Abstract: Stochastic optimization algorithms are widely used to solve large-scale machine learning problems. However, their theoretical analysis necessitates access to unbiased estimates of the true ...
Abstract: Collision-free robot motion planning is crucial in robotic applications. Traditional sampling-based methods struggle with kinematic/dynamic constraints and intermediate process constraints, ...
Kimi-K2-Mini is an experimental compressed version of the 1.07T parameter Kimi-K2 model, targeting ~32.5B parameters for more accessible deployment. This project explores several optimization ...
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