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  • 🚀 What is Trending in AI Research?: IP-Adapter + FineRecon + PUMA + DeciCoder + SeamlessM4T....

🚀 What is Trending in AI Research?: IP-Adapter + FineRecon + PUMA + DeciCoder + SeamlessM4T....

This newsletter brings AI research news that is much more technical than most resources but still digestible and applicable

Researchers at Tencent AI Lab Introduce IP-Adapter, a lightweight solution that adds image prompt capabilities to pretrained models. Utilizing a decoupled cross-attention mechanism for text and image features, IP-Adapter achieves comparable performance to fully fine-tuned models but with only 22M parameters. Its design allows for generalization across different models and supports multimodal image generation when combined with text prompts.

This paper from Apple introduces FineRecon, a method featuring three key solutions. First, it employs a resolution-agnostic Truncated Signed Distance Function (TSDF) supervision strategy to optimize network learning. Second, it incorporates a depth guidance strategy using multi-view depth estimates for better surface accuracy. Lastly, the method refines the network architecture to condition output on high-resolution image features, enhancing detail sharpness. FineRecon outperforms existing methods in multiple depth and 3D reconstruction metrics.

This paper introduces PUMA, a framework aimed at fast and secure Transformer model inference. PUMA employs high-quality approximations for computationally expensive functions, such as GeLU and Softmax, and introduces secure versions of Embedding and LayerNorm. The framework offers a 2x speed improvement over the state-of-the-art MPC framework, MPCFORMER, while maintaining similar accuracy to plaintext models. PUMA can evaluate large models like LLaMA-7B in about 5 minutes to generate a single token.

Based on Deci’s AI efficiency foundation, DeciCoder leverages cutting-edge architecture and AutoNAC™, a proprietary Neural Architecture Search technology. Unlike manual, labor-intensive approaches that often fall short, AutoNAC™ automates the process of generating optimal architectures. This results in an impressive architecture optimized for NVIDIA’s A10 GPU, which not only boosts throughput but rivals the accuracy of SantaCoder.

Researchers from MetaAI and UC Berkley propose a foundational multilingual and multitask model that seamlessly translates and transcribes across speech and text. They call it “SeamlessM4T”. The M4T in the name stands for Massively Multilingual and Multimodal Machine Translation. It is an AI model with speech-to-text, speech-to-speech, text-to-speech, text-to-text translation, and automatic speech recognition for up to 100 languages.

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