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📚TheoryIntermediate

Sequence-to-Sequence with Attention

Sequence-to-sequence with attention lets a decoder focus on the most relevant parts of the input at each output step, rather than compressing everything into a single vector.

#sequence-to-sequence#attention#encoder-decoder+12
📚TheoryIntermediate

Key-Value Memory Systems

Key-Value memory systems store information as pairs where keys are used to look up values by similarity rather than exact match.

#key-value memory
Intermediate
Advanced
Filtering by:
#scaled dot-product
#attention
#scaled dot-product
+12
📚TheoryIntermediate

Self-Attention as Graph Neural Network

Self-attention can be viewed as message passing on a fully connected graph where each token (node) sends a weighted message to every other token.

#self-attention#graph neural network#message passing+11
📚TheoryIntermediate

Positional Encoding Theory

Transformers are permutation-invariant by default, so they need positional encodings to understand word order in sequences.

#positional encoding#sinusoidal encoding#transformer+11
📚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