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Quantitative Performance Analysis of Stopping Criteria for CMA-ES
Announce Type: new Abstract: Covariance matrix adaptation evolution strategy (CMA-ES) is a state-of-the-art black-box optimization algorithm. In general, CMA-ES uses a portfolio of multiple stopping criteria to automatically determine when to stop the search. This mechanism aims to avoid unnecessary consumption of the function evaluation budget during stagnation.
ES-Merging: Biological MLLM Merging via Embedding Space Signals
arXiv:2603.14405v2 Announce Type: replace Abstract: Biological multimodal large language models (MLLMs) have emerged as powerful foundation models for scientific discovery. However, existing models are specialized to a single modality, limiting their ability to solve inherently cross-modal scientific problems. While model merging is an efficient method to combine the different modalities into a unified MLLM, existing methods rely on input-agnostic parameter space heuristics that fail to...
Gradient-Free Training of Spiking Neural Networks via Low-Rank Evolution Strategies
Announce Type: new Abstract: Spiking Neural Networks (SNNs) offer compelling energy efficiency on neuromorphic hardware, yet their training remains challenging because the discrete spike threshold is non-differentiable. Surrogate-gradient methods sidestep this by approximating the derivative, but they impose backpropagation infrastructure that is incompatible with on-chip learning. Evolution Strategies (\es) are a natural gradient-free alternative, yet their computational cost scales with...
An energy-stable parametric finite element method for the Willmore flow in three dimensions
arXiv:2506.21025v3 Announce Type: replace Abstract: This work develops novel energy-stable parametric finite element methods (ES-PFEM) for the Willmore flow and curvature-dependent geometric gradient flows of surfaces in three dimensions. The key to achieving the energy stability lies in the use of two novel geometric identities: (i) a reformulated variational form of the normal velocity field, and (ii) incorporation of the temporal evolution of the mean curvature into the governing...
ReSGA: A Large Tail Risk Model for Learning Value-at-Risk and Expected Shortfall
Announce Type: cross Abstract: Learning Value-at-Risk (VaR) and Expected Shortfall (ES) is important for managing financial risks effectively. Existing approaches with limited parameters are vulnerable to model misspecification in the era of big data.
Roles of individual pigments in ultrafast excitation dynamics of light-harvesting phycobiliproteins revealed by recombinant techniques and two-dimensional electronic spectroscopy
Announce Type: replace Abstract: Phycobiliproteins serve as highly efficient light-harvesting antennae in cyanobacteria, yet the molecular factors governing their ultrafast energy relaxation and coherence dynamics remain incompletely understood. In this study, we investigate the role of pigment arrangement and pigment-protein interactions by combining recombinant protein engineering with two-dimensional electronic spectroscopy (2D-ES). In addition to wild-type allophycocyanin (APC) and...
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Nutrepedia 🇺🇸 EN ▼ 🇺🇸 English US 🇬🇧 English GB 🇨🇦 English CA 🇦🇺 English AU 🇲🇽 Español MX 🇪🇸 Español ES 🇦🇷 Español AR 🇨🇴 Español CO 🇧🇷 Português BR 🇵🇹 Português PT 🇫🇷 Français FR 🇨🇦 Français 🇳 中文 (普通话) 🇯🇵 日本語 🇰🇷 한국어 🇷🇺 Русский 🇩🇪 Deutsch 🇮🇹 Italiano 🇳🇱 Nederlands 🇸🇦 العربية 🇵🇱 Polski 🇹🇷 Türkçe 🇮🇩 Bahasa Indonesia 🇮🇳 हिन्दी 🇧🇩 বাংলা 🇮🇳 తెలుగు
Beyond Waypoints: A Trajectory-Centric Waypointing Paradigm for Vision-Language Navigation
Announce Type: new Abstract: Vision-Language Navigation in Continuous Environments (VLN-CE) requires agents to follow natural-language instructions while navigating in real-world-like environments. Most VLN-CE approach\-es adopt a three-stage framework: a waypoint predictor proposes navigable waypoints, and a navigator selects the best waypoint, with a low-level controller executing the movement to it. However, this decoupled paradigm often leads to unreachable waypoints or inconsistencies...
$\alpha$-PFN: Fast Entropy Search via In-Context Learning
Announce Type: new Abstract: Information-theoretic acquisition functions such as Entropy Search (ES) offer a principled exploration-exploitation framework for Bayesian optimization (BO). However, their practical implementation relies on complicated and slow approximations, i.e., a Monte Carlo estimation of the information gain. This complexity can introduce numerical errors and requires specialized, hand-crafted implementations.
Cyber Security of Sensor Systems for State Sequence Estimation: A Machine Learning Approach
arXiv:2506.06572v3 Announce Type: replace Abstract: Due to possible devastating consequences, counteracting sensor data attacks is an extremely impor- tant topic, which has not seen sufficient study. To the best of our knowledge, this paper develops the first meth- ods that accurately identify/eliminate only the problem- atic attacked sensor data presented to a sequence es- timation/regression algorithm under any attack from our attack model. The approach does not assume a known form for the...