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A simple differentiable programming language
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/0-306-48332-7_23openalex-snapshot
Automatic Differentiation: Root Problem and Branch Problem
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Security protocols : 6th International Workshop, Cambridge, UK, April 15-17, 1998 : proceedings
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Using Theorem Proving for Numerical Analysis Correctness Proof of an Automatic Differentiation Algorithm
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Foundations of Software Science and Computation Structures
inferredcites →https://doi.org/10.1007/978-3-319-21401-6_26openalex-snapshot
The Lean Theorem Prover (System Description)
inferredcites →https://doi.org/10.1007/978-3-319-89366-2_25openalex-snapshot
Differential Calculus with Imprecise Input and Its Logical Framework
inferredcites →https://doi.org/10.1007/978-3-642-37075-5_22openalex-snapshot
A Language for Differentiable Functions
inferredcites →https://doi.org/10.1007/978-3-642-54833-8_3openalex-snapshot
An Array-Oriented Language with Static Rank Polymorphism
inferredcites →https://doi.org/10.1007/s10990-008-9037-1openalex-snapshot
Nesting forward-mode AD in a functional framework
inferredcites →https://doi.org/10.1016/0377-0427(94)90294-1openalex-snapshot
The if-problem in automatic differentiation
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An Introduction to Real Analysis
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Categorical Models for Simply Typed Resource Calculi : Proceedings of the 26th Conference on the Mathematical Foundations of Programming Semantics (MFPS 2010)
inferredcites →https://doi.org/10.1016/j.entcs.2012.08.017openalex-snapshot
A Simply Typed λ-Calculus of Forward Automatic Differentiation
inferredcites →https://doi.org/10.1016/s0304-3975(03)00392-xopenalex-snapshot
The differential lambda-calculus
inferredcites →https://doi.org/10.1017/s0960129504004359openalex-snapshot
Domain theory and differential calculus (functions of one variable)
inferredcites →https://doi.org/10.1090/surv/053openalex-snapshot
The Convenient Setting of Global Analysis
inferredcites →https://doi.org/10.1108/09504120610664321openalex-snapshot
Encyclopedia of Optimization
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Stochastic gradient descent with differentially private updates
inferredcites →https://doi.org/10.1137/1.9781611971309openalex-snapshot
Optimization and Nonsmooth Analysis
inferredcites →https://doi.org/10.1145/1330017.1330018openalex-snapshot
Reverse-mode AD in a functional framework
inferredcites →https://doi.org/10.1145/1390156openalex-snapshot
Proceedings of the 25th international conference on Machine learning - ICML '08
inferredcites →https://doi.org/10.1145/2450153.2450158openalex-snapshot
The Tapenade automatic differentiation tool
inferredcites →https://doi.org/10.1145/2976749.2978318openalex-snapshot
Deep Learning with Differential Privacy
inferredcites →https://doi.org/10.1145/3190508.3190551openalex-snapshot
Dynamic control flow in large-scale machine learning
inferredcites →https://doi.org/10.1145/3236765openalex-snapshot
The simple essence of automatic differentiation
inferredcites →https://doi.org/10.1145/3341700openalex-snapshot
Demystifying differentiable programming: shift/reset the penultimate backpropagator
inferredcites →https://doi.org/10.1145/3341701openalex-snapshot
Efficient differentiable programming in a functional array-processing language
inferredcites →https://doi.org/10.25080/majora-92bf1922-003openalex-snapshot
Theano: A CPU and GPU Math Compiler in Python
inferredcites →https://doi.org/10.4230/lipics.ecoop.2019.25openalex-snapshot
Automatic differentiation in machine learning: a survey
inferredcites →https://doi.org/10.48550/arxiv.1605.08695openalex-snapshot
TensorFlow: A system for large-scale machine learning
inferredcites →https://doi.org/10.48550/arxiv.1706.08605openalex-snapshot
Developing Bug-Free Machine Learning Systems With Formal Mathematics
inferredcites →https://doi.org/10.48550/arxiv.1707.00558openalex-snapshot
Pycobra: A Python Toolbox for Ensemble Learning and Visualisation
inferredcites →https://doi.org/10.48550/arxiv.1809.09569openalex-snapshot
Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming
inferredcites →https://doi.org/10.48550/arxiv.1903.01855openalex-snapshot
TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning
inferredcites →https://doi.org/10.48550/arxiv.1912.01703openalex-snapshot
PyTorch: An Imperative Style, High-Performance Deep Learning Library
inferredcites →https://doi.org/10.70930/tac/ktrjz7joopenalex-snapshot
Differential Restriction Categories
inferredcites →https://doi.org/10.70930/tac/y9rglypbopenalex-snapshot
Cartesian Differential Categories
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-030-44914-8_3openalex-snapshot
On the Versatility of Open Logical Relations
inferred← citeshttps://doi.org/10.1007/978-3-030-45231-5_17openalex-snapshot
Correctness of Automatic Differentiation via Diffeologies and Categorical Gluing
