Bayesian model averaging is a practical method for dealing with uncertainty due to model specification. Use of this technique requires the estimation of model probability weights. In this work, we revisit the derivation of estimators for these model...
By operations on models we show how to relate completeness with respect to permissive-nominal models to completeness with respect to nominal models with finite support. Models with finite support are a special case of permissive-nominal models, so th...
Atkinson–Shiffrin model (also known as the multi-store model or modal model) is a model of memory proposed in 1968 by Richard Atkinson and Richard Shiffrin. The
A mixed model, mixed-effects model or mixed error-component model is a statistical model containing both fixed effects and random effects. These models
This paper presents a New Open Logistics Interconnection (NOLI) reference model for a Physical Internet, inspired by the Open Systems Interconnection (OSI) reference model for data networks. This NOLI model is compared to the OSI model, and to the Tr...
We study a family of Ising perceptron models with $\{0,1\}$-valued activation functions. This includes the classical half-space models, as well as some of the symmetric models considered in recent works. For each of these models we show that the free...
The mixture cure rate model is the most commonly used cure rate model in the literature. In the context of mixture cure rate model, the standard approach to model the effect of covariates on the cured or uncured probability is to use a logistic funct...
As scientific and technological advancements result from human intellectual labor and computational costs, protecting model intellectual property (IP) has become increasingly important to encourage model creators and owners. Model IP protection invol...
In this paper, we propose a new influence spread model, namely, Complementary\&Competitive Independent Cascade (C$^2$IC) model. C$^2$IC model generalizes three well known influence model, i.e., influence boosting (IB) model, campaign oblivious (CO)IC...
In this work, we will be testing four different general \textit{f(R)}-gravity models, two of which are the more realistic models (namely the Starobinsky and the Hu-Sawicki models), to determine if they are viable alternative models to pursue a more v...
Recent work using Rank-One Model Editing (ROME), a popular model editing method, has shown that there are certain facts that the algorithm is unable to edit without breaking the model. Such edits have previously been called disabling edits. These dis...
We test various volatility models using the Bitcoin spot price series. Our models include HIST, EMA ARCH, GARCH, and EGARCH, models. Both of our in-sample-fit and out-of-sample-forecast results suggest that GARCH and EGARCH models perform much better...
Jan 28, 2026 · Discuss Tesla's Model S, Model 3, Model X, Model Y, Cybertruck, Roadster and More.
We propose a hybrid forecast model based on discrete grey-fuzzy Markov and grey neural network model and show that our hybrid model can improve much more the performance of forecast than traditional grey-Markov model and neural network models. Our si...
Model Mondays is a weekly livestream with Discord office hours - to help you navigate the fast-moving ecosystem of generative AI models with 5-minute roundups and 15-minute spotlight sessions. Build your model IQ - and make informed model choices! (⭐ 239 | Jupyter Notebook)
A team of researchers from OpenAI recently published a paper describing GPT-3, a deep-learning model for natural-language with 175 billion parameters, 100x more than the previous version, GPT-2. The model is pre-trained on nearly half a trillion words and achieves state-of-the-ar…
We propose a framework for robust evaluation of reasoning capabilities of language models, using functional variants of benchmarks. Models that solve a reasoning test should exhibit no difference in performance over the static version of a problem compared to a snapshot of the fu…
Model predictive control (MPC) algorithms can be sensitive to model mismatch when used in challenging nonlinear control tasks. In particular, the performance of MPC for vehicle control at the limits of handling suffers when the underlying model overe...
Deep neural network (DNN) models are valuable intellectual property of model owners, constituting a competitive advantage. Therefore, it is crucial to develop techniques to protect against model theft. Model ownership resolution (MOR) is a class of t...
The configuration interaction relativistic Hartree-Fock (CI-RHF) model is developed in this work. Compared to the conventional configuration interaction shell model (CISM), the CI-RHF model can be applied to study the structural properties of a wide...
