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Знакомьтесь с Verba 1.0: запускайте передовую технологию RAG локально с интеграцией Ollama и открытыми моделями исходного кода

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Создание базовых моделей машинного обучения для транслитерированных данных без обучения: фреймворк TRANSMI

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Набор данных и бенчмарк CinePile для аутентичного понимания видеоформата.

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Обучение для планирования в сетях: методика авторегрессии

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Новая статья от Huawei об исследовании языковых моделей на основе трансформеров.

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Новые методы машинного обучения для создания дифференциально-защищенных синтетических данных, описанные Google AI

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Проектирование архитектуры для автономных роботов

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Оценка производительности многофункциональных моделей на разном количестве обучающих образцов.

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Сравнительное исследование LoRA и полного донастройки в больших языковых моделях.

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Машинное обучение упрощает моделирование потери сигнала с помощью упрощенных функций

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Новый метод для улучшения моделирования последовательностей с использованием техники БПФ

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Улучшение классификации графов с помощью внимания к ребрам и узлам и многосетевых графовых нейронных сетей (GNN)

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Новая модель Yi-1.5-34B: улучшенная версия с большим корпусом и тонкой настройкой.

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Обзор и сравнение GPT-4 и GPT-4o: основные обновления

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Инструмент для визуализации графиков: помощь в понимании, отладке и оптимизации моделей машинного обучения

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Исследование отображения данных как проблемы поиска

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Поиск единой модели реальности: задача искусственного интеллекта

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Новая семья моделей Meta AI Chameleon устанавливает новый уровень для мультимодельного машинного обучения.

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Ученые из Cerebras и Neural Magic представляют Sparse Llama: первый продукт LLM на основе Llama с 70% разреженностью

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Исследование Google DeepMind об исследовании разрыва в производительности между онлайн и оффлайн методами для выравнивания искусственного интеллекта.

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