self-organising-transformer

# Self-Organising Transformer Architecture Architecture that determines its own structure during training, combining Deep Dual Competitive Learning (DDCL) with incremental representation. ## Core Innovation Traditional transformers have fixed architecture. DDCL-INCRT learns the optimal structure: - **DDCL**: Replaces feedforward blocks with learned prototype vectors - **INCRT**: Incrementally builds representation hierarchy - **Self-determination**: Network discovers its own optimal structure ## Key Components ### 1. Deep Dual Competitive Learning (DDCL) ```python # Instead of fixed feedforward: # x -> Linear -> ReLU -> Linear -> output # DDCL uses learned prototypes: # x -> match_prototypes(x) -> select_best -> combine -> output class DDCLBlock: prototypes: Tensor # Learned dictionary of patterns def forward(x): # Competitive learning: match input to prototypes similarities = cosine_similarity(x, self.prototypes) best_matches = top_k(similarities) return weighted_combination(best_matches) ``` ### 2. Hierarchical Prototype Structure Multi-level prototypes capture patterns at different scales: - **Level 1**: Basic features (edges, textures) - **Level 2**: Combinations (shapes, parts) - **Level 3**: High-level concepts ### 3. Self-Organising Mechanism Architecture evolves during training: - Add prototypes when novelty detected - Merge similar prototypes for efficiency - Prune unused prototypes - Adjust hierarchy depth ## Implementation Patterns ### Pattern 1: Replace Transformer FFN ```python class SelfOrganisingTransformer(nn.Module): def __init__(self, d_model, n_prototypes): self.ddcl = DDCLBlock(d_model, n_prototypes) def forward(self, x): # Attention remains standard attn_out = self.attention(x) # FFN replaced by DDCL return self.ddcl(attn_out) ``` ### Pattern 2: Incremental Hierarchy ```python # Start with minimal structure # Grow during training based on data complexity def grow_hierarchy(model, data): novelty_score = compute_novelty(data, model.prototypes) if novelty_score > threshold: model.add_prototype_level() ``` ### Pattern 3: Prototype Pruning ```python # Remove unused prototypes periodically def prune_prototypes(model): usage_counts = model.get_prototype_usage() unused = find_unused_prototypes(usage_counts) model.remove_prototypes(unused) ``` ## Benefits | Aspect | Fixed Architecture | Self-Organising | |--------|-------------------|-----------------| | Efficiency | Over-parameterized | Optimal for data | | Adaptation | Manual tuning | Automatic | | Interpretability | Hidden layers | Visible prototypes | | Memory | Fixed size | Dynamic allocation | ## Activation Keywords - self-organising transformer - DDCL - DDCL-INCRT - adaptive architecture - prototype learning - 自组织架构 - competitive learning - dynamic neural network ## Use Cases 1. **Adaptive models**: Networks that adjust to task complexity 2. **Interpretable AI**: Prototypes are human-readable patterns 3. **Efficient inference**: Only activate needed prototypes 4. **Incremental learning**: Add new knowledge without retraining ## Related Skills - **transformer-architecture**: Standard transformer patterns - **competitive-learning**: Learning with competition - **prototype-networks**: Prototype-based models ## Resources - arxiv.org/abs/2604.01880 - DDCL-INCRT paper - Competitive learning literature - Prototype networks research ## Notes - Requires careful initialization of prototypes - Trade-off: flexibility vs training stability - Best for domains with clear prototype patterns