Research
Derivative-Free Optimizer Outperforms Adam on MNIST Classification
A gradient-free optimization method called MDP achieved 93.7% validation accuracy on MNIST, surpassing Adam's 91.8% using the same 25,450-parameter network architecture.
1 min read
Sourcer/machinelearning
Researchers demonstrated that derivative-free optimization can outperform gradient-based methods on neural network training. The experiment applied MDP, a gradient-free optimizer, to train a 784-32-10 neural network for MNIST image classification without using backpropagation or any gradient informa...
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Method & sources
- Source type
- Primary publication (lab/vendor blog) — our analysis + implication
- Source link
- r/machinelearning
- Published
- UTC
- Byline
- By the gotcontext.ai team (editorial standards)
- Correction?
- corrections@gotcontext.ai