Camille Couturier

334 posts

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Camille Couturier

Camille Couturier

@couturierc

Applied researcher @Microsoft; ideas my own

Katılım Mayıs 2011
173 Takip Edilen259 Takipçiler
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Mehrdad Farajtabar
Mehrdad Farajtabar@MFarajtabar·
1/ Can Large Language Models (LLMs) truly reason? Or are they just sophisticated pattern matchers? In our latest preprint, we explore this key question through a large-scale study of both open-source like Llama, Phi, Gemma, and Mistral and leading closed models, including the recent OpenAI GPT-4o and o1-series. arxiv.org/pdf/2410.05229 Work done with @i_mirzadeh, @KeivanAlizadeh2, Hooman Shahrokhi, Samy Bengio, @OncelTuzel. #LLM #Reasoning #Mathematics #AGI #Research #Apple
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Denis Wirtz
Denis Wirtz@deniswirtz·
Here is our largest database of PhD fellowships yet: we found 195 fellowships and funding opportunities. For each fellowship, this searchable database lists description, deadline, amount, and eligibility criteria. Download it here: research.jhu.edu/rdt/funding-op…
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Jim Fan
Jim Fan@DrJimFan·
Wow, now you can estimate full-body pose of multiple people with nothing but Wi-Fi signal 🤯 I can think of 2 killer apps based on this tech: 1. Full-body VR gaming with just home Wi-Fi. 2. Fall detection of elders in hospital. No cams for better privacy. Saves lives! 1/🧵
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Caroline Chavier
Caroline Chavier@MrsCaroline_C·
Jeudi dernier, @ingridtherwath et moi avons accueilli Constance, Azadi. Cette petite fille est un élan de vie inextinguible. Nous sommes ivres de fatigue et de bonheur. Notre famille est désormais au complet. Merci aux équipes exceptionnelles de la @MaterniteBluets 💜
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Camille Couturier
Camille Couturier@couturierc·
@SFR Merci de bien vouloir supprimer mon adresse email, utilisée sans mon consentement, de vos bases de données.
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Ananya Kumar
Ananya Kumar@ananyaku·
How should you fine-tune a large pretrained model (CLIP, SimCLR) robustly? We find that standard fine-tuning can do poorly out-of-distribution (test data ≠ fine-tuning data). Our analysis leads to a simple fix, higher accuracy on 10 datasets. arxiv.org/abs/2202.10054 (ICLR Oral)
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Alfredo Canziani
Alfredo Canziani@alfcnz·
An awesome @geoffreyhinton presenting «The Forward-Forward Algorithm for Training Deep Neural Networks». A training algorithm that does not require to propagate derivatives or remember neural activities and learns representations of streaming sensory data on the fly. #NeurIPS22
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Luiz Pessoa
Luiz Pessoa@PessoaBrain·
𝗠𝘂𝗹𝘁𝗶𝘀𝗰𝗮𝗹𝗲 𝗯𝗿𝗮𝗶𝗻 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴 as the way to go! "The quest for multiscale brain modeling", by Egidio D’Angelo and Viktor Jirsa. How do all scales influence each other?? doi.org/10.1016/j.tins…
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Camille Couturier
Camille Couturier@couturierc·
@TERPays2LaLoire Bonjour, dans le TER 857323 entre Le Mans et Nantes. Il fait super froid (clim à fond). Y at-il possibilité de moduler la clim ? 2 enjeux: 1/ climatique et économie d'énergie 2/ ne pas attraper de rhume en plein juillet. :-)
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Brian Lester
Brian Lester@blester125·
My first @GoogleAI residency project was accepted to @emnlpmeeting #EMNLP2021! Prompt Tuning can condition a frozen T5 XXL model to perform new tasks while only adding 0.003% more parameters and no performance loss. Camera Ready 📸: arxiv.org/abs/2104.08691 Quick Thread 🧵(1/7)
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Akari Asai
Akari Asai@AkariAsai·
Introducing 𝗔𝗧𝗧𝗘𝗠𝗣𝗧, a new modular, multi-task, and parameter-efficient approach to combine knowledge from multiple tasks to solve a new task using a small trainable of parameters 🔥 while keeping the original LM *frozen* 🧊 [1/9] Paper 📜: homes.cs.washington.edu/~akari/papers/…
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Thomas Zimmermann
Thomas Zimmermann@tomzimmermann·
The Productivity + Intelligence group in @MSFTResearch seeks experienced research managers in empirical software engineering to join us in advancing research on data-driven and AI-infused developer productivity experiences. For more information: careers.microsoft.com/us/en/job/9848…
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Michele Tufano
Michele Tufano@tufanomichele·
Check out our work at Microsoft on Unit Test Case Generation with Transformers! Models pretrained on English and source code, then finetuned on the task of generating test cases. Paper: arxiv.org/abs/2009.05617 Collaborators: @DawnDrainML, @asvyatko, @skdeng, Neel Sundaresan
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