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Обучение моделей на изображениях для улучшения понимания текста

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Google выпустил набор данных FRAMES для тестирования приложений Retrieval-Augmented Generation (RAG)

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Искусственный интеллект, использующий небольшое количество примеров для создания качественных текстовых вложений

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NotebookLM добавляет интеграцию с аудио и YouTube для улучшения обмена аудио-презентациями.

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10 способов снижения затрат на вывод больших языковых моделей

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Простой и мощный подход к непрерывному обучению без образцов.

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Новая технология для прогнозирования больших объемов данных.

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Фреймворк BioMed-VITAL для настройки биомедицинского визуального обучения.

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Новый подход к улучшению эффективности LLM в многоязычных средах.

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Проблема моделирования данных: аналитические выводы и стратегии устранения.

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Методы разделения текста для улучшения генерации (RAG): Полное руководство по оптимизации сегментации текста.

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Ученые из Китая представили INT-FlashAttention: архитектуру квантования INT8, совместимую с FlashAttention, улучшающую скорость вывода FlashAttention на графических процессорах Ampere.

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Метод персонализации для надежных моделей искусственного интеллекта в здравоохранении.

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Использование качественных данных для эффективной разработки языковой модели белков.

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Гибкий и мощный фреймворк AI для создания мультиагентных систем AI.

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Решение FusionANNS: улучшенная производительность, масштабируемость и экономия совместной обработки CPU/GPU.

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Эффективный поиск документов с помощью векторного поиска.

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Ученые представили механизм самокоррекции для улучшения безопасности и надежности больших языковых моделей.

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Применение теории вейвлетов для ускорения обучения моделей языка различных типов данных.

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Почему Adam лучше, чем SGD: объяснение на основе гессиана.

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