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4,920 papers
Geometry finally makes a commanded 3D target a natural goal interface: it is constructed the goal latent from the target and the current latent, at no cost in success rate, without a goal observation.
Event-time posterior modeling is established as a probabilistic and interpretable formulation for linking single-trial EEG dynamics to behavioral timing for linking single-trial EEG dynamics to behavioral timing.
Economic benchmarks add incremental predictive information to a largely date-driven general factor, and the evidence does not support treating them as a distinct latent capability.
It is argued that LASSO, not the highest-discriminating model, is the model best suited to direct clinical deployment, and lessons for the machine learning and healthcare community regarding data infrastructure, model selection, and value of calibration and interpretability in high-stakes decision support are presented.
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OEIS-derived benchmarks substantially measure memorisation, and MDL supplies a cheap, contamination-free difficulty signal they currently lack, according to an exactly computable reference learner.
This work proposes a fully distributed continuous-time algorithm for shared linear equality constraints that converges without multiplier exchange and reaches any GNE, reducing communication overhead and improving privacy.
CallosumNet is a spatio-temporal graph unlearning framework biologically inspired by the corpus callosum structure that reconstructs subgraphs using biologically-inspired virtual edges and restores interlinked spatio-temporal dependencies among subgraphs via a lightweight meta-graph integration layer.
IsleNet is proposed, which uses spatial-entropy-guided partitioning to create balanced, locally coherent subgraphs and reconnects them with lightweight virtual edges and ensures exact removal with low cost of ST-graph unlearning.
The proposed GP-pro-c model, a product-of-experts Gaussian process model that incorporates an information-based method to calibrate the overestimated posterior variances, is proposed and suggests that the proposed information-based calibration method is a promising approach for improving uncertainty estimation in scalable GP models.
Multi-band EEG Latent-state Tokenization (MEL), a coordinate-preserving EEG representation framework that anchors each target fMRI response to its preceding EEG history and organizes it into lag-channel-frequency neural-state tokens, which improves prediction over strong NeuroBOLT baselines.
Open quantum systems lose energy and phase coherence through different dissipative processes, but these processes can produce overlapping dynamical signatures. The Liouvillian spectrum summarizes how such a system relaxes, yet it is not obvious how much of that spectrum is needed to distinguish the underlying dissipation rates. We study this question for amplitude damping and dephasing in a six-qubit Lindblad model whose spectrum can be derived analytically. We retain only the slowest non-steady spectral modes and ask how many are required before each dissipative rate becomes recoverable. We show that population modes contain no dephasing information, which creates a lower bound of D = 2^n retained modes for uniform dephasing identifiability in the relevant rate regime. The measured recovery threshold reaches this bound at n = 4,5,6, while n = 3 remains above it. At n = 6, least squares achieves a mean joint absolute error of order 10^-9, compared with 4.355 x 10^-4 for four tabular learning methods. Robustness tests show that this advantage weakens when the spectra are perturbed and when a transverse field breaks the commuting structure. These results show that the amount and structure of retained spectral information can determine whether dissipative parameters are recoverable, independently of the estimator used. The present conclusions apply to noise-free simulator spectra rather than measurement-derived spectra.
A larger-batch configuration reduces time-to-target only when its throughput gain exceeds its samples-to-target penalty, and a larger-batch configuration reduces time-to-target only when its throughput gain exceeds its samples-to-target penalty.
Education Innovation
Machine Intelligence
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
General Science
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.
Climate & Sustainability
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