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4,920 papers
Results support sampled sensitivity supervision as a practical way to improve neural PDE surrogates when forward accuracy, inverse stability, robustness, and computational cost must be considered together.
Results show that explicitly teaching the relation between a molecule and its structural core can reliably shape the organization of molecular embedding space, while the extent of usefulness of this organization remains task dependent.
An analytical framework is developed to compare fully-connected ReLU ANNs and integrate-and-fire SNNs for time-series data with respect to their theoretical energy efficiency at matched expressive capacity, and derives an expressivity-normalized efficiency ratio and explicit thresholds in network width, spike sparsity, and ANN depth scaling.
It is demonstrated that imposing a linear encoder preserves most of the representational capacity of the autoencoder, provided the decoder remains nonlinear, and suggested that the nonlinear decoder is the critical component for manifold learning, rather than the encoder.
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Experimental results demonstrate that the proposed uncertainty-aware replay buffer enables an RL agent to obtain higher rewards during training compared to other existing uncertainty-aware RL frameworks.
A scalable bandit-based system that optimizes product page layouts in real time while preserving human control over design intent, providing a scalable path toward learning-to-design the web.
An architecture search framework that makes the alignment between experts and the structure of the data an explicit search variable and ensures that the assignment of data clusters to experts is optimised jointly with the per-expert architectures is proposed.
PruneShift, an evaluation framework that separates broad predictive fidelity, fidelity near selector outputs, and the quality of the selected pruning decision, is introduced, showing why predictive fit, decision reliability, and pruning method quality require separate evidence.
The first complex augmented Broad Learning System (CA-BLS) is introduced, which transforms real-valued inputs into phase-encoded complex representations and adopts widely linear modeling to jointly leverage covariance and pseudo-covariance information via complex conjugate augmentation, enabling effective modeling of latent nonlinearities, coherence structures, and second-order dependencies inaccessible to conventional BLS formulations.
GraM-Diff is proposed, a unified classifier-guided Graph-Mamba diffusion framework for EEG synthesis that embeds Graph Convolutional Networks within a diffusion U-Net to model inter-electrode connectivity and Bidirectional Mamba state-space blocks for linear-complexity long-range temporal modeling.
HDR-RoPE is proposed, which extends RoPE from independent 2D rotations to higher-dimensional rotations and introduces a Paley-I orthogonal basis to obtain balanced, isotropic, and dense phase mixing within each rotation subspace and significantly enhances channel coupling and rotational degrees of freedom while maintaining orthogonal stability and the relative position closure property.
An underlying mechanism explaining the gap in uncertainty quantification is identified: path switching, where agents frequently abandon their current search direction in-trajectory, breaking the link between early signal and final outcome.
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GPT-Lab
· gpt-lab.eu
Sep 3, 2026
Adaptive AI agents can help make BIM data more machine-readable by navigating IFC models, interpreting inconsistent information, and mapping it to defined standards. In this blog, Alok Rawat shares findings from a real-world pilot in construction workflows. The post Adaptive AI Agents in Construction Workflows appeared first on GPT-Lab.
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