Beyond Backpropagation: Monte Carlo Method Can Train Deep Neural Networks

arXiv:2607.08406 · cs.LG, stat.ML · Submitted 2026-07-09 · Read on arXiv

cs.LG, stat.ML

Submitted: 2026-07-09

Updated: 2026-07-09

Comments: 22 pages, 5 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 37× in this paper · explained in 1 episode
• Pruning — 24× in this paper · explained in 4 episodes
• Transformer — 21× in this paper · explained in 2 episodes
• Loss Function — 11× in this paper · explained in 2 episodes
• Transformer Architecture — 8× in this paper · explained in 4 episodes
• Generalization — 6× in this paper · explained in 14 episodes
• Redundancy — 5× in this paper · explained in 2 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Spiking neural networks — 4× in this paper · explained in 2 episodes
• Sparsity — 3× in this paper · explained in 5 episodes
• Adam — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Grokking — 2× in this paper · explained in 2 episodes
• Residual Connections — 2× in this paper · explained in 3 episodes
• Weight Decay — 2× in this paper · explained in 2 episodes
• Associative Memory — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Computational Efficiency — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 10 episodes
• Diffusion Models — 1× in this paper · explained in 9 episodes
• Feed-Forward Network — 1× in this paper
• Limit Cycles — 1× in this paper · explained in 3 episodes
• Model Compression — 1× in this paper · explained in 3 episodes
• Monte Carlo Dropout — 1× in this paper · explained in 3 episodes
• Monte Carlo Sampling — 1× in this paper · explained in 2 episodes
• Overfitting — 1× in this paper · explained in 2 episodes
• Perplexity — 1× in this paper · explained in 3 episodes
• Phase Transition — 1× in this paper · explained in 3 episodes
• Phase Transitions — 1× in this paper · explained in 2 episodes
• Physical Constraints — 1× in this paper · explained in 7 episodes
• Predictive Coding — 1× in this paper · explained in 3 episodes
• Quantization — 1× in this paper · explained in 15 episodes
• Quantum Circuits — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 3 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Time Series Forecasting — 1× in this paper · explained in 4 episodes

Sources

• The Forward-Forward Algorithm: Some Preliminary Investigations
• A Global Algorithm for Training Multilayer Neural Networks
• A General Theory for Training Learning Machine

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