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Применение машинного обучения в развитии мембранных технологий.

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Microsoft выпустила модель GRIN MoE для эффективного и масштабируемого глубокого обучения.

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Набор данных FC-AMF-OCR: 9,3 миллиона изображений финансовых документов с полными аннотациями OCR.

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Новый метод улучшения точности ИИ в сложных математических и программных задачах.

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Модель PPlug: Простая и гибкая генерация персонализированного текста

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Улучшение поиска: Искусственный интеллект для снижения ошибок на 67%

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Новый подход к символьной регрессии с использованием больших языковых моделей.

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Эффективная платформа для запуска нейронных сетей на различном оборудовании.

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Инновационный набор инструментов AI для оптимизации операций LLM в различных областях.

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Исследование квантования моделей с разным количеством параметров

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Поиск с расширенными возможностями для интеграции текстовых и визуальных запросов.

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Новый прорыв в разработке ПО: AI достигает 91% точности в воссоздании 90 000 строк кода.

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ByteDance представила иерархическую модель большого языка для улучшения рекомендаций.

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Новые открытые модели языка для исследований искусственного интеллекта.

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NVIDIA представила NVLM 1.0: семейство мультимодальных больших языковых моделей с улучшенными возможностями обработки текста и изображений.

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Новая модель SFR-RAG: улучшение точности и эффективности в генерации контента.

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Можно ли оптимизировать большие языковые модели быстрее, чем Adam? Новая разработка SOAP от Гарварда обещает улучшить и стабилизировать обучение глубоких нейронных сетей.

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Прогнозирование хаотических систем с использованием физически обоснованных нейрооператоров: устранение ограничений традиционных моделей.

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Структура мышления в искусственном интеллекте: моделирование итеративного рассуждения в больших языковых моделях.

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Создание цепочек рассуждений с помощью Llama-3.1 70b на Groq

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