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How can we transfer knowledge from large foundation models to a much smaller downstream model? In new ICML work, we show a simple yet conceptually significant modification to knowledge distillation works well with negligible overhead! arxiv.org/abs/2406.07337 1/9
Are all these time-series-specific model design necessary for deep forecasters / foundation forecasting models? In Chronos, we claim no novelty in time series modeling, but that's exactly the point. "Everything should be made as simple as possible, but no simpler." #TimeSeries
🚀 Excited to share that we released Chronos today. Chronos is a framework for building pretrained time series models based on language model architectures. Simple idea: quantize time series into tokens and feed them into 🤗 @huggingface models. 🧵
🚀 Excited to share that we released Chronos today. Chronos is a framework for building pretrained time series models based on language model architectures. Simple idea: quantize time series into tokens and feed them into 🤗 @huggingface models. 🧵
Excited to share a project that I had the incredible opportunity to contribute to during my last summer internship at AWS: Chronos 📈! Chronos is a framework for pretrained probabilistic time series models that demonstrates exceptional zero-shot performance.
Excited to share our latest work! Chronos is a zero-shot forecasting model that can generate accurate predictions for new time series not seen during training. 📜Paper: arxiv.org/abs/2403.07815 💻Code: github.com/amazon-science… 🤗Model weights: huggingface.co/collections/am…
The paper decisions are out for our ICLR 2024 Workshop on AI4DifferentialEquations In Science! See ai4diffeqtnsinsci.github.io/papers for details! Congratulations to all authors! #ai4science @iclr_conf
📢 Final #CFP! Submit your research to the #AI4DifferentialEquations in Science Workshop @ #ICLR by Feb 10th! Excited to announce our partnership with @Algorithms_MDPI for a special issue featuring selected papers from our workshop. Stay tuned at ai4diffeqtnsinsci.github.io
Reminder for a final call for papers for our AI4DifferentialEquations In Science 2024 ICLR Workshop due Saturday, February 10th AoE! Call for Papers: openreview.net/group?id=ICLR.… @iclr_conf #ai4science
Appreciations to @danielle_maddix for a captivating @AIforGood talk, shedding light on physics-constrained #ML for scientific computing 💻🌐 In case you missed it, watch it now on: youtube.com/live/qflj9ZPL1…
Ensure you catch the talk tomorrow at 5 PM! See you online🎙️ @AIforGood @danielle_maddix #reminder
Our next @AIforGood talk in the "AI for Earth and Sustainability Science" series is just #oneweek away! 🌎 Join us online on Jan 31 at 5 PM for a talk with @danielle_maddix, moderated by @Reichstein_BGC 👥 Register here: aiforgood.itu.int/event/physics-… ✨
Our next @AIforGood talk in the "AI for Earth and Sustainability Science" series is just #oneweek away! 🌎 Join us online on Jan 31 at 5 PM for a talk with @danielle_maddix, moderated by @Reichstein_BGC 👥 Register here: aiforgood.itu.int/event/physics-… ✨
At ICLR, Amazon scientists are hosting their first workshop on AI4DifferentialEquations in Science. They invite all submissions on using machine learning to solve differential equations with applications in science and engineering: amzn.to/3Hiyb11 #ICLR2024 #AI4Science
Machine learning (ML) models could help solve difficult problems in physics, but their outputs sometimes violate basic physical laws. In two papers, Amazon researchers show how to impose physics-based constraints on ML models. #MachineLearning #SciML
Our team has research intern positions available for this fall (or late summer) at @awscloud AI Labs in Bay Area and Berlin, in the field of AI4Science / SciML, and time series forecasting with applications in supply chain optimization and cloud-based systems. DM if interested.
Our paper "Guiding continuous operator learning through Physics-based boundary constraints" is on arxiv (lnkd.in/gRDE5qFt)! Try our code here: lnkd.in/gXe6gm8h. Happy to have collaborated with Nadim Saad, Gaurav Gupta and Shima Alizadeh on this work.
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xiyuanzh
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