On the public citation graph
Rigorous learning curve bounds from statistical mechanics
0 signed · 49 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-1-4612-5254-2openalex-snapshot
Convergence of Stochastic Processes
inferredcites →https://doi.org/10.1007/bf00994005openalex-snapshot
Implementing Valiant's Learnability Theory Using Random Sets
inferredcites →https://doi.org/10.1016/0304-3975(91)90026-xopenalex-snapshot
Learnability with respect to fixed distributions
inferredcites →https://doi.org/10.1016/0890-5401(89)90002-3openalex-snapshot
A general lower bound on the number of examples needed for learning
inferredcites →https://doi.org/10.1016/0890-5401(92)90010-dopenalex-snapshot
Decision theoretic generalizations of the PAC model for neural net and other learning applications
inferredcites →https://doi.org/10.1016/b978-0-08-094829-4.50020-9openalex-snapshot
A Statistical Approach to Learning and Generalization in Layered Neural Networks
inferredcites →https://doi.org/10.1088/0305-4470/22/12/004openalex-snapshot
Three unfinished works on the optimal storage capacity of networks
inferredcites →https://doi.org/10.1088/0305-4470/26/23/031openalex-snapshot
Statistical mechanics calculation of Vapnik-Chervonenkis bounds for perceptrons
inferredcites →https://doi.org/10.1103/physreva.41.7097openalex-snapshot
First-order transition to perfect generalization in a neural network with binary synapses
inferredcites →https://doi.org/10.1103/physreva.45.6056openalex-snapshot
Statistical mechanics of learning from examples
inferredcites →https://doi.org/10.1103/physrevlett.65.1683openalex-snapshot
Learning from examples in large neural networks
inferredcites →https://doi.org/10.1103/physrevlett.71.1772openalex-snapshot
Systems that can learn from examples: Replica calculation of uniform convergence bounds for perceptrons
inferredcites →https://doi.org/10.1103/revmodphys.65.499openalex-snapshot
The statistical mechanics of learning a rule
inferredcites →https://doi.org/10.1137/1116025openalex-snapshot
On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
inferredcites →https://doi.org/10.1145/168304.168385openalex-snapshot
General bounds on the number of examples needed for learning probabilistic concepts
inferredcites →https://doi.org/10.1162/neco.1991.3.2.258openalex-snapshot
Recognizing Hand-Printed Letters and Digits Using Backpropagation Learning
inferredcites →https://doi.org/10.1162/neco.1991.3.3.386openalex-snapshot
The Transition to Perfect Generalization in Perceptrons
inferredcites →https://doi.org/10.1162/neco.1992.4.2.249openalex-snapshot
How Tight Are the Vapnik-Chervonenkis Bounds?
inferredcites →https://doi.org/10.1162/neco.1992.4.4.605openalex-snapshot
Four Types of Learning Curves
inferredcites →https://doi.org/10.1162/neco.1992.4.6.854openalex-snapshot
Tight Bounds on Transition to Perfect Generalization in Perceptrons
inferredcites →https://doi.org/10.1162/neco.1994.6.5.851openalex-snapshot
Measuring the VC-Dimension of a Learning Machine
inferredcites →https://doi.org/10.1214/aop/1176995384openalex-snapshot
Central Limit Theorems for Empirical Measures
inferredcites →https://doi.org/10.2307/2988246openalex-snapshot
Estimation of Dependences Based on Empirical Data.
inferredcites →https://doi.org/10.5555/114836.114847openalex-snapshot
Learning curves in large neural networks
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/9781118445112.stat08155openalex-snapshot
Generalization Error for Decision Problems
inferred← citeshttps://doi.org/10.1006/jtbi.2000.2240openalex-snapshot
The Evolutionary Dynamics of Grammar Acquisition
inferred← citeshttps://doi.org/10.1007/3-540-40992-0_15openalex-snapshot
Average-Case Analysis of Classification Algorithms for Boolean Functions and Decision Trees
inferred← citeshttps://doi.org/10.1007/3-540-45681-3_33openalex-snapshot
A Scalable Constant-Memory Sampling Algorithm for Pattern Discovery in Large Databases
inferred← citeshttps://doi.org/10.1007/978-3-031-39144-6_2openalex-snapshot
Explaining Two Strange Learning Curves
inferred← citeshttps://doi.org/10.1007/bf00996271openalex-snapshot
On the Stochastic Complexity of Learning Realizable and Unrealizable Rules
inferred← citeshttps://doi.org/10.1023/a:1007428731714openalex-snapshot
Statistical Mechanics of Online Learning of Drifting Concepts: A Variational Approach
inferred← citeshttps://doi.org/10.1023/a:1007495401240openalex-snapshot
Analytical Mean Squared Error Curves for Temporal Difference Learning
inferred← citeshttps://doi.org/10.1038/s41598-023-36148-7openalex-snapshot
Leveraging human expert image annotations to improve pneumonia differentiation through human knowledge distillation
inferred← citeshttps://doi.org/10.1088/1742-5468/ac3a77openalex-snapshot
A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent*
inferred← citeshttps://doi.org/10.1088/1751-8113/45/42/425005openalex-snapshot
Replica theory for learning curves for Gaussian processes on random graphs
inferred← citeshttps://doi.org/10.1109/isit.2016.7541669openalex-snapshot
Rate-distortion bounds on Bayes risk in supervised learning
inferred← citeshttps://doi.org/10.1109/tpami.2022.3220744openalex-snapshot
The Shape of Learning Curves: A Review
inferred← citeshttps://doi.org/10.1145/238061.238064openalex-snapshot
VC dimension of an integrate-and-fire neuron model
inferred← citeshttps://doi.org/10.1145/2484028.2484159openalex-snapshot
Sequential testing in classifier evaluation yields biased estimates of effectiveness
inferred← citeshttps://doi.org/10.1145/307400.307433openalex-snapshot
Theoretical analysis of a class of randomized regularization methods
inferred← citeshttps://doi.org/10.1145/3077257.3077265openalex-snapshot
An Overreaction to the Broken Machine Learning Abstraction
inferred← citeshttps://doi.org/10.1145/3292500.3332294openalex-snapshot
Statistical Mechanics Methods for Discovering Knowledge from Modern Production Quality Neural Networks
inferred← citeshttps://doi.org/10.18653/v1/p18-2072openalex-snapshot
Predicting accuracy on large datasets from smaller pilot data
inferred← citeshttps://doi.org/10.2478/jaiscr-2021-0012openalex-snapshot
Learning Novelty Detection Outside a Class of Random Curves with Application to COVID-19 Growth
inferred← citeshttps://doi.org/10.3390/bioengineering10121421openalex-snapshot
A Critical Assessment of Generative Models for Synthetic Data Augmentation on Limited Pneumonia X-ray Data
inferred← citeshttps://doi.org/10.36227/techrxiv.171340392.27668396/v1openalex-snapshot
Scaling Laws of Data-Driven Machine Learning Models: A Survey and Taxonomy
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.