Category
3,686 papers
A discrete diffusion model that parameterizes normalized probability distributions using variational autoregressive networks using variational autoregressive networks is introduced, establishing a neural-network framework for the discrete diffusion model with normalized probability distributions.
It is argued that accelerated samplers such as flow maps are essential infrastructure for efficient post-training, and that the dominant KL-regularized formulation is only one of many choices worth revisiting.
The Gravitational Reference Advanced Technology Test In Space (GRATTIS) mission will demonstrate the functionality and sensitivity performance of the Simplified Gravitational Reference Sensor (S-GRS), an ultra-precise inertial sensor for future Earth geodesy missions. These sensors are used to measure or compensate for...
Wearable biosensors have considerable potential to improve early intervention, lessen healthcare costs, and facilitate remote care models, but additional clinical validation, regulatory alignment, and strong integration into healthcare infrastructures are necessary to realise this potential.
Reach audiences
Advertise in front of researchers, engineers, and readers.
This work proposes count-FM, a flow-matching framework for count data based on a continuous-time birth-death process with local unit jumps, which achieves better sample quality than representative baselines while using substantially fewer parameters in simulation.
Beyond conditional average treatment effects, treatments may impact the entire outcome distribution in covariate-dependent ways, for example, by altering the variance or tail risks for specific subpopulations. We propose a novel estimand to capture such conditional distributional treatment effects, and develop a doubly...
Deep learning problems rarely involve objectives that are equal in importance. A primary objective defines the goal, whilst secondary objectives, such as sparsity, compression, or robustness constrain the solution. While existing multi-objective methods have proven effective in practice, they have a clear symmetry prob...
This work revisits the analysis of best-policy identification for finite-horizon risk-sensitive reinforcement learning under the entropic risk measure through a forward-model based algorithm building on KL-based exploration bonuses that adapt to the entropic criterion.
Generative models for time-series imputation achieve strong reconstruction accuracy, yet provide no finite-sample reliability guarantees, a critical limitation in power systems where imputed values inform dispatch and planning. We introduce SPLICE (Self-supervised Predictive Latent Inpainting with Conformal Envelopes),...
PRISM-FCP is a communication-efficient Byzantine-robust federated conformal prediction framework that uses partial model sharing to mitigate stochastic model-poisoning attacks during training and histogram-based filtering to mitigate adversarial calibration submissions.
We study decentralized partially observable team decision problems with low-rank latent dynamics and unknown system models. The proposed framework combines team-theoretic equivalence with low-rank model representations to address cooperative decision-making in partially observable Markov decision processes without prio...
This work proposes automatic depth-based local center clustering (A-DLCC), a fully data-driven method that eliminates numerical parameter tuning in clustering and designs a single agglomeration rule that automatically estimates the number of clusters and decides when to stop merging.
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Health & Bioscience
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Data Mining & Modeling
GPT-Lab
· gpt-lab.eu
Nov 3, 2026
Is your company’s AI sovereign? A practical guide for CIOs on AI vendor lock-in, EU Data Act switching, cloud sovereignty, and testing your exit plan before you need it. The post Sovereignty Is the Option to Leave appeared first on GPT-Lab.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
