A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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This paper introduces self-adaptive physics-informed neural networks that dynamically adjust loss weights during training to improve solution accuracy for nonlinear PDEs.
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Proposes invariant Causal Representation Learning (iCaRL) for out-of-distribution generalization in nonlinear settings.
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Proposes a modular reinforcement learning architecture for nonlinear, nonstationary control tasks using multiple models.
Gene H. Golub, Víctor Pereyra
This paper reviews 30 years of variable projection method developments for separable nonlinear least-squares problems, showing it reduces parameter space and improves conditioning for faster convergence.
David Fournier, Hans J. Skaug, Johnoel Ancheta, et al.
ADMB is a programming framework using automatic differentiation for efficient and accurate statistical inference in highly parameterized nonlinear models.
Zheng Zhang
A flexible camera calibration technique using a planar pattern observed at multiple orientations, with closed-form solution and nonlinear refinement.
Sebastian Bach, Alexander Binder, Grégoire Montavon, et al.
Proposes Layer-Wise Relevance Propagation (LRP) to visualize pixel contributions for nonlinear classifiers, enabling interpretable heatmaps of classification decisions.
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SimCLR simplifies contrastive learning by optimizing data augmentation, adding learnable nonlinear transformations, and scaling batch size and training steps.