automatic differentiation
PulseAugur coverage of automatic differentiation — every cluster mentioning automatic differentiation across labs, papers, and developer communities, ranked by signal.
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New research explores finite-difference methods for PINNs
A new paper explores the use of finite-difference (FD) methods for computing derivatives in Physics-Informed Neural Networks (PINNs), presenting it as an alternative to automatic differentiation (AD). The research demon…
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Finite-difference methods offer faster, more accurate derivatives for PINNs
A new paper explores finite-difference (FD) methods as an alternative to automatic differentiation (AD) for computing derivatives in physics-informed neural networks (PINNs). The research indicates that FD can match AD …
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New solver-level differentiation method enhances differentiable simulations
Researchers have developed a new method called solver-level differentiation for creating differentiable simulations. This approach differentiates the executed solver directly, rather than the converged equation or relyi…
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Quantum classifiers show inherent defense against gradient attacks due to measurement costs
Researchers have analyzed the cost of adversarial attacks against quantum classifiers, finding that finite quantum measurement statistics, or shot noise, can act as a defense mechanism. The study quantifies the measurem…
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New methods explore gradient-free optimization for neural networks
Researchers are exploring novel methods for optimizing neural networks without relying on traditional gradient-based approaches. One paper introduces a first-order layer for differentiable optimization that avoids compu…
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New algorithms accelerate optimization for machine learning and spectrum cartography
Researchers have developed new methods for accelerating optimization algorithms, specifically focusing on randomized-subspace Nesterov accelerated gradient techniques. These methods aim to reduce computational costs by …