik arXiv CODE CVF
Informative-Object-Annotations-Tell-Me-Something-I-Dont-Know-CVPR-2019-1812.10358
Provably Powerful Graph Networks (NeurIPS 2019) Haggai Maron , Heli Ben-Hamu , Hadar Serviansky , Yaron Lipman arXiv NeurIPS NIPS Proceedingsβ Supplementary Material
Provably-Powerful-Graph-Networks-NeurIPS-2019-1905.11136
Tags: A Local Block Coordinate Descent Algorithm for the Convolutional Sparse Coding Model , academia , Achiya Jerbi , ACL 2019 , active , activities , administrative perspectives , advance AI research , AI , AI field , AI methods , Alex Bronstein , Aligning Vector-spaces with Noisy Supervised Lexicons , Alon Zvirin , Amir Hertz , analytics , analytics space , applied machine learning , artificial intelligence , arXiv , association , Automation of Machine-Learning , Autonomous driving , Bayesian , Big Data Analytics , broad spectrum , classification , clustering , code , comments , Computational neural networks , computer vision , Conference , content , Continuous delivery , Cross validation , CVF , CVPR 2019 , Cyber , Daniel Cohen-Or , Data Science , Data Scientists , database , David Konopnicki , decision trees , deep learning , delivering , DESIGN , Developers , DevOps , Dialogue Bots , Dimensionality reduction: bridging the gap between theory and practice , domains , during 2019 , EC 2019 , Education , Emanuele Rodolà , engineering , equitable reality , established , Ethics of artificial intelligence , Ev Zisselman , excellent , experience , Explaining of Israel , Fintech , first event , focus , Future of AI , Gal Chechik , Gradient descent algorithm , Graduate , graduate women students , great success , Guy Lev , Hadar Serviansky , Haggai Maron , Healthcare , Heli Ben-Hamu , helpful feedback , high-quality , ICCV 2019 , ICCV 2019 Workshop , ideas , improvement , industry , industry tracks , Informative Object Annotations: Tell Me Something I Don’t Know , innovation , Intelligent robots , iot , Jacob Goldberger , Jeremias Sulam , Jonathan Herzig , Large scale analytics , latest work , leading experts , Lee Cohen , Lior Bracha , Looking forward , Machine ethics , MACHINE LEARNING , meeting you all , MeshCNN: A Network with an Edge , MetaLearn 2019 , Michael Elad , Michal Shmueli-Scheuer , Momen(e)t: Flavor the Moments in Learning to Classify Shapes , Mor Joseph-Rivlin , NAACL 2019 , Natural language processing , Natural Language Understanding , neural networks , NeurIPS , NeurIPS 2019 , NeurIPS 2019 Workshop , NIPS , Noa Fish , Noa Yehezkel Lubin , Nova Ora Fandina , Ofer Neiman , Optimal Algorithm for Bayesian Incentive-Compatible Exploration between theory and practice , Or Litany , organizing conference , Oshri Halimi , Overfitting , participants , participate , predictive applications , Proceedingsβ , Provably Powerful Graph Networks , Raja Giryes , Rana Hanocka , real world , real-world domains , Registration , regression , reinforcement learning , research and application , research innovations , research track , researchers , Retail , Robot rights , robotics , Ron Kimmel , SEGA: Searching efficiently for new generator architectures , Shachar Fleishman , Siggraph 2019 , Sivan Doveh , sponsorship , state-of-the-art , students , supervised learning , Supplementary Material , support vector machines , Systems for ML , TalkSumm: A Dataset and Scalable Annotation Method for Scientific Paper Summarization Based on Conference Talks , technical presentations , TECHNOLOGY , The conference , The Summit , The Win AI Group , the world’s leading AI conferences , Threat to human dignity , topics , tutorial , unsupervised learning , Unsupervised Learning of Dense Shape Correspondence , Weaponization of AI , Win AI 2019 , WinAI 2019 , WinAI team , women , women in academia and industry , women in the field of computer science , Yair Bartal , Yaron Lipman , Yishay Mansour , Yoav Goldberg
About The Author
CODESIGN.BLOG
WinAI 2019
sherry 0 Data Science, Design, Engineering, Explaining of Israel, Technology,
The Win AI Group was established to promote activities delivered by women in the field of computer science in general and AI in particular, in order to create a more equitable reality between men and women in academia and industry.
