Tsinghua University Team Proposes O²-Recon Method Achieving Breakthrough in 3D Reconstruction

Tsinghua University Launches New AI Minor Degree to Promote Interdisciplinary Innovation

DeepSeek and Tsinghua University Collaborate on Self-Optimizing AI Model

Amidst the growing prevalence of artificial intelligence, the collaboration between DeepSeek and Tsinghua University has garnered significant industry attention. DeepSeek, a Chinese startup, is renowned for its breakthroughs in low-cost inference models. This collaboration aims to further reduce the training costs of AI models, thereby enhancing operational efficiency. DeepSeek recently launched a new low-cost inference model that has generated considerable market excitement. To further optimize this model, DeepSeek's research team...

DeepSeek and Tsinghua University Joint Research: Innovative Reward Model Inference Method Improves Scalability

Researchers from DeepSeek and Tsinghua University recently published a new paper exploring scaling methods for reward model inference, seemingly advancing DeepSeek R2. Reinforcement learning is widely used in the large-scale post-training phase of large language models, but faces the challenge of obtaining accurate reward signals for these models. The researchers found that using pointwise generative reward modeling (GRM) improves model adaptability and scalability during inference. To this end, they propose Self-Principle Calibration Tuning (SPCT) learning.

Tsinghua University Open-Sources Video-T1: AI Transforms Videos into High-Definition Masterpieces Without Retraining

Tsinghua University and Ant Group Achieve Breakthrough: BodyGen Boosts Robot Performance by 60%

Revolutionary Fast3R Technology: One-Click 3D Reconstruction of Thousands of Images at Incredible Speed!

Tsinghua University Launches AutoDroid-V2: A New Breakthrough in AI Automation Control on Mobile Devices

Tsinghua University Collaborates with Tencent to Present ColorFlow: Automatically Coloring Black and White Comics While Maintaining Character Consistency

With the development of digital art, automated image processing technology has received increasing attention. Recently, a research team from Tsinghua University and Tencent's ARC Lab proposed a novel image sequence coloring model called ColorFlow. This model aims to address the challenge of maintaining consistency in character and object identity while coloring black and white image sequences, fulfilling the practical needs of industries such as comics and animation. ColorFlow is a three-stage diffusion base framework that leverages contextual information by utilizing a reference image pool for black and white.

Kimi and Tsinghua University Launch Open Source Model Inference Architecture Mooncake

Kimi Technology Co., Ltd. and Tsinghua University's MADSys laboratory have jointly released an open-source project called Mooncake, aimed at collaboratively building a large model inference architecture centered around KVCache. In June 2024, both parties released the design plan for the Mooncake inference system based on Kimi, which significantly enhances inference throughput through PD separation and a storage-computation architecture, garnering widespread attention in the industry.

Kimi Collaborates with Tsinghua University to Launch Mooncake Open Source Model Inference Architecture to Enhance AI Inference Efficiency

In an era of rapid development in artificial intelligence, the intelligence level of large models continues to improve, but the challenges of inference system efficiency are becoming increasingly apparent. Addressing high inference loads, reducing inference costs, and shortening response times has become an important issue faced by the industry. Kimi has collaborated with the MADSys laboratory at Tsinghua University to launch the KVCache-based Mooncake inference system design plan, which will be officially released in June 2024. The Mooncake inference system enhances efficiency through its innovative approach.