yes we are working hard on Initialization Parameter Optimization!
MACHINE LEARNING
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LongCePO Framework Outperforms o1-mini and Claude Sonnet
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CePO – our test time reasoning framework – has an upgrade! LongCePO is able to combine information across contexts for effectively unlimited context reasoning. It outperforms o1-mini and Claude Sonnet in LongBench v2. Read our blog to learn more: https://
cerebras.ai/blog/longcepo -
DeepScaleR: 1.5B Model Achieves 43.1% Pass@1 on AIME2024
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Developed by Sijun Tan (UC Berkeley) & team, this model smashes benchmarks w/ a 43.1% Pass@1 accuracy on AIME2024, a +14.3% over its base model! DeepScaleR, a 1.5B parameter model fine tuned from Deepseek-R1-Distilled-Qwen-1.5B. Watch for more: https://
bit.ly/4kmLiRf -
Training Deep Networks Without Normalization Using Parameterized Tanh
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New paper: turns out you can train deep nets without normalization layers by replacing them with a parameterized tanh()
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DataAISummit San Francisco June 9-12 AI Data Intelligence
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#DataAISummit is coming to San Francisco June 9 -12! Explore the innovations shaping the next era of AI and data intelligence. Topics we’ll cover include: AI and AI agents Data warehousing Analytics and BI And more Register today: https://
dbricks.co/4i7X93H -
AI Dev 25 Conference Kicks Off in San Francisco
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It’s starting – just kicked off AI Dev 25, the AI developer conference, in San Francisco! Happy Pi day!
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Axelera AI Metis M.2 Enables Real-time YOLOv8 on Raspberry Pi
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Axelera AI + Raspberry Pi = #EdgeAI for #PiDay2025! Here’s a sneak peek of a @Raspberry_Pi 5 + Axelera AI Metis M.2 running real-time #YOLOv8 inference. Just an early test—but what demos would you like to see with Axelera on an RPi? Watch this space! #PiDay
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Multi-Agent Systems and Google’s CO-Scientist: The Future of AI
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Multi-agent systems are the future, and Google’s CO-Scientist is an exciting early prototype. AI agents are evolving—chat-based, trigger-based, and autonomous. Now, imagine a high-level agent orchestrating a minion agent to get things done.
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Intent Analysis Boosts LLM Accuracy Through ARR Method
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Can intent analysis boost LLM accuracy? ARR—analyzing intent, retrieving data, and reasoning—offers a fresh take on AI responses. Read more: https://
bit.ly/4hF3ZwB #AI #MachineLearning -
LLM Distillation: Optimizing AI Model Development Techniques
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Which of these is most important to you when building AI models? Find out how LLM distillation optimizes them all in our latest blog, where we dive into how the technique streamlines AI model development https://
snorkel.ai/blog/llm-disti
llation-demystified-a-complete-guide/
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