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Modality-agnostic decoders leverage modality-invariant representations in human subjects' brain activity to predict stimuli irrespective of their modality (image, text, mental imagery).

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LLMは「蒸留（distillation；ディスティレーション）」と呼ばれるプロセスをつうじて、ほかのモデルを訓練するためのデータセットを生成できる。このプロセスでは、「生徒」モデルが「教師」モデルの出力を模倣するように学習する。この過程は、LLMの低コスト版を作成する目的で利用されることがあるが、教師モデルのどの特性が生徒モデルに伝達されるかは不明である。

Discover top crypto and stock platforms in our 2026 beginner's guide to automate your trading with the best AI tools today.

Quant Developer – Private Equity (Data & Analytics) ...