mailitics

Tag: modeling

  • Modeling Urban Walking Risk Using Spatial-Temporal Machine Learning

    Modeling Urban Walking Risk Using Spatial-Temporal Machine Learning Estimating neighborhood-level pedestrian risk from real-world incident data The post Modeling Urban Walking Risk Using Spatial-Temporal Machine Learning appeared first on Towards Data Science. Aneesh Patil Go to original source

  • Topic Modeling Techniques for 2026: Seeded Modeling, LLM Integration, and Data Summaries

    Topic Modeling Techniques for 2026: Seeded Modeling, LLM Integration, and Data Summaries Seeded topic modeling, integration with LLMs, and training on summarized data are the fresh parts of the NLP toolkit. The post Topic Modeling Techniques for 2026: Seeded Modeling, LLM Integration, and Data Summaries appeared first on Towards Data Science. Petr Koráb Go to…

  • Generative modeling of conditional probability distributions on the level-sets of collective variables

    Generative modeling of conditional probability distributions on the level-sets of collective variables arXiv:2512.17374v1 Announce Type: new Abstract: Given a probability distribution $mu$ in $mathbb{R}^d$ represented by data, we study in this paper the generative modeling of its conditional probability distributions on the level-sets of a collective variable $xi: mathbb{R}^d rightarrow mathbb{R}^k$, where $1 le k…

  • Masked Mineral Modeling: Continent-Scale Mineral Prospecting via Geospatial Infilling

    Masked Mineral Modeling: Continent-Scale Mineral Prospecting via Geospatial Infilling arXiv:2511.09722v1 Announce Type: new Abstract: Minerals play a critical role in the advanced energy technologies necessary for decarbonization, but characterizing mineral deposits hidden underground remains costly and challenging. Inspired by recent progress in generative modeling, we develop a learning method which infers the locations of minerals…

  • Beyond Linear Diffusions: Improved Representations for Rare Conditional Generative Modeling

    Beyond Linear Diffusions: Improved Representations for Rare Conditional Generative Modeling arXiv:2510.02499v1 Announce Type: new Abstract: Diffusion models have emerged as powerful generative frameworks with widespread applications across machine learning and artificial intelligence systems. While current research has predominantly focused on linear diffusions, these approaches can face significant challenges when modeling a conditional distribution, $P(Y|X=x)$, when…

  • Flow Matching-Based Generative Modeling for Efficient and Scalable Data Assimilation

    Flow Matching-Based Generative Modeling for Efficient and Scalable Data Assimilation arXiv:2508.13313v1 Announce Type: new Abstract: Data assimilation (DA) is the problem of sequentially estimating the state of a dynamical system from noisy observations. Recent advances in generative modeling have inspired new approaches to DA in high-dimensional nonlinear settings, especially the ensemble score filter (EnSF). However,…

  • Advanced Topic Modeling with LLMs

    Advanced Topic Modeling with LLMs A deep dive into topic modeling by leveraging representation models and generative AI with BERTopic The post Advanced Topic Modeling with LLMs appeared first on Towards Data Science. Alex Davis Go to original source

  • Prescriptive Modeling Makes Causal Bets – Whether You Know it or Not!

    Prescriptive Modeling Makes Causal Bets – Whether You Know it or Not! An explanation of the causal assumption implicit in prescriptive modeling and how to satisfy it. The post Prescriptive Modeling Makes Causal Bets – Whether You Know it or Not! appeared first on Towards Data Science. Jarom Hulet Go to original source

  • Prescriptive Modeling Unpacked: A Complete Guide to Intervention With Bayesian Modeling.

    Prescriptive Modeling Unpacked: A Complete Guide to Intervention With Bayesian Modeling. Learn how to move beyond prediction and actively make intervention through prescriptive modeling. This in-depth guide walks you through Bayesian approaches to system intervention, with practical examples in predictive maintenance. The post Prescriptive Modeling Unpacked: A Complete Guide to Intervention With Bayesian Modeling. appeared…

  • Modeling Spatial Extremes using Non-Gaussian Spatial Autoregressive Models via Convolutional Neural Networks

    Modeling Spatial Extremes using Non-Gaussian Spatial Autoregressive Models via Convolutional Neural Networks arXiv:2505.03034v1 Announce Type: new Abstract: Data derived from remote sensing or numerical simulations often have a regular gridded structure and are large in volume, making it challenging to find accurate spatial models that can fill in missing grid cells or simulate the process…

  • Why Most Cyber Risk Models Fail Before They Begin

    Why Most Cyber Risk Models Fail Before They Begin Cybersecurity leaders are being asked impossible questions. “What’s the likelihood of a breach this year?” “How much would it cost?” And “how much should we spend to stop it?” Yet most risk models used today are still built on guesswork, gut instinct, and colorful heatmaps, not…

  • No-Regret Generative Modeling via Parabolic Monge-Amp`ere PDE

    No-Regret Generative Modeling via Parabolic Monge-Amp`ere PDE arXiv:2504.09279v1 Announce Type: new Abstract: We introduce a novel generative modeling framework based on a discretized parabolic Monge-Amp`ere PDE, which emerges as a continuous limit of the Sinkhorn algorithm commonly used in optimal transport. Our method performs iterative refinement in the space of Brenier maps using a mirror…

  • Poisson-Process Topic Model for Integrating Knowledge from Pre-trained Language Models

    Poisson-Process Topic Model for Integrating Knowledge from Pre-trained Language Models arXiv:2503.17809v1 Announce Type: new Abstract: Topic modeling is traditionally applied to word counts without accounting for the context in which words appear. Recent advancements in large language models (LLMs) offer contextualized word embeddings, which capture deeper meaning and relationships between words. We aim to leverage…

  • Beyond Causal Language Modeling

    Beyond Causal Language Modeling A deep dive into “Not All Tokens Are What You Need for Pretraining” Introduction A few days ago, I had the chance to present at a local reading group that focused on some of the most exciting and insightful papers from NeurIPS 2024. As a presenter, I selected a paper titled…

  • Modeling COVID-19 spread in the USA using metapopulation SIR models coupled with graph convolutional neural networks

    Modeling COVID-19 spread in the USA using metapopulation SIR models coupled with graph convolutional neural networks arXiv:2501.02043v1 Announce Type: new Abstract: Graph convolutional neural networks (GCNs) have shown tremendous promise in addressing data-intensive challenges in recent years. In particular, some attempts have been made to improve predictions of Susceptible-Infected-Recovered (SIR) models by incorporating human mobility…

  • Enhancing Masked Time-Series Modeling via Dropping Patches

    Enhancing Masked Time-Series Modeling via Dropping Patches arXiv:2412.15315v1 Announce Type: new Abstract: This paper explores how to enhance existing masked time-series modeling by randomly dropping sub-sequence level patches of time series. On this basis, a simple yet effective method named DropPatch is proposed, which has two remarkable advantages: 1) It improves the pre-training efficiency by…