Tootfinder

Use multi level models with {parsnip}: http://multilevelmod.tidymodels.org/ #rstats #ML

Higher-order Kripke models for intuitionistic and non-classical modal logics
Victor Barroso-Nascimento
https://arxiv.org/abs/2507.18798 https://arxiv.org/p…

Higher-order Kripke models for intuitionistic and non-classical modal logics
This paper introduces higher-order Kripke models, a generalization of standard Kripke models that is remarkably close to Kripke's original idea - both mathematically and conceptually. Standard Kripke models are now considered $0$-ary models, whereas an $n$-ary model for $n > 0$ is a model whose set of objects (''possible worlds'') contains only $(n-1)$-ary Kripke models. Models with infinitely many layers are also considered. This framework is obtained by promoting a radical change of perspecti…

via https://www.theguardian.com/technology/2025/may/26/large-language-models-that-power-ai-should-be-publicly-owned:
Matteo Valleriani: "It is time to build public, open-access LLMs for the humanities…

Source: Meta has hired highly influential OpenAI researcher Trapit Bansal to work on its AI reasoning models under the company's new AI superintelligence unit (Maxwell Zeff/TechCrunch)
https://techcrunch.com/2025/06/26/meta-hi…

Meta hires key OpenAI researcher to work on AI reasoning models | TechCrunch
Meta has hired a key OpenAI researcher to work on AI reasoning models under its new AI superintelligence unit.

Hybrid Models for Financial Forecasting: Combining Econometric, Machine Learning, and Deep Learning Models
Dominik Stempie\'n, Robert \'Slepaczuk
https://arxiv.org/abs/2505.19617

Hybrid Models for Financial Forecasting: Combining Econometric, Machine Learning, and Deep Learning Models
This research systematically develops and evaluates various hybrid modeling approaches by combining traditional econometric models (ARIMA and ARFIMA models) with machine learning and deep learning techniques (SVM, XGBoost, and LSTM models) to forecast financial time series. The empirical analysis is based on two distinct financial assets: the S&P 500 index and Bitcoin. By incorporating over two decades of daily data for the S&P 500 and almost ten years of Bitcoin data, the study provides a comp…

Last week, we continued our #ISE2025 lecture on distributional semantics with the introduction of neural language models (NLMs) and compared them to traditional statistical n-gram models.
Benefits of NLMs:
- Capturing Long-Range Dependencies
- Computational and Statistical Tractability
- Improved Generalisation
- Higher Accuracy
@…

CXR-CML: Improved zero-shot classification of long-tailed multi-label diseases in Chest X-Rays
Rajesh Madhipati, Sheethal Bhat, Lukas Buess, Andreas Maier
https://arxiv.org/abs/2507.19398

CXR-CML: Improved zero-shot classification of long-tailed multi-label diseases in Chest X-Rays
Chest radiography (CXR) plays a crucial role in the diagnosis of various diseases. However, the inherent class imbalance in the distribution of clinical findings presents a significant challenge for current self-supervised deep learning models. These models often fail to accurately classify long-tailed classes. Current Vision-Language models such as Contrastive Language Image Pre-training (CLIP) models effectively model the manifold distribution of the latent space, enabling high zero-shot clas…

CodeGuard: A Generalized and Stealthy Backdoor Watermarking for Generative Code Models
Haoxuan Li, Jiale Zhang, Xiaobing Sun, Xiapu Luo
https://arxiv.org/abs/2506.20926

CodeGuard: A Generalized and Stealthy Backdoor Watermarking for Generative Code Models
Generative code models (GCMs) significantly enhance development efficiency through automated code generation and code summarization. However, building and training these models require computational resources and time, necessitating effective digital copyright protection to prevent unauthorized leaks and misuse. Backdoor watermarking, by embedding hidden identifiers, simplifies copyright verification by breaking the model's black-box nature. Current backdoor watermarking techniques face two mai…

$T^3$: Multi-level Tree-based Automatic Program Repair with Large Language Models
Quanming Liu, Xupeng Bu, Zhichao Yan, Ru Li
https://arxiv.org/abs/2506.21211

$T^3$: Multi-level Tree-based Automatic Program Repair with Large Language Models
Automatic Program Repair (APR) is a core technology in software development and maintenance, with aims to enable automated defect repair with minimal human intervention. In recent years, the substantial advancements in Large Language Models (LLMs) and the Chain-of-Thought (CoT) techniques have significantly enhanced the reasoning capabilities of these models. However, due to the complex logic and multi-step reasoning ability needed, the application of CoT techniques in the APR domain remains in…

Alibaba releases its Qwen3-235B-A22B-Thinking-2507 reasoning LLM on Hugging Face, topping several benchmarks, as Alibaba moves away from hybrid reasoning models (Carl Franzen/VentureBeat)
https://venturebeat.com/ai/its-qwens-s…