State-of-the-art convolutional networks easily memorize random labels and unstructured noise images, indicating that generalization in deep learning cannot be explained by traditional capacity or regularization arguments.
hub
In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning
18 Pith papers cite this work. Polarity classification is still indexing.
18
Pith papers citing it
abstract
We present experiments demonstrating that some other form of capacity control, different from network size, plays a central role in learning multilayer feed-forward networks. We argue, partially through analogy to matrix factorization, that this is an inductive bias that can help shed light on deep learning.
hub tools
citation-role summary
background 2
other 1
citation-polarity summary
representative citing papers
Data symmetries generically do not induce conserved quantities in NN training for analytic non-polynomial losses, but can for MSE with tensorizable networks.
New conditions for support vector proliferation (SVP) in RKHS for bounded orthonormal systems and sub-Gaussian features, yielding generalization bounds for kernel SVMs beyond prior restrictive assumptions.
Gradient matching empirically recovers implicit regularization effects such as l2 penalties from early stopping and dropout in neural networks.
BLADE converts influence-based bi-level data selection into a Hessian-free penalized objective with a dynamic reference model, proves first-order convergence, and reports better performance than prior methods on LLM training.
Establishes convergence guarantees for overparameterized 2-layer ReLU networks in flow matching, generalization bounds for the velocity-field objective, and Wasserstein guarantees for generated samples, using multi-task representation learning bounds.
Polynomial representations yield an effective-degree simplicity metric that predicts generalization across tasks and serves as a differentiable regularizer improving performance in classification and RL.
Linear generative models memorize at small data loads but converge continuously once samples scale linearly with dimension; this convergence is insensitive to sharp recovery of principal latent factors.
Deep sequence models develop geometric memory in embeddings that encodes novel global relationships, transforming l-fold composition tasks into 1-step navigation via a natural spectral bias connected to Node2Vec.
SPIN lets weak LLMs become strong by self-generating training data from previous model versions and training to prefer human-annotated responses over its own outputs, outperforming DPO even with extra GPT-4 data on benchmarks.
For orthogonal inputs, gradient flow on shallow ReLU nets with MSE loss at small init converges to zero loss, exhibits min-variation-norm bias, initial alignment, and saddle-to-saddle dynamics.
Evolving Parameter Isolation (EPI) periodically updates parameter isolation masks using online gradient signals during supervised fine-tuning to protect emerging task-critical parameters and reduce interference and forgetting.
Function-space definitions of sharpness and complexity jointly explain more generalization variance than parameter-space versions, yet leave unexplained cases that suggest the two-factor view is incomplete.
Transformer world models on Atari exhibit game-specific scaling regimes, but joint training on 26 environments produces consistent monotonic gains that improve downstream control policies to a median normalized score of 0.770.
Learning rate decay during SFT increases pretrained model sharpness, which exacerbates catastrophic forgetting and causes overtraining in LLMs.
Derives approximation rates and excess risk bounds for Frobenius norm-constrained DNNs learning sparse compositional functions on DAGs, applicable to multi-index models and binary trees while avoiding the curse of dimensionality.
Sparse MLPs trained via SET plus neuron pruning achieve competitive performance on 15 datasets while pruning ~50% of hidden neurons and keeping parameter count linear in neuron count.