Morning Overview on MSN

LLMs have tons of parameters, but what is a parameter?

Large language models are routinely described in terms of their size, with figures like 7 billion or 70 billion parameters ...

MiroMind’s MiroThinker 1.5 delivers trillion-parameter performance from a 30B model — at 1/20th the cost

Joining the ranks of a growing number of smaller, powerful reasoning models is MiroThinker 1.5 from MiroMind, with just 30 ...
eLife

Virtual Brain Inference (VBI): A flexible and integrative toolkit for efficient probabilistic inference on virtual brain models

This paper presents a valuable software package, named "Virtual Brain Inference" (VBI), that enables faster and more efficient inference of parameters in dynamical system models of whole-brain ...
eLife

Virtual Brain Inference (VBI): A flexible and integrative toolkit for efficient probabilistic inference on virtual brain models

Not revised: This Reviewed Preprint includes the authors’ original preprint (without revision), an eLife assessment, public reviews, and a provisional response from the authors. This work provides a ...
GitHub

inference.log parameter #332

I’m new to nnDetection and have a question. In the inference.log file, what is the difference between "Model Inference Summary" and "Found Inference Plan"? Specifically, I noticed that in the Model ...
VentureBeat

Inside Ring-1T: Ant engineers solve reinforcement learning bottlenecks at trillion scale

China’s Ant Group, an affiliate of Alibaba, detailed technical information around its new model, Ring-1T, which the company said is “the first open-source reasoning model with one trillion total ...
The Next Platform

Google Shows Off Its Inference Scale And Prowess

If the hyperscalers are masters of anything, it is driving scale up and driving costs down so that a new type of information technology can be cheap enough so it can be widely deployed. The ...
GitHub

Timing randomization using statistical parameters

To introduce more realistic timing behavior, all time-related parameters should support randomized values based on a mean and standard deviation, defined in the configuration. This approach will ...
Frontiers

Comparative analysis of inflow forecasting using machine learning and statistical techniques: case study of Mangla reservoir and Marala Headworks

Management of water resources is a universal challenge, transcending geographical boundaries. It has been in the limelight for decades due to climate and environmental changes at the global level. The ...
IEEE

A Method to Compute the Probability of Positioning Failure for Vehicles in the Context of Dependence Between Parameter Estimation and Statistical Hypothesis Testing

Abstract: Positioning technologies are widely used in automotive, aviation, rail, and maritime safety-critical applications. Therefore, the computation of the probability of positioning failure for ...
Scientific Research Publishing

Generalized Bayesian Inference for Regression-Type Models with an Intractable Normalizing Constant ()

Regression models with intractable normalizing constants are valuable tools for analyzing complex data structures, yet parameter inference for such models remains highly challenging—particularly when ...