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1000 results for “OpenRefine”
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A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods
Tiago Salvador, Kilian FATRAS, Ioannis Mitliagkas, Adam M Oberman
Action editor: Mingsheng Long.
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A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods
Tiago Salvador, Kilian FATRAS, Ioannis Mitliagkas, Adam M Oberman
Action editor: Mingsheng Long.
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A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods
Tiago Salvador, Kilian FATRAS, Ioannis Mitliagkas, Adam M Oberman
Action editor: Mingsheng Long.
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MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information
Tim Ruhkopf, Aditya Mohan, Difan Deng, Alexander Tornede, Frank Hutter, Marius Lindauer
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MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information
Tim Ruhkopf, Aditya Mohan, Difan Deng, Alexander Tornede, Frank Hutter, Marius Lindauer
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MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information
Tim Ruhkopf, Aditya Mohan, Difan Deng, Alexander Tornede, Frank Hutter, Marius Lindauer
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MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information
Tim Ruhkopf, Aditya Mohan, Difan Deng, Alexander Tornede, Frank Hutter, Marius Lindauer
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MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information
Tim Ruhkopf, Aditya Mohan, Difan Deng, Alexander Tornede, Frank Hutter, Marius Lindauer
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Slowly, but without pause, our #FLOSS slicer for resin #3dPrinting is starting to take shape :) .
https://codeberg.org/SoulCrafted/Slicer
However, we are not alone, there are other people trying to bring new open source solutions to this space.
In a few days the #OpenResinAlliance ( https://openresin.org/ ) will release a first early access version to their backers on April 1st (no joke), and an open beta on on May 1st.
They started long before we typed our first line of code. So far, from what I saw, they are bringing something awesome to the table.
I hope we'll find ways to collaborate.
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Slowly, but without pause, our #FLOSS slicer for resin #3dPrinting is starting to take shape :) .
https://codeberg.org/SoulCrafted/Slicer
However, we are not alone, there are other people trying to bring new open source solutions to this space.
In a few days the #OpenResinAlliance ( https://openresin.org/ ) will release a first early access version to their backers on April 1st (no joke), and an open beta on on May 1st.
They started long before we typed our first line of code. So far, from what I saw, they are bringing something awesome to the table.
I hope we'll find ways to collaborate.
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We will be participating in the Google #SummerOfCode again this year! Come work with us!
https://summerofcode.withgoogle.com/programs/2024/organizations/openrefine-j0
#gsoc #java #javascript #foss -
A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scores
Naoya Takeishi, Yoshinobu Kawahara
Action editor: Mingsheng Long.
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NFDI4Culture has awarded @OpenRefine a EUR 10,000 grant to enhance its reconciliation feature.
Planned Working Packages:
1️⃣ Redesigning and redeveloping the #reconciliation dialog
2️⃣ Improving the presentation of reconciled data
3️⃣ Enhancing the #dataEnrichment process
4️⃣ User-friendly error messageshttps://openrefine.org/blog/2023/08/23/nfdi-grant
#OpenRefine ^kb -
Generating Teammates for Training Robust Ad Hoc Teamwork Agents via Best-Response Diversity
Arrasy Rahman, Elliot Fosong, Ignacio Carlucho, Stefano V Albrecht
Action editor: Angeliki Lazaridou.
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Learning better with Dale’s Law: A Spectral Perspective - #NeurIPS2023 contribution by Li et al. (2023). It discusses how to train brainlike #RNNs with separate inhibitory and excitatory units with similar performance as standard RNNs:
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Learning better with Dale’s Law: A Spectral Perspective - #NeurIPS2023 contribution by Li et al. (2023). It discusses how to train brainlike #RNNs with separate inhibitory and excitatory units with similar performance as standard RNNs:
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Learning better with Dale’s Law: A Spectral Perspective - #NeurIPS2023 contribution by Li et al. (2023). It discusses how to train brainlike #RNNs with separate inhibitory and excitatory units with similar performance as standard RNNs:
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Learning better with Dale’s Law: A Spectral Perspective - #NeurIPS2023 contribution by Li et al. (2023). It discusses how to train brainlike #RNNs with separate inhibitory and excitatory units with similar performance as standard RNNs:
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Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning
Matthew Kyle Schlegel, Volodymyr Tkachuk, Adam M White, Martha White
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Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning
Matthew Kyle Schlegel, Volodymyr Tkachuk, Adam M White, Martha White
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Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning
Matthew Kyle Schlegel, Volodymyr Tkachuk, Adam M White, Martha White
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Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning
Matthew Kyle Schlegel, Volodymyr Tkachuk, Adam M White, Martha White
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Attentional-Biased Stochastic Gradient Descent
Qi Qi, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang
Action editor: Changyou Chen.
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Attentional-Biased Stochastic Gradient Descent
Qi Qi, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang
Action editor: Changyou Chen.
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Attentional-Biased Stochastic Gradient Descent
Qi Qi, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang
Action editor: Changyou Chen.
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Attentional-Biased Stochastic Gradient Descent
Qi Qi, Yi Xu, Wotao Yin, Rong Jin, Tianbao Yang
Action editor: Changyou Chen.
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Spectral Regularization Allows Data-frugal Learning over Combinatorial Spaces
Amirali Aghazadeh, Nived Rajaraman, Tony Tu, Kannan Ramchandran
Action editor: Joan Bruna.
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Spectral Regularization Allows Data-frugal Learning over Combinatorial Spaces
Amirali Aghazadeh, Nived Rajaraman, Tony Tu, Kannan Ramchandran
Action editor: Joan Bruna.
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Spectral Regularization Allows Data-frugal Learning over Combinatorial Spaces
Amirali Aghazadeh, Nived Rajaraman, Tony Tu, Kannan Ramchandran
Action editor: Joan Bruna.
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Spectral Regularization Allows Data-frugal Learning over Combinatorial Spaces
Amirali Aghazadeh, Nived Rajaraman, Tony Tu, Kannan Ramchandran
Action editor: Joan Bruna.