Unverified50% confidenceFactExact time
The first model uses the original MNIST dataset, with an architecture of 784→20→20→10, MSE as the loss function, plain gradient descent as the optimizer, no activation function in the output layer, and a test accuracy of about 92% after 2000 epochs of training.
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11/2/2026
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UnverifiedThe second model's architecture is expanded to 784→40→40→10, MSE is replaced with cross-entropy, softmax is added to the output layer, the optimizer remains plain gradient descent, and the accuracy is about 96% after 2000 epochs.76% similarUnverifiedThe handwritten MLP model can achieve approximately 98.5% accuracy on the full MNIST training set.73% similarUnverifiedThe third model uses the Arabic Digits MNIST dataset, with an architecture of 784→20→20→15→10, cross-entropy loss plus plain gradient descent, an accuracy of about 96% after 2000 epochs, and uses JAX automatic differentiation to compute gradients.72% similarUnverifiedFastEmbed-rs所使用的量化模型在MTEB基准测试中相比全精度模型的性能下降通常控制在1-2%以内71% similarUnverifiedThe same scaffold raised the weak model's execution quality from 0.88 to 2.0 (across four scoring dimensions), yet pushed the strong model toward overcautious shutdown (all four dimensions dropped).71% similar
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