Tootfinder

#Springer Nature book on #machinelearning is full of made-up #citations

Oliver Sacks on sensory substitution in 2010, predictions about the next 30 years https://www.discovermagazine.com/we-are-learning-to-exploit-the-amazing-plasticity-of-the-brain-16718 We are learning to exploit the amazing plasticity…

We Are Learning to Exploit the Amazing Plasticity of the Brain
Neurologist Oliver Sacks on his hopes for science over the next 30 years

Deep Brain Net: An Optimized Deep Learning Model for Brain tumor Detection in MRI Images Using EfficientNetB0 and ResNet50 with Transfer Learning
Daniel Onah, Ravish Desai
https://arxiv.org/abs/2507.07011

Deep Brain Net: An Optimized Deep Learning Model for Brain tumor Detection in MRI Images Using EfficientNetB0 and ResNet50 with Transfer Learning
In recent years, deep learning has shown great promise in the automated detection and classification of brain tumors from MRI images. However, achieving high accuracy and computational efficiency remains a challenge. In this research, we propose Deep Brain Net, a novel deep learning system designed to optimize performance in the detection of brain tumors. The model integrates the strengths of two advanced neural network architectures which are EfficientNetB0 and ResNet50, combined with transfer…

CleanQRL: Lightweight Single-file Implementations of Quantum Reinforcement Learning Algorithms
Georg Kruse, Rodrigo Coelho, Andreas Rosskopf, Robert Wille, Jeanette Miriam Lorenz
https://arxiv.org/abs/2507.07593

CleanQRL: Lightweight Single-file Implementations of Quantum Reinforcement Learning Algorithms
At the interception between quantum computing and machine learning, Quantum Reinforcement Learning (QRL) has emerged as a promising research field. Due to its novelty, a standardized and comprehensive collection for QRL algorithms has not yet been established. Researchers rely on numerous software stacks for classical Reinforcement Learning (RL) as well as on various quantum computing frameworks for the implementation of the quantum subroutines of their QRL algorithms. Inspired by the CleanRL l…

ChronoFlow - a Data-driven Model for #Gyrochronology: https://iopscience.iop.org/article/10.3847/1538-4357/adcd73 -> U of T Astronomers Pioneer Innovative Machine Learning Model to Determine the Ages of Stars: https://www.dunlap.utoronto.ca/u-of-t-astronomers-pioneer-innovative-machine-learning-model-to-determine-the-ages-of-stars/

FLoRA: An Advanced AI-Powered Engine to Facilitate Hybrid Human-AI Regulated Learning
Xinyu Li, Tongguang Li, Lixiang Yan, Yuheng Li, Linxuan Zhao, Mladen Rakovi\'c, Inge Molenaar, Dragan Ga\v{s}evi\'c, Yizhou Fan
https://arxiv.org/abs/2507.07362

FLoRA: An Advanced AI-Powered Engine to Facilitate Hybrid Human-AI Regulated Learning
SRL, defined as learners' ability to systematically plan, monitor, and regulate their learning activities, is crucial for sustained academic achievement and lifelong learning competencies. Emerging Artificial Intelligence (AI) developments profoundly influence SRL interactions by potentially either diminishing or strengthening learners' opportunities to exercise their own regulatory skills. Recent literature emphasizes a balanced approach termed Hybrid Human-AI Regulated Learning (HHAIRL), in w…

Ironically eponymous.
Springer Nature Book on Machine Learning is Full of Made-up Citations
'Based on a tip from a reader, we checked 18 of the 46 citations in the book. Two-thirds of them either did not exist or had substantial errors. And three researchers cited in the book confirmed the works they supposedly authored were fake or the citation contained substantial errors.'

Springer Nature book on machine learning is full of made-up citations
Would you pay $169 for an introductory ebook on machine learning with citations that appear to be made up? If not, you might want to pass on purchasing Mastering Machine Learning: From Basics to Ad…

ME$^3$-BEV: Mamba-Enhanced Deep Reinforcement Learning for End-to-End Autonomous Driving with BEV-Perception
Siyi Lu, Run Liu, Dongsheng Yang, Lei He
https://arxiv.org/abs/2508.06074

ME$^3$-BEV: Mamba-Enhanced Deep Reinforcement Learning for End-to-End Autonomous Driving with BEV-Perception
Autonomous driving systems face significant challenges in perceiving complex environments and making real-time decisions. Traditional modular approaches, while offering interpretability, suffer from error propagation and coordination issues, whereas end-to-end learning systems can simplify the design but face computational bottlenecks. This paper presents a novel approach to autonomous driving using deep reinforcement learning (DRL) that integrates bird's-eye view (BEV) perception for enhanced …

A Unified Empirical Risk Minimization Framework for Flexible N-Tuples Weak Supervision
Shuying Huang, Junpeng Li, Changchun Hua, Yana Yang
https://arxiv.org/abs/2507.07771

A Unified Empirical Risk Minimization Framework for Flexible N-Tuples Weak Supervision
To alleviate the annotation burden in supervised learning, N-tuples learning has recently emerged as a powerful weakly-supervised method. While existing N-tuples learning approaches extend pairwise learning to higher-order comparisons and accommodate various real-world scenarios, they often rely on task-specific designs and lack a unified theoretical foundation. In this paper, we propose a general N-tuples learning framework based on empirical risk minimization, which systematically integrates …

Bridging Logic and Learning: Decoding Temporal Logic Embeddings via Transformers
Sara Candussio, Gaia Saveri, Gabriele Sarti, Luca Bortolussi
https://arxiv.org/abs/2507.07808

Bridging Logic and Learning: Decoding Temporal Logic Embeddings via Transformers
Continuous representations of logic formulae allow us to integrate symbolic knowledge into data-driven learning algorithms. If such embeddings are semantically consistent, i.e. if similar specifications are mapped into nearby vectors, they enable continuous learning and optimization directly in the semantic space of formulae. However, to translate the optimal continuous representation into a concrete requirement, such embeddings must be invertible. We tackle this issue by training a Transformer…