C♥️LM Workshop 2024
@CaLM_Workshop
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C♥️LM: First Workshop on Causality and Large Models @ NeurIPS 2024
Joined August 2024
We are happy 😁 to announce 📢 the First Workshop on Causality and Large Models (C♥️LM) at #NeurIPS2024 . 📜 Submission deadline: September 06 (4-6 pages).💻 Website:
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Our Workshop on Causality and Large Models (C♥️LM) at #NeurIPS2024 has started in East Exhibition Hall C! Come join us!.
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We are in the final lap towards the submission deadline for the Workshop on Causality and Large Models (C♥️LM). Stay calm 😉, polish your manuscripts and submit before the end of the day September 23, 2024, anywhere on earth.🌐.
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The deadline (September 23, 2024, AoE) for the Workshop on Causality and Large Models (C♥️LM) at #NeurIPS2024 is approaching but there's no cause 😉 to panic. While you prepare your submissions have a look at this Zotero library with related references:
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We have extended the submission deadline to September 23, 2024, AoE. You can visit our website to submit a paper or sign up as a reviewer:
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We have extended the submission deadline to September 23, 2024, AoE. You can visit our website to submit a paper or sign up as a reviewer:
We are happy 😁 to announce 📢 the First Workshop on Causality and Large Models (C♥️LM) at #NeurIPS2024 . 📜 Submission deadline: September 06 (4-6 pages).💻 Website:
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We invite papers on the following topics and more:. → Causality for large models: Applying ideas from causality to improve large models. → Causality with large models: Leveraging large models to improve causal inference.
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The First Workshop on Causality and Large Models (C♥️LM) will take place @NeurIPSConf 2024 . 📜 Submission deadline: September 06 (4-6 pages).💻 Website: #NeurIPS2024.
Announcing the NeurIPS 2024 Workshops!. Read our blog for details on our selection process this year - and the list of accepted workshops for 2024!.
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Organised by a great team: @felixludos, @cchoi314, @luigigres, @anNicolicioiu, @LiXiusi, @SophieXhon11060, @rpatrik96, @ValvodaJosef, Haoxuan Li, @Mengyue_Yang_, @dhanya_sridhar, @bschoelkopf.
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→ Causality in large models: Assessing the causal knowledge captured by large models and their (causal) reasoning abilities. → Causality of large models: Investigating the causal structure of how large models work and how to make them more interpretable and controllable.
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We invite papers on the following topics and more:. → Causality for large models: Applying ideas from causality to improve large models. → Causality with large models: Leveraging large models to improve causal inference.
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