multimodal

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Interpretierbare Merkmale

A team at ๐€๐ง๐ญ๐ก๐ซ๐จ๐ฉ๐ข๐œ, creator of the Claude models, published a paper about extracting ๐ข๐ง๐ญ๐ž๐ซ๐ฉ๐ซ๐ž๐ญ๐š๐›๐ฅ๐ž ๐Ÿ๐ž๐š๐ญ๐ฎ๐ซ๐ž๐ฌ from Claude 3 Sonnet. This is achieved by placing a sparse autoencoder halfway through the model and then training it. An autoencoder is a neural network that learns to encode input data, here a middle layer of Claude, into

Chameleon, ein gemischt-modales Early-Fusion-Grundlagenmodell

In a new paper, Meta announces ๐‚๐ก๐š๐ฆ๐ž๐ฅ๐ž๐จ๐ง, a ๐ฆ๐ข๐ฑ๐ž๐-๐ฆ๐จ๐๐š๐ฅ ๐ž๐š๐ซ๐ฅ๐ฒ-๐Ÿ๐ฎ๐ฌ๐ข๐จ๐ง foundation model. Contrary to earlier multimodal models, which model the different modalities (text, image, audio, etc.) separately, mixed-modal early-fusion foundation models like Chameleon are end-to-end models. They ingest all modalities from the start and project them into one representational space. That permits integrating information across

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