Owing to their simplicity, minimalistic design, and computational efficiency, MMPerc classifiers are promising candidates for conventional machine learning tasks, linear evaluation of deep neural networks, integration with hyperdimensional computing and vector symbolic architecture representations, and deployment in resource-constrained applications.
D. Rachkovskij, Evgeny Osipov, O. Volkov et al.· Neural Computation· 0 citations
It is suggested that domain-specific training matters more than model scale for PET/CT report error detection, supporting compact models as an accurate and computationally efficient approach to automated radiology report quality assurance.
Hermione Warr, Harry Anthony, Lilli J. Freischem et al.· 0 citations
Wave-BLS, a robust broad learning framework that integrates the wave loss function, which is asymmetric, bounded, and smooth, enabling controlled penalization of large errors, is proposed, establishing Wave-BLS as a stable and robust alternative to existing broad learning models for learning under data uncertainty.
M. Akhtar, A. Varshney, A. Quadir et al.· 0 citations
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A synthetic dataset containing fake private information is constructed and a white-box auditing framework is proposed to systematically assess whether claimed-forgotten information is genuinely removed and finds that a simpleverse greedy decoding -- selecting the least likely token at each step -- can recover supposedly forgotten private information.
Shi-Cheng Hu, Runhe Tian, Ziqiao Wang et al.· 0 citations
Future wireless systems are expected to transform the surrounding space from a passive propagation medium into a smart electromagnetic environment, where engineered surfaces control wave propagation, support wireless sensing, and create programmable electromagnetic fingerprints. A key challenge in realizing this vision is the inverse design of metasurfaces for tailored electromagnetic propagation. While forward analysis evaluates the response of a known geometry, the inverse task starts from a prescribed scattering signature and seeks a physically realizable structure that produces it. This inverse task is inherently nonlinear and often high-dimensional, while candidate solutions may be non-unique and provide no direct indication of practical realizability. Here, we introduce a conditional diffusion framework for inverse design of dielectric resonator metasurfaces from target angular scattering patterns. Trained on T-matrix simulated geometry-response pairs, the model learns a conditional distribution of geometries instead of a deterministic mapping, enabling multiple candidate designs for the ill-posed inverse problem. The best generated metasurface achieves a mean percentage error of 1.39%, outperforming CMA-ES optimization (4.1% after 10 h) while requiring only about one minute for after-training inference. The model also produces lower error distributions than deterministic neural baselines for out-of-distribution spectra, highlighting the potential of diffusion models for efficient metasurface design.
M. Tsukerman, K. Grotov, D. Vovchuk et al.· 0 citations
INTERVenE is presented, a family of Transformer architectures whose input is an interval-based, knowledge-based temporal abstraction (KBTA), a token stream of named clinical concepts drawn from a curated medical ontology, rather than an unnamed bin index or a raw measurement triplet.
The Graph Spectral Neural Operator is introduced, a neural operator that combines spatial graph spectral decompositions with temporal Fourier transforms through a unified space--time spectral kernel that enables globally coherent operator learning on non-Cartesian discretizations without domain warping or autoregressive rollouts.
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.
Abdolmehdi Behroozi, Chao-Peng Shen, Daniel Kifer et al.· 0 citations
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.
David Sulu, Lorenzo Di Fruscia, Jana M. Weber· 0 citations
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.
Miriam Kranzlmüller, P. Esser, Gitta Kutyniok· 0 citations
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.
Louen Pottier, Louis Lesueur, Anders Thorin· 1 citation
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
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.
