LocalProp: Neuro-Localized Memory-Efficient Backpropagation

arXiv:2609.32517 · cs.AI, cs.CV, cs.LG, math.OC · Submitted 2026-09-26 · Read on arXiv

cs.AI, cs.CV, cs.LG, math.OC

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Pruning — 82× in this paper · explained in 4 episodes
• Fine-Tuning — 36× in this paper · explained in 5 episodes
• Transformer — 31× in this paper · explained in 2 episodes
• Accuracy — 20× in this paper · explained in 1 episode
• Adaptation — 6× in this paper · explained in 1 episode
• Distillation — 6× in this paper · explained in 2 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Local Optimization — 5× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Embeddings — 3× in this paper · explained in 2 episodes
• Self-Supervised Learning — 3× in this paper · explained in 8 episodes
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Latent Representation — 2× in this paper · explained in 2 episodes
• Latent Representations — 2× in this paper · explained in 3 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Core — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Linear probe — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Self-Distillation — 1× in this paper · explained in 7 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• Training Deep Nets with Sublinear Memory Cost
• The Forward-Forward Algorithm: Some Preliminary Investigations
• Distilling the Knowledge in a Neural Network
• Hyperspherical Forward-Forward with Prototypical Representations

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