A new three-point inverse solution using the α-β model reconstructs meteoroid masses and bulk densities from limited fireball observations, achieving 88% convergence on the EN catalog and producing a continuous density range of 300-4000 kg m^{-3} instead of discrete PE categories.
CMA-ES/pycma on Github,
9 Pith papers cite this work. Polarity classification is still indexing.
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Reformulates constrained black-box optimization as posterior inference in latent space of flow-based models amortized by outsourced diffusion models, claiming superior performance on synthetic and real tasks.
Introduces observation traveling salesman distance and observation entropy to quantify exploration in Bayesian optimization acquisition functions and links them to empirical performance.
GraViti introduces a graph-level VAE with relaxed permutation invariance that maps whole graphs to latent vectors, achieves strong reconstruction on large molecular datasets, and generates valid samples by learning constraints directly from graph-level representations.
PhDLspec combines differential spectra from physical stellar models with a transformer to derive approximately 30 stellar parameters from low-resolution spectra hundreds of times faster than traditional calculations.
BAxUS adapts Bayesian optimization over nested random subspaces to achieve better performance than prior high-dimensional methods while providing theoretical guarantees against failure.
An empirical study of JEPA world models identifies architecture, training objective, and planning choices that yield a model outperforming DINO-WM and V-JEPA-2-AC on navigation and manipulation tasks.
A method combining goal babbling with CMA-ES-based local online motor babbling is used to learn inverse kinematics and explore motor abundance on a 10-DoF musculoskeletal robot arm.
Shallow MLPs and dense CPGs outperform deeper MLPs and Actor-Critic RL in bounded robot control tasks with limited proprioception, with a Parameter Impact metric indicating extra RL parameters yield no performance gain over evolutionary strategies.
citing papers explorer
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Consistency between dynamical modeling and photometrically derived masses of fireballs
A new three-point inverse solution using the α-β model reconstructs meteoroid masses and bulk densities from limited fireball observations, achieving 88% convergence on the EN catalog and producing a continuous density range of 300-4000 kg m^{-3} instead of discrete PE categories.
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Posterior Inference in Latent Space for Scalable Constrained Black-box Optimization
Reformulates constrained black-box optimization as posterior inference in latent space of flow-based models amortized by outsourced diffusion models, claiming superior performance on synthetic and real tasks.
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Exploring Exploration in Bayesian Optimization
Introduces observation traveling salesman distance and observation entropy to quantify exploration in Bayesian optimization acquisition functions and links them to empirical performance.
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GraViti: Graph-Level Variational Autoencoders with Relaxed Permutation Invariance
GraViti introduces a graph-level VAE with relaxed permutation invariance that maps whole graphs to latent vectors, achieves strong reconstruction on large molecular datasets, and generates valid samples by learning constraints directly from graph-level representations.
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PhDLspec: physical-prior embedded deep learning method for spectroscopic determination of stellar labels in high-dimensional parameter space
PhDLspec combines differential spectra from physical stellar models with a transformer to derive approximately 30 stellar parameters from low-resolution spectra hundreds of times faster than traditional calculations.
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Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces
BAxUS adapts Bayesian optimization over nested random subspaces to achieve better performance than prior high-dimensional methods while providing theoretical guarantees against failure.
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What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?
An empirical study of JEPA world models identifies architecture, training objective, and planning choices that yield a model outperforming DINO-WM and V-JEPA-2-AC on navigation and manipulation tasks.
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Local Online Motor Babbling: Learning Motor Abundance of A Musculoskeletal Robot Arm
A method combining goal babbling with CMA-ES-based local online motor babbling is used to learn inverse kinematics and explore motor abundance on a 10-DoF musculoskeletal robot arm.
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Benefits of Low-Cost Bio-Inspiration in the Age of Overparametrization
Shallow MLPs and dense CPGs outperform deeper MLPs and Actor-Critic RL in bounded robot control tasks with limited proprioception, with a Parameter Impact metric indicating extra RL parameters yield no performance gain over evolutionary strategies.