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📚TheoryAdvanced

Transformer Expressiveness

Transformer expressiveness studies what kinds of sequence-to-sequence mappings a Transformer can represent or approximate.

#transformer expressiveness#universal approximation#self-attention+12
📚TheoryIntermediate

Depth vs Width Tradeoffs

Depth adds compositional power: stacking layers lets neural networks represent functions with many repeated patterns using far fewer neurons than a single wide layer.

#depth vs width
Advanced
Filtering by:
#universal approximation
#relu
#piecewise linear
+12
📚TheoryIntermediate

Attention Mechanism Theory

Attention computes a weighted sum of values V where the weights come from how similar queries Q are to keys K.

#attention#self-attention#multi-head attention+12
📚TheoryAdvanced

Neural Network Expressivity

Neural network expressivity studies what kinds of functions different network architectures can represent and how efficiently they can do so.

#neural network expressivity#depth separation#relu linear regions+12