DEER
Deep Equilibrium Recurrent; a method that reformulates RNN forward propagation as a fixed-point equation system to be solved in parallel, drawing on implicit deep learning ideas. By applying Newton-type iteration, it reduces computational complexity from O[T] to O[(log T)²].
Timeline (last 90 days)
DEER solves the computation of the entire sequence as a large system of equations in one shot through Newton-type fixed-point iteration, theoretically reducing complexity from O[T] to O[(log T)²].
DEER (Deep Equilibrium Recurrent)'s fixed-point iteration draws on ideas from implicit deep learning, treating the unrolling of an RNN along the time dimension as the implicit equation h = F(h, x).
In chaotic systems, DEER's fixed-point iteration is difficult to converge, and runtime degrades to O[T log T], significantly diminishing the parallelization advantage.
The research team introduced Generalized Teacher Forcing (GTF) to stabilize DEER, making its iterative process controllable and convergent again.
All Facts (4)
DEER solves the computation of the entire sequence as a large system of equations in one shot through Newton-type fixed-point iteration, theoretically reducing complexity from O[T] to O[(log T)²].
50%UnverifiedDEER (Deep Equilibrium Recurrent)'s fixed-point iteration draws on ideas from implicit deep learning, treating the unrolling of an RNN along the time dimension as the implicit equation h = F(h, x).
50%UnverifiedIn chaotic systems, DEER's fixed-point iteration is difficult to converge, and runtime degrades to O[T log T], significantly diminishing the parallelization advantage.
50%UnverifiedThe research team introduced Generalized Teacher Forcing (GTF) to stabilize DEER, making its iterative process controllable and convergent again.
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