REVIEW 7 cited by
From Lazy to Rich: Exact Learning Dynamics in Deep Linear Networks
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Biological and artificial neural networks develop internal representations that enable them to perform complex tasks. In artificial networks, the effectiveness of these models relies on their ability to build task specific representation, a process influenced by interactions among datasets, architectures, initialization strategies, and optimization algorithms. Prior studies highlight that different initializations can place networks in either a lazy regime, where representations remain static, or a rich/feature learning regime, where representations evolve dynamically. Here, we examine how initialization influences learning dynamics in deep linear neural networks, deriving exact solutions for lambda-balanced initializations-defined by the relative scale of weights across layers. These solutions capture the evolution of representations and the Neural Tangent Kernel across the spectrum from the rich to the lazy regimes. Our findings deepen the theoretical understanding of the impact of weight initialization on learning regimes, with implications for continual learning, reversal learning, and transfer learning, relevant to both neuroscience and practical applications.
Forward citations
Cited by 7 Pith papers
-
How are linear representations learned? Exact solutions to the dynamics of abstraction
Exact solutions show abstraction is set by input/target geometry, rises with depth, peaks under small init, and is attenuated by nonlinearities—improving LLM probes via GELU ablation.
-
Spectral Dynamics in Deep Networks: Feature Learning, Outlier Escape, and Learning Rate Transfer
A two-level DMFT predicts width-consistent outlier escape and hyperparameter transfer under μP in deep networks, with bulk restructuring dominating for tasks with many outputs.
-
Signal-to-Noise Ratio and Sample Size Govern Representational Alignment in Neural Networks
Representational alignment varies monotonically with SNR and non-monotonically with sample size (minimized near interpolation threshold) across linear and nonlinear networks, and is decoupled from generalization error.
-
Spectral Dynamics in Deep Networks: Feature Learning, Outlier Escape, and Learning Rate Transfer
A two-level DMFT tracks bulk and outlier spectral dynamics in wide networks, predicting width-consistent outlier growth and hyperparameter transfer under muP scaling for deep linear nets while noting bulk restructurin...
-
A Theory of How Pretraining Shapes Inductive Bias in Fine-Tuning
In diagonal linear networks, fine-tuning generalization is governed by a tunable per-dimension penalty whose sparsity and pretraining dependence define four regimes and a trade-off between feature reuse and new-featur...
-
You Had One Job: Per-Task Quantization Using LLMs' Hidden Representations
TAQ estimates per-layer importance from hidden representations and output sensitivity on task calibration data to allocate mixed precision in a training-free PTQ setting, outperforming task-agnostic baselines on accur...
-
You Had One Job: Per-Task Quantization Using LLMs' Hidden Representations
A per-layer, per-task bit-allocation rule based on hidden-activation entropy and variance reportedly matches full-precision TriviaQA accuracy within about one point — but the paper's tables and abstract are internally...
Discussion (0). Sign in to comment.