On the public citation graph

Deep self-consistent learning of local volatility

0 signed · 11 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.

Claim with ORCID

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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1088/0266-5611/19/1/306openalex-extracted

Calibration of the local volatility in a trinomial tree using Tikhonov regularization

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-extracted

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/tetci.2021.3086237openalex-extracted

Deep Learning in Physics: A Study of Dielectric Quasi-Cubic Particles in a Uniform Electric Field

inferredcites →https://doi.org/10.1137/20m1381538openalex-extracted

Short Communication: Beyond Surrogate Modeling: Learning the Local Volatility via Shape Constraints

inferredcites →https://doi.org/10.1145/1390156.1390294openalex-extracted

Extracting and composing robust features with denoising autoencoders

inferredcites →https://doi.org/10.4230/lipics.ecoop.2019.25openalex-extracted

Automatic differentiation in machine learning: a survey

inferredcites →https://doi.org/10.48550/arxiv.1502.03167openalex-extracted

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

inferredcites →https://doi.org/10.48550/arxiv.2105.09866openalex-extracted

inferredcites →https://doi.org/10.48550/arxiv.2108.01862openalex-extracted

Reconstructing a dynamical system and forecasting time series by self-consistent deep learning

inferredcites →https://doi.org/10.5555/1756006.1953039openalex-extracted

Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion

inferredcites →https://doi.org/10.1137/1.9780898717495openalex-snapshot

Computational Methods for Option Pricing

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.