The Sole-Manrubia model of ``macro''-evolution describes the origination, evolution and extinction of species on geological time scales. We report some properties of this model which provide deep insight into this simple model which captures severa...
This article provides a mathematically rigorous introduction to denoising diffusion probabilistic models (DDPMs), sometimes also referred to as diffusion probabilistic models or diffusion models, for generative artificial intelligence. We provide a d...
Jan 30, 2006 · Here's a quote from The Standard Catalog of Smith & Wesson, 3rd Ed. Model of 1988 & Model of 1989 625's, 45 acp & 45 Colt: "The authors are quite befuddled by varying information …
This study presents FiLLM, a Filipino-optimized large language model, designed to enhance natural language processing (NLP) capabilities in the Filipino language. Built upon the SeaLLM-7B 2.5 model, FiLLM leverages Low-Rank Adaptation (LoRA) fine-tun...
Recent advances in artificial intelligence have witnessed the emergence of large-scale deep learning models capable of interpreting and generating both textual and imaging data. Such models, typically referred to as foundation models, are trained on...
We present a discrete-time dynamic model of income tax evasion. The model is solved exactly in the case of a single taxpayer and shown to have some realistic properties, including avoiding the Yitzhaki paradox. The extension to an agent-based model w...
Jan 30, 2006 · Here's a quote from The Standard Catalog of Smith & Wesson, 3rd Ed. Model of 1988 & Model of 1989 625's, 45 acp & 45 Colt: "The authors are quite befuddled by varying information …
Hobbylinc carries 307 diecast model cars from 17 manufacturers. Diecast Model Cars are discounted up to 36%. Popular manufacturers are Model-Power, New-Ray, Imex and Jada-Toys. Categories …
Model Railroader is the leader in model railroad media in North America and has been since 1934. Discover articles and videos about model trains here.
Scaling up autoregressive models in vision has not proven as beneficial as in large language models. In this work, we investigate this scaling problem in the context of text-to-image generation, focusing on two critical factors: whether models use di...
It’s happening very openly but very subtly. The champions of open weight models are slowly increasing their sizes to the point a very small portion of this sub can run them locally. An even smaller ...
Based on the Nagel-Schreckenberg (NS) model with periodic boundary conditions, we proposed the NSOS model by adding the overtaking strategy (OS). In our model, overtaking vehicles are randomly selected with probability $q$ at each time step, and the...
Embedding models play a crucial role in representing and retrieving information across various NLP applications. Recent advances in large language models (LLMs) have further enhanced the performance of embedding models. While these models are often b...
We study the supersymmetric hybrid model near and after the end of inflation. As usual, we reduce the model to a purely scalar hybrid model on the level of the classical fields. But on the level of quantum fluctuations and their backreaction we tak...
Jan 30, 2006 · Here's a quote from The Standard Catalog of Smith & Wesson, 3rd Ed. Model of 1988 & Model of 1989 625's, 45 acp & 45 Colt: "The authors are quite befuddled by varying information …
In this paper, we focus on enhancing a diffusion-based text-to-video (T2V) model during the post-training phase by distilling a highly capable consistency model from a pretrained T2V model. Our proposed method, T2V-Turbo-v2, introduces a significant...
We identify the precise relationship between the five-parameter $τ^{(2)}$-family in the $N$-state chiral Potts model and XXZ chains with $U_q (sl_2)$-cyclic representation. By studying the Yang-Baxter relation of the six-vertex model, we discover...
We investigate the robustness properties of ResNeXt class image recognition models trained with billion scale weakly supervised data (ResNeXt WSL models). These models, recently made public by Facebook AI, were trained with ~1B images from Instagram...
The Maryland model was introduced more than 30 years ago as an integrable model of localization by aperiodic order. Even though quite popular and rich of fascinating mathematical properties, this model has so far remained quite artificial, as compare...
In this paper we present our preliminary work on model-based behavioral analysis of horse motion. Our approach is based on the SMAL model, a 3D articulated statistical model of animal shape. We define a novel SMAL model for horses based on a new temp...