Methods › General › Stochastic Optimization › Forward gradient

Forward gradient

12 papers tagged archive 2025-07-28

Introduced by Atılım Güneş Baydin et al. in Gradients without Backpropagation

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Forward gradients are unbiased estimators of the gradient ∇f(θ) for a function f: ℝⁿ →ℝ, given by g(θ) = ⟨∇f(θ) , v ⟩v.

Here v = (v₁, …, vₙ) is a random vector, which must satisfy the following conditions in order for g(θ) to be an unbiased estimator of ∇f(θ)

Forward gradients can be computed with a single jvp (Jacobian Vector Product), which enables the use of the forward mode of autodifferentiation instead of the usual reverse mode, which has worse computational characteristics.

PaperSource

Papers archive 2025-07-28

12 shown of 12, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

7 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Benchmarking1
Imitation Learning1
Memorization1
Model Optimization1
Offline RL1
Reinforcement Learning (RL)1
regression1

Usage over time archive 2025-07-28

Papers per year tagged with Forward gradient: 2022 to 2025, peak 4 4 0 2022: 4 papers 2022 2023: 3 papers 2023 2024: 4 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (12 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Stochastic Optimization

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