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Как создаются признаки в глубоком обучении: изучение представления признаков

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Рост возможностей интеллектуального поиска и генерации в искусственном интеллекте (RAG)

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Глубокое обучение в медицине: проблемы, применение и перспективы

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Исследование роли сходства примеров в улучшении рассуждений больших языковых моделей.

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Исследование из Корнелла разъясняет сложности причинно-следственных связей в оценке вероятности вмешательства

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Модель встраивания NVIDIA доминирует в бенчмарках MTEB

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Простой и понятный заголовок: “Легкая кодовая база для эффективного дообучения моделей Mistral”

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Развитие серии GPT: технические особенности и показатели производительности от GPT-1 до GPT-4o

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Преодоление проблемы инверсии градиента в федеративном обучении: алгоритм DAGER для точного восстановления текста

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Symflower запускает DevQualityEval: новый стандарт для улучшения качества кода в больших языковых моделях.

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Как создавать тексты с использованием знаний: сочетание поиска и генерации

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Роль ограждений в создании готовых к производству решений с использованием искусственного интеллекта

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Исследование MIT предлагает значительное усовершенствование гипотезы одномерного линейного представления.

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Улучшение планирования агента: параметрический подход искусственного интеллекта к знанию мира

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Обзор исследований эффективных мультимодальных больших языковых моделей

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Новая статья от ByteDance Research: улучшение выбора данных для машинного перевода с помощью G-DIG

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AI-фреймворк OLAPH для улучшения достоверности через автоматические оценки

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Улучшение запоминания и внимания: новые возможности SirLLM

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Переформулированный заголовок: “Переход от Llama 2 к Llama 3: новый этап в открытых языковых моделях Meta”

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Фреймворк Cognita: создание модульных приложений RAG с открытым исходным кодом

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