Unverified50% confidenceFactExact time
Forward pass refers to the one-way computation process of a neural network from input to output, not involving backpropagation or gradient updates.
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11/1/2026
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UnverifiedA forward pass is the process of a neural network computing once completely from the input layer to the output layer; an autoregressive language model needs N forward passes to generate N tokens, with latency growing linearly with output length; a classifier-style model needs only one forward pass.76% similarUnverifiedNeuron activation is the output value after the input data undergoes a weighted sum and a nonlinear function.73% similarUnverifiedImplicit neural networks here refer to using a small neural network to directly parameterize the warping function φ, with normalized time coordinates as input and the corresponding alignment positions as output.73% similarUnverifiedDuring the inference phase, only forward propagation needs to be executed; gradient computation and backpropagation are not required.71% similarUnverifiedBackpropagation requires the network to have a clear layered structure and differentiable activation functions, which is difficult to map biologically onto the real learning mechanisms of neurons.70% similar
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