Unverified50% confidenceFactExact time
FlowGenesis proposes "Representation Flux" as a local statistic to capture changes in a neuron's average activation on mini-batch data.
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50%
Confidence
Long-term
Relevance
10/27/2026
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Related Claims
UnverifiedExperiments on image and text classification tasks show that even when initial network sizes differ, after FlowGenesis adjustment the range of final neuron counts on the same dataset tends to converge, while predictive performance remains comparable.71% similarUnverifiedThe core idea of Liquid Neural Networks is to allow the time constants of neurons to change dynamically with input data, giving the network adaptive adjustment capabilities when processing temporal signals.67% similarUnverifiedA 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.66% similarUnverifiedFlowGenesis is extended to continual learning, where under task-incremental learning settings, representation-guided structural adaptation reduces the number of neurons and parameters allocated to subsequent tasks while maintaining accuracy comparable to fixed-width progressive neural networks.64% similarUnverifiedDiffusion models and flow matching absorb the sampling cost once during training by training a neural network, requiring only a forward pass at inference time.63% similar
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