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Машинное обучение и физика: история Нобелевской премии 2024 года

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LLM360 Group представляет TxT360: качественный набор данных для предварительного обучения LLM с 15 триллионами токенов

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Podcastfy AI: Открытый пакет на Python для преобразования веб-контента, PDF и текста в увлекательные многоязычные аудио-разговоры с помощью GenAI

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SEAL: Двухкодировочная структура для улучшения иерархического обучения с подцелями на основе LLM

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Hex-LLM: Новый фреймворк для эффективного обслуживания открытых языковых моделей на Google Cloud TPUs

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Оценка возможностей планирования больших языковых моделей: осуществимость, оптимальность и универсальность модели o1 от OpenAI

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Стэнфордские ученые представили Tutor CoPilot: систему сотрудничества человека и ИИ для улучшения качества репетиторства.

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Оценка влияния переменной o1 на вероятностные предвзятости LLM: от прогнозирования к обоснованию

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LLaVA-Critic: Модель для оценки производительности на разнообразных мультимодальных задачах.

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Новый подход к улучшению эффективности моделей трансформера: внимание с выборочной активацией.

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Обучение предпочтений для улучшения работы больших языковых моделей.

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Модель Apple AI для оценки глубины снимков без использования метрик.

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Новые мультиязычные языковые модели для Европейского союза: EuroLLM-1.7B и EuroLLM-1.7B-Instruct.

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Машинное обучение с использованием графов и байесовских сетей для выбора примеров.

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Инструмент для обработки речи на основе PyTorch

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Новый набор данных для настройки математических инструкций от NVIDIA AI: 14 млн пар задач-решений.

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Как случайные дизайны влияют на современное машинное обучение: ключевой фактор раскрывается.

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Эффективное параллельное обучение минимальных LSTM и GRU.

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Новая технология NVIDIA для чатботов в компаниях: FACTS

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Модель Lotus для предсказания плотной геометрии на основе диффузии.

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