inferred← citeshttps://doi.org/10.1007/978-3-030-72019-3_22openalex-snapshot
Reverse AD at Higher Types: Pure, Principled and Denotationally Correct
inferred← citeshttps://doi.org/10.1007/978-3-031-21780-7_11openalex-snapshot
Integrating Deep Neural Networks with Dependent Type Semantics
inferred← citeshttps://doi.org/10.1016/j.tcs.2021.09.027openalex-snapshot
Differential logical relations, part II increments and derivatives
inferred← citeshttps://doi.org/10.1017/s0956796825100051openalex-snapshot
Parallel dual-numbers reverse AD
inferred← citeshttps://doi.org/10.1017/s096012952300018xopenalex-snapshot
CHAD for expressive total languages
inferred← citeshttps://doi.org/10.1017/s0960129524000215openalex-snapshot
Automatic differentiation for ML-family languages: Correctness via logical relations
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Smoothing Methods for Automatic Differentiation Across Conditional Branches
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On the principles of differentiable quantum programming languages
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Parallelism-preserving automatic differentiation for second-order array languages
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Provably correct, asymptotically efficient, higher-order reverse-mode automatic differentiation
inferred← citeshttps://doi.org/10.1145/3527634openalex-snapshot
CHAD: Combinatory Homomorphic Automatic Differentiation
inferred← citeshttps://doi.org/10.1145/3571198openalex-snapshot
ADEV: Sound Automatic Differentiation of Expected Values of Probabilistic Programs
inferred← citeshttps://doi.org/10.1145/3571236openalex-snapshot
You Only Linearize Once: Tangents Transpose to Gradients
inferred← citeshttps://doi.org/10.1145/3571247openalex-snapshot
Efficient Dual-Numbers Reverse AD via Well-Known Program Transformations
inferred← citeshttps://doi.org/10.1145/3617178openalex-snapshot
Differentiable Quantum Programming with Unbounded Loops
inferred← citeshttps://doi.org/10.1145/3624007.3624054openalex-snapshot
Partial Evaluation of Automatic Differentiation for Differential-Algebraic Equations Solvers
inferred← citeshttps://doi.org/10.1145/3632897openalex-snapshot
Monotonicity and the Precision of Program Analysis
inferred← citeshttps://doi.org/10.1145/3689492.3690051openalex-snapshot
Scimitar: Functional Programs as Optimization Problems
inferred← citeshttps://doi.org/10.1145/3734192.3734195openalex-snapshot
Distributed Strategies—Past and Future
inferred← citeshttps://doi.org/10.1145/3763125openalex-snapshot
Sound and Modular Activity Analysis for Automatic Differentiation in MLIR
inferred← citeshttps://doi.org/10.1145/3776654openalex-snapshot
JAX Autodiff from a Linear Logic Perspective
inferred← citeshttps://doi.org/10.1177/29498732251339951openalex-snapshot
On the Relevance of Logic for Artificial Intelligence, and the Promise of Neurosymbolic Learning
inferred← citeshttps://doi.org/10.26226/morressier.604907f41a80aac83ca25d18openalex-snapshot
Reverse AD at Higher Types: Pure, Principled and Denotationally Correct
inferred← citeshttps://doi.org/10.26226/morressier.604907f41a80aac83ca25d56openalex-snapshot
On the Versatility of Open Logical Relations: Continuity, Automatic Differentiation, and a Containment Theorem
inferred← citeshttps://doi.org/10.3390/asi5030047openalex-snapshot
Components and Indicators of the Robot Programming Skill Assessment Based on Higher Order Thinking
inferred← citeshttps://doi.org/10.4204/eptcs.333.20openalex-snapshot
Categorical semantics of a simple differential programming language
inferred← citeshttps://doi.org/10.46298/entics.12278openalex-snapshot
Cartesian Differential Kleisli Categories
inferred← citeshttps://doi.org/10.46298/entics.12303openalex-snapshot
A Language for Evaluating Derivatives of Functionals Using Automatic Differentiation
inferred← citeshttps://doi.org/10.46298/lmcs-18(1:41)2022openalex-snapshot
Higher Order Automatic Differentiation of Higher Order Functions
inferred← citeshttps://doi.org/10.46298/lmcs-19(2:3)2023openalex-snapshot
Smart Choices and the Selection Monad
inferred← citeshttps://doi.org/10.48550/arxiv.2002.08241openalex-snapshot
A Differential-form Pullback Programming Language for Higher-order Reverse-mode Automatic Differentiation
inferred← citeshttps://doi.org/10.48550/arxiv.2007.05282openalex-snapshot
Denotational Correctness of Forward-Mode Automatic Differentiation for Iteration and Recursion
inferred← citeshttps://doi.org/10.48550/arxiv.2008.12613openalex-snapshot
Type-driven Neural Programming by Example
inferred← citeshttps://doi.org/10.48550/arxiv.2105.03918openalex-snapshot
Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics
inferred← citeshttps://doi.org/10.48550/arxiv.2107.07559openalex-snapshot
Learning Stable Galerkin Models of Turbulence with Differentiable Programming
inferred← citeshttps://doi.org/10.48550/arxiv.2109.07573openalex-snapshot
Differentiable Physics: A Position Piece
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Towards Denotational Semantics of AD for Higher-Order, Recursive, Probabilistic Languages
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