Graduate women students who wish to advance AI research are invited to leave details and participate in activities we organize.
We invite data scientists (women) who are active in the AI field to submit their research to us.
As part of our first event, we will host a group of graduate women students who will present to us their latest work received at the world’s leading AI conferences during 2019.
Looking forward to meeting you all,
WinAI team
TalkSumm: A Dataset and Scalable Annotation Method for Scientific Paper Summarization Based on Conference Talks (ACL 2019)
TalkSumm-A-Dataset-and-Scalable-Annotation-Method-for-Scientific-Paper-Summarization-Based-on-Conference-Talks-P19-1204Guy Lev, Michal Shmueli-Scheuer, Jonathan Herzig, Achiya Jerbi, David Konopnicki
Momen(e)t: Flavor the Moments in Learning to Classify Shapes (ICCV 2019 Workshop)
Momenet-Flavor-the-Moments-in-Learning-to-Classify-Shapes-ICCVW2019Mor Joseph-Rivlin, Alon Zvirin, Ron Kimmel
MeshCNN: A Network with an Edge (Siggraph 2019)
MeshCNN-A-Network-with-an-Edge-Siggraph-2019-1809.05910Rana Hanocka, Amir Hertz, Noa Fish, Raja Giryes, Shachar Fleishman, Daniel Cohen-Or CODE SITE
MeshCNN-A-Network-with-an-Edge-Siggraph-2019-Slides
Dimensionality reduction: bridging the gap between theory and practice (NeurIPS 2019)
Dimensionality-Reduction-Theoretical-Peprspective-on-Practical-Measures-NeurIPS2019Yair Bartal, Ofer Neiman, Nova Ora Fandina CODE SITE
Dimensionality-Reduction-Theoretical-Peprspective-on-Practical-Measures-NeurIPS2019-Slides
Optimal Algorithm for Bayesian Incentive-Compatible Exploration between theory and practice (EC 2019)
Optimal-Algorithm-for-Bayesian-Incentive-Compatible-Exploration-between-theory-and-practice-EC-2019-1810.10304Lee Cohen, Yishay Mansour arXiv TAU
Aligning Vector-spaces with Noisy Supervised Lexicons (NAACL 2019)
Aligning-Vector-spaces-with-Noisy-Supervised-Lexicons-NAACL-2019-1903.10238Noa Yehezkel Lubin, Jacob Goldberger, Yoav Goldberg arXiv CODE ACL
Unsupervised Learning of Dense Shape Correspondence (CVPR 2019)
Unsupervised-Learning-of-Dense-Shape-Correspondence-CVPR-2019-1812.02415Oshri Halimi, Or Litany, Emanuele Rodolà, Alex Bronstein, Ron Kimmel arXiv CODE CVF Supplementary Material
SEGA: Searching efficiently for new generator architectures (NeurIPS 2019 Workshop)
Sivan Doveh, Raja Giryes MetaLearn 2019
A Local Block Coordinate Descent Algorithm for the Convolutional Sparse Coding Model (CVPR 2019)
A-Local-Block-Coordinate-Descent-Algorithm-for-the-Convolutional-Sparse-Coding-Model-CVPR-2019-1811.00312Ev Zisselman, Jeremias Sulam, Michael Elad arXiv CODE CVF Supplementary Material
Informative Object Annotations: Tell Me Something I Don’t Know (CVPR 2019)
Informative-Object-Annotations-Tell-Me-Something-I-Dont-Know-CVPR-2019-1812.10358Lior Bracha, Gal Chechik arXiv CODE CVF
Provably Powerful Graph Networks (NeurIPS 2019)
Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, Yaron Lipman arXiv NeurIPS NIPS Proceedingsβ Supplementary Material
