PEPSKit.jl is a new Julia software package providing high-level algorithms for iPEPS tensor-network simulations of 2D quantum systems with symmetry support.
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Don’t unroll adjoint: Dif- ferentiating SSA-Form programs
11 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
This paper presents reverse-mode algorithmic differentiation (AD) based on source code transformation, in particular of the Static Single Assignment (SSA) form used by modern compilers. The approach can support control flow, nesting, mutation, recursion, data structures, higher-order functions, and other language constructs, and the output is given to an existing compiler to produce highly efficient differentiated code. Our implementation is a new AD tool for the Julia language, called Zygote, which presents high-level dynamic semantics while transparently compiling adjoint code under the hood. We discuss the benefits of this approach to both the usability and performance of AD tools.
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cs.LG 2 cs.PL 2 math.OC 2 astro-ph.IM 1 cond-mat.str-el 1 cs.DC 1 physics.chem-ph 1 quant-ph 1roles
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use method 1representative citing papers
CHAD is a homomorphic source-to-source transformation for forward- and reverse-mode AD on higher-order functional languages with arrays, proven correct via compositional logical relations.
Constrained policy optimization for stochastic optimal control under nonstationary uncertainties via Markov embeddability and finite approximation.
Zygote is a differentiable programming system in Julia that supports gradients for nearly all language constructs while generating high-performance code without user refactoring.
A method for adjoint differentiation of stencil loops that preserves their structure and parallelizability via combined AD and loop transformations, released as the PerforAD tool with seismic and CFD test cases.
A Riemannian L-BFGS method with adapted Cauchy-point bound handling outperforms classical interior-point and L-BFGS-B solvers on mixed manifold-plus-bounds problems by orders of magnitude.
Magnetic cavity coupling renders H2 ground states metastable, inverts singlet-triplet gaps, and stabilizes exotic antiaromatic states in rings like H4 and C4H4 by preventing Jahn-Teller distortions.
Gradient-based optimization of SUPER and FTPE pulse protocols via auto-differentiation and uniTEMPO yields higher preparation fidelities than resonant pi-pulses or standard two-photon excitation, with the advantage increasing at higher temperatures.
A distributionally robust PAC-Bayesian approach derives sub-Gaussian loss proxies and performance bounds tied to closed-loop operator norms via system level synthesis, enabling optimization-based safety certificates for controllers facing sim-to-real gaps.
The authors compare multiple methods for incorporating action information into RNN state updates for RL and report empirical results on illustrative domains.
PySR delivers a distributed evolutionary symbolic regression tool with a new EmpiricalBench for recovering historical scientific equations from data.
citing papers explorer
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PEPSKit.jl: A Julia package for projected entangled-pair state simulations
PEPSKit.jl is a new Julia software package providing high-level algorithms for iPEPS tensor-network simulations of 2D quantum systems with symmetry support.
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CHAD: Combinatory Homomorphic Automatic Differentiation
CHAD is a homomorphic source-to-source transformation for forward- and reverse-mode AD on higher-order functional languages with arrays, proven correct via compositional logical relations.
-
Constrained Policy Optimization for Stochastic Optimal Control under Nonstationary Uncertainties
Constrained policy optimization for stochastic optimal control under nonstationary uncertainties via Markov embeddability and finite approximation.
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A Differentiable Programming System to Bridge Machine Learning and Scientific Computing
Zygote is a differentiable programming system in Julia that supports gradients for nearly all language constructs while generating high-performance code without user refactoring.
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Automatic Differentiation for Adjoint Stencil Loops
A method for adjoint differentiation of stencil loops that preserves their structure and parallelizability via combined AD and loop transformations, released as the PerforAD tool with seismic and CFD test cases.
-
A Riemannian quasi-Newton algorithm for optimization with Euclidean bounds
A Riemannian L-BFGS method with adapted Cauchy-point bound handling outperforms classical interior-point and L-BFGS-B solvers on mixed manifold-plus-bounds problems by orders of magnitude.
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Engineering molecular potential energy surfaces using magnetic cavity quantum electrodynamics
Magnetic cavity coupling renders H2 ground states metastable, inverts singlet-triplet gaps, and stabilizes exotic antiaromatic states in rings like H4 and C4H4 by preventing Jahn-Teller distortions.
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Efficient optimisation of multi-parameter quantum control protocols for strongly-coupled systems
Gradient-based optimization of SUPER and FTPE pulse protocols via auto-differentiation and uniTEMPO yields higher preparation fidelities than resonant pi-pulses or standard two-photon excitation, with the advantage increasing at higher temperatures.
-
Distributionally Robust PAC-Bayesian Control
A distributionally robust PAC-Bayesian approach derives sub-Gaussian loss proxies and performance bounds tied to closed-loop operator norms via system level synthesis, enabling optimization-based safety certificates for controllers facing sim-to-real gaps.
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Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning
The authors compare multiple methods for incorporating action information into RNN state updates for RL and report empirical results on illustrative domains.
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Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl
PySR delivers a distributed evolutionary symbolic regression tool with a new EmpiricalBench for recovering historical scientific equations from data.