Privatar uses horizontal frequency partitioning and distribution-aware minimal perturbation to enable private offloading of VR avatar reconstruction, supporting 2.37x more users with modest overhead.
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CROSS compiler maps HE workloads to TPU architecture via basis-aligned and memory-aligned transformations, reporting higher throughput-per-watt than prior GPU and ASIC libraries on NTT and HE operators.
Gemini Ultra reaches human-expert performance on MMLU for the first time and sets new state-of-the-art results on 30 of 32 benchmarks, including all 20 multimodal ones tested.
Gopher, a 280 billion parameter language model, achieves state-of-the-art performance on the majority of 152 tasks with largest gains in reading comprehension, fact-checking, and toxic language detection.
citing papers explorer
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Privatar: Scalable Privacy-preserving Multi-user VR via Secure Offloading
Privatar uses horizontal frequency partitioning and distribution-aware minimal perturbation to enable private offloading of VR avatar reconstruction, supporting 2.37x more users with modest overhead.
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Leveraging ASIC AI Chips for Homomorphic Encryption
CROSS compiler maps HE workloads to TPU architecture via basis-aligned and memory-aligned transformations, reporting higher throughput-per-watt than prior GPU and ASIC libraries on NTT and HE operators.
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Gemini: A Family of Highly Capable Multimodal Models
Gemini Ultra reaches human-expert performance on MMLU for the first time and sets new state-of-the-art results on 30 of 32 benchmarks, including all 20 multimodal ones tested.
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Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Gopher, a 280 billion parameter language model, achieves state-of-the-art performance on the majority of 152 tasks with largest gains in reading comprehension, fact-checking, and toxic language detection.