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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

Temporal Convolutions

Temporal (causal) convolution computes each output at time t using only the current and past inputs, ensuring no future information leakage.

#temporal convolution
Advanced
Group:
Sequence & Temporal Models
#causal convolution
#fir filter
+12
📚TheoryAdvanced

Mamba & Selective State Spaces

Mamba uses a state-space model whose parameters are selected (gated) by the current input token, letting the model adapt its memory dynamics at each step.

#mamba#selective state space#ssm+12
📚TheoryIntermediate

LSTM & Gating Mechanisms

Long Short-Term Memory (LSTM) networks use gates (forget, input, and output) to control what information to erase, write, and reveal at each time step.

#lstm#forget gate#input gate+11
📚TheoryIntermediate

Recurrent Neural Network Theory

A Recurrent Neural Network (RNN) processes sequences by carrying a hidden state that is updated at every time step using h_t = f(W_h h_{t-1} + W_x x_t + b).

#recurrent neural network#rnn#backpropagation through time+12