Paper Citation Record · LEDGER

Discovering Symbolic Models from Deep Learning with Inductive Biases

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 27 inbound Pith citation observations for arXiv:2006.11287.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2006.11287 v2

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Pith citing papers itemized under the disclosed page cap.

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Pith citing papers

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Learning interactions between Rydberg atoms cites this paper.

Learning interactions between Rydberg atoms Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 92

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Generating particle physics Lagrangians with transformers cites this paper.

Generating particle physics Lagrangians with transformers Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 26

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Principled model selection for stochastic dynamics cites this paper.

Principled model selection for stochastic dynamics Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 6

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Towards characterizing dark matter subhalo perturbations in stellar streams with graph neural networks cites this paper.

Towards characterizing dark matter subhalo perturbations in stellar streams with graph neural networks Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 32

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Machine Learning-Based Analytical Expressions for Gray-Body Factors and Application to Primordial Black Holes cites this paper.

Machine Learning-Based Analytical Expressions for Gray-Body Factors and Application to Primordial Black Holes Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 35

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Dynamical Data for More Efficient and Generalizable Learning: A Case Study in Disordered Elastic Networks cites this paper.

Dynamical Data for More Efficient and Generalizable Learning: A Case Study in Disordered Elastic Networks Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 36

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Learning Causality for Modern Machine Learning cites this paper.

Learning Causality for Modern Machine Learning Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 2023

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$\mathcal{CP}$-Analyses with Symbolic Regression cites this paper.

$\mathcal{CP}$-Analyses with Symbolic Regression Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 35

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How Should We Meta-Learn Reinforcement Learning Algorithms? cites this paper.

How Should We Meta-Learn Reinforcement Learning Algorithms? Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 14

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Data-driven discovery of dynamical models in biology cites this paper.

Data-driven discovery of dynamical models in biology Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 241

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Symbolic Regression for Shared Expressions: Introducing Partial Parameter Sharing cites this paper.

Symbolic Regression for Shared Expressions: Introducing Partial Parameter Sharing Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 5

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Learning to Unscramble: Simplifying Symbolic Expressions via Self-Supervised Oracle Trajectories cites this paper.

Learning to Unscramble: Simplifying Symbolic Expressions via Self-Supervised Oracle Trajectories Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 10

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Into the Gompverse: A robust Gompertzian reionization model for CMB analyses cites this paper.

Into the Gompverse: A robust Gompertzian reionization model for CMB analyses Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 27

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Neuro-Symbolic ODE Discovery with Latent Grammar Flow cites this paper.

Neuro-Symbolic ODE Discovery with Latent Grammar Flow Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 11

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Neuro-Symbolic ODE Discovery with Latent Grammar Flow cites this paper.

Neuro-Symbolic ODE Discovery with Latent Grammar Flow Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 11

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Machine Learning for Multi-messenger Probes of New Physics and Cosmology: A Review and Perspective cites this paper.

Machine Learning for Multi-messenger Probes of New Physics and Cosmology: A Review and Perspective Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 235

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Scale-Aware Adversarial Analysis: A Diagnostic for Generative AI in Multiscale Complex Systems cites this paper.

Scale-Aware Adversarial Analysis: A Diagnostic for Generative AI in Multiscale Complex Systems Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 88

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Predicting intermediate-mass black hole formation in star clusters with machine learning cites this paper.

Predicting intermediate-mass black hole formation in star clusters with machine learning Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 84

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Observation e3b87af4-e7f8-463e-b7a7-bc2599b9f53a · inbound

Symbolic Classification-Enabled LHC Limits Online BSM Global Fits cites this paper.

Symbolic Classification-Enabled LHC Limits Online BSM Global Fits Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 39

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Self-Revising Discovery Systems for Science: A Categorical Framework for Agentic Artificial Intelligence cites this paper.

Self-Revising Discovery Systems for Science: A Categorical Framework for Agentic Artificial Intelligence Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 71

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EML-CD: Causal Mechanism Recovery via EML Symbolic Trees in Structure Learning cites this paper.

EML-CD: Causal Mechanism Recovery via EML Symbolic Trees in Structure Learning Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 7

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Observation 86174ac2-939d-4d8f-bab9-ae9163c6f301 · inbound

Physics-guided discovery of dynamical dark-energy equations of state through iterative AI reasoning cites this paper.

Physics-guided discovery of dynamical dark-energy equations of state through iterative AI reasoning Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 22

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Sample Complexity of Scientific Discovery: PAC Learnability of Compositional Function Trees cites this paper.

Sample Complexity of Scientific Discovery: PAC Learnability of Compositional Function Trees Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 50

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Evolution-Level Quantum Optimal Control of Single-Qubit Gates with Physics-Informed Neural Networks cites this paper.

Evolution-Level Quantum Optimal Control of Single-Qubit Gates with Physics-Informed Neural Networks Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 4

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Attractor Geometry Determines the Identifiability Limits of System Discovery cites this paper.

Attractor Geometry Determines the Identifiability Limits of System Discovery Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 2020

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Symbolic Extraction of Non-Perturbative Transverse-Momentum-Dependent Distributions from Drell-Yan Data cites this paper.

Symbolic Extraction of Non-Perturbative Transverse-Momentum-Dependent Distributions from Drell-Yan Data Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 49

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Foundation Models for Astrophysics cites this paper.

Foundation Models for Astrophysics Discovering Symbolic Models from Deep Learning with Inductive Biases

Reference 26

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