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Traditional methods for training vision-language models (VLMs) often require the centralized aggregation of vast datasets, which raises concerns regarding privacy and scalability. Federated learning offers a solution by allowing models to be trained across a distributed network of devices while keeping data locally but adapting VLMs to this framework presents unique challenges. To address these…
Reinforcement learning (RL) is a type of learning approach where an agent interacts with an environment to collect experiences and aims to maximize the reward received from the environment. This usually involves a looping process of experience collecting and enhancement, and due to the requirement of policy rollouts, it is called online RL. Both on-policy…
The 2024 Zhongguancun Forum in Beijing saw the introduction of Vidu, an advanced AI model that can generate 16-second 1080p video clips with a simple prompt. Developed by ShengShu-AI and Tsinghua University, Vidu is set to compete with OpenAI’s Sora, marking a significant milestone for China’s generative AI capabilities and ambition to lead in emerging technologies. Vidu’s primary technology is the Universal Vision…
Large language models (LLMs) are the backbone of numerous computational platforms, driving innovations that impact a broad spectrum of technological applications. These models are pivotal in processing and interpreting vast amounts of data, yet they are often hindered by high operational costs and inefficiencies related to system tool utilization. Optimizing LLM performance without prohibitive computational…
Scientific Machine Learning (SciML) is an innovative field at the crossroads of ML, data science, and computational modeling. This emerging discipline utilizes powerful algorithms to propel discoveries across various scientific domains, including biology, physics, and environmental sciences. Image Source Expanding the Horizons of Research Accelerated Discovery and Innovation SciML allows for the quick processing and…