Tags: A Local Block Coordinate Descent Algorithm for the Convolutional Sparse Coding Model, academia, Achiya Jerbi, ACL 2019, active, activities, administrative perspectives, advance AI research, AI, AI field, AI methods, Alex Bronstein, Aligning Vector-spaces with Noisy Supervised Lexicons, Alon Zvirin, Amir Hertz, analytics, analytics space, applied machine learning, artificial intelligence, arXiv, association, Automation of Machine-Learning, Autonomous driving, Bayesian, Big Data Analytics, broad spectrum, classification, clustering, code, comments, Computational neural networks, computer vision, Conference, content, Continuous delivery, Cross validation, CVF, CVPR 2019, Cyber, Daniel Cohen-Or, Data Science, Data Scientists, database, David Konopnicki, decision trees, deep learning, delivering, DESIGN, Developers, DevOps, Dialogue Bots, Dimensionality reduction: bridging the gap between theory and practice, domains, during 2019, EC 2019, Education, Emanuele Rodolà, engineering, equitable reality, established, Ethics of artificial intelligence, Ev Zisselman, excellent, experience, Explaining of Israel, Fintech, first event, focus, Future of AI, Gal Chechik, Gradient descent algorithm, Graduate, graduate women students, great success, Guy Lev, Hadar Serviansky, Haggai Maron, Healthcare, Heli Ben-Hamu, helpful feedback, high-quality, ICCV 2019, ICCV 2019 Workshop, ideas, improvement, industry, industry tracks, Informative Object Annotations: Tell Me Something I Don’t Know, innovation, Intelligent robots, iot, Jacob Goldberger, Jeremias Sulam, Jonathan Herzig, Large scale analytics, latest work, leading experts, Lee Cohen, Lior Bracha, Looking forward, Machine ethics, MACHINE LEARNING, meeting you all, MeshCNN: A Network with an Edge, MetaLearn 2019, Michael Elad, Michal Shmueli-Scheuer, Momen(e)t: Flavor the Moments in Learning to Classify Shapes, Mor Joseph-Rivlin, NAACL 2019, Natural language processing, Natural Language Understanding, neural networks, NeurIPS, NeurIPS 2019, NeurIPS 2019 Workshop, NIPS, Noa Fish, Noa Yehezkel Lubin, Nova Ora Fandina, Ofer Neiman, Optimal Algorithm for Bayesian Incentive-Compatible Exploration between theory and practice, Or Litany, organizing conference, Oshri Halimi, Overfitting, participants, participate, predictive applications, Proceedingsβ, Provably Powerful Graph Networks, Raja Giryes, Rana Hanocka, real world, real-world domains, Registration, regression, reinforcement learning, research and application, research innovations, research track, researchers, Retail, Robot rights, robotics, Ron Kimmel, SEGA: Searching efficiently for new generator architectures, Shachar Fleishman, Siggraph 2019, Sivan Doveh, sponsorship, state-of-the-art, students, supervised learning, Supplementary Material, support vector machines, Systems for ML, TalkSumm: A Dataset and Scalable Annotation Method for Scientific Paper Summarization Based on Conference Talks, technical presentations, TECHNOLOGY, The conference, The Summit, The Win AI Group, the world’s leading AI conferences, Threat to human dignity, topics, tutorial, unsupervised learning, Unsupervised Learning of Dense Shape Correspondence, Weaponization of AI, Win AI 2019, WinAI 2019, WinAI team, women, women in academia and industry, women in the field of computer science, Yair Bartal, Yaron Lipman, Yishay Mansour, Yoav Goldberg
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About The Author
sherry
CODESIGN.BLOG