neuron-explorer
Steering-first neuron explorer for Qwen3.5-4B: push every MLP neuron, keep every rollout, browse the results
翻译准备中
Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
翻译准备中
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正在加载完整 README。 以下为已保存的摘录。
DiffEq(For)Lux.jl (aka DiffEqFlux.jl) fuses the world of differential equations with machine learning
by helping users put diffeq solvers into neural networks. This package utilizes
DifferentialEquations.jl, and Lux.jl as its building blocks to support research in
Scientific Machine Learning, specifically neural differential equations to add physical information into traditional machine learning.
We maintain backwards compatibility with Flux.jl via FromFluxAdaptor()查看完整 README ↗目前无法加载完整 README。请通过原文链接阅读。 以下为已保存的摘录。
DiffEq(For)Lux.jl (aka DiffEqFlux.jl) fuses the world of differential equations with machine learning
by helping users put diffeq solvers into neural networks. This package utilizes
DifferentialEquations.jl, and Lux.jl as its building blocks to support research in
Scientific Machine Learning, specifically neural differential equations to add physical information into traditional machine learning.
We maintain backwards compatibility with Flux.jl via FromFluxAdaptor()查看完整 README ↗