3 个仓库
Computation of gradients from physical simulation results to be used in machine learning frameworks.
Distinct from Gradient Computation: Focuses on propagating gradients from simulation adjoint kernels back to ML models, rather than general automatic differentiation.
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jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti
NVIDIA generates reverse-mode adjoint kernels that propagate gradients from simulation results back into machine learning frameworks.
torchdiffeq 是一个 PyTorch ODE(常微分方程)求解器库,专为解决初值问题和构建神经 ODE 框架而设计。它提供了一个可微分的 ODE 积分器,允许深度学习模型通过随时间积分动力学函数来模拟连续深度。 该库具有用于内存高效反向传播的伴随法梯度计算器。通过在时间上反向求解增广伴随系统,它可以在不存储每个中间求解器状态的情况下计算参数梯度。 该项目涵盖了具有自适应和固定步长求解器的数值积分,并结合了误差控制和多项式状态插值。它还支持可微分事件管理,以便在特定标量函数触发时终止求解器,并沿事件时间传播梯度。 该框架常用于连续归一化流以及通过学习连续时间常微分方程进行概率分布转换等应用。
Computes loss function gradients by solving an augmented adjoint system backwards in time to optimize memory.
Backpropagates gradients through physics and rendering simulations to enable gradient-based optimization.