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Recurrent Models
Recurrent models are a class of neural networks designed to process sequential data. They operate by maintaining a hidden state, denoted as 'h', which captures information from previous elements in the sequence. At each time step 'i', the model updates its state to 'h_i' by applying a function 'f' to the previous state 'h_{i-1}' and the current input 'input_i'. This relationship is formally expressed by the recurrence relation: This mechanism allows the model to have a 'memory' of past events, making it suitable for tasks involving sequences like natural language processing.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Recurrent Memory Models as a Basis for Self-Attention Alternatives
Recursive Formula for Memory as a Cumulative Average
A recurrent model with an internal state
his processing a sequence of inputs. The state is updated at each step according to the ruleh_i = f(h_{i-1}, input_i), whereh_{i-1}is the state from the previous step andinput_iis the current input. When the model processes the third input in a sequence, what information does the termh_2(the state after the second input) represent in the computation for the new stateh_3?Analysis of Sequential Information Processing
A neural network processes a sequence of inputs by updating a hidden state
hat each stepiusing the formula:h_i = f(h_{i-1}, input_i). Which component in this formula is directly responsible for carrying forward a compressed summary of the entire sequence processed up to the previous step (i-1)?Recurrent Computation of and in Linear Attention
Real-Time Applications of Recurrent Models
Resurgence of Recurrent Models in Large Language Models
Sequential Token Processing in Recurrent Models
Comparison of Efficient LLM Architectures