Large Language Models for EDA Cloud Job Resource and Lifetime Prediction
arXiv:2512.19701v1 Announce Type: new
Abstract: The rapid growth of cloud computing in the Electronic Design Automation (EDA) industry has created a critical need for resource and job lifetime prediction to achieve optimal scheduling. Traditional machine learning methods often struggle with the com...
Reducing Label Dependency in Human Activity Recognition with Wearables: From Supervised Learning to Novel Weakly Self-Supervised Approaches
arXiv:2512.19713v1 Announce Type: new
Abstract: Human activity recognition (HAR) using wearable sensors has advanced through various machine learning paradigms, each with inherent trade-offs between performance and labeling requirements. While fully supervised techniques achieve high accuracy, they...
Development and external validation of a multimodal artificial intelligence mortality prediction model of critically ill patients using multicenter data
arXiv:2512.19716v1 Announce Type: new
Abstract: Early prediction of in-hospital mortality in critically ill patients can aid clinicians in optimizing treatment. The objective was to develop a multimodal deep learning model, using structured and unstructured clinical data, to predict in-hospital mor...
Thermodynamic Focusing for Inference-Time Search: Practical Methods for Target-Conditioned Sampling and Prompted Inference
arXiv:2512.19717v1 Announce Type: new
Abstract: Finding rare but useful solutions in very large candidate spaces is a recurring practical challenge across language generation, planning, and reinforcement learning. We present a practical framework, \emph{Inverted Causality Focusing Algorithm} (ICFA)...
Synthetic Data Blueprint (SDB): A modular framework for the statistical, structural, and graph-based evaluation of synthetic tabular data
arXiv:2512.19718v1 Announce Type: new
Abstract: In the rapidly evolving era of Artificial Intelligence (AI), synthetic data are widely used to accelerate innovation while preserving privacy and enabling broader data accessibility. However, the evaluation of synthetic data remains fragmented across ...
PhysMaster: Building an Autonomous AI Physicist for Theoretical and Computational Physics Research
arXiv:2512.19799v1 Announce Type: new
Abstract: Advances in LLMs have produced agents with knowledge and operational capabilities comparable to human scientists, suggesting potential to assist, accelerate, and automate research. However, existing studies mainly evaluate such systems on well-defined...
A Branch-and-Price Algorithm for Fast and Equitable Last-Mile Relief Aid Distribution
arXiv:2512.19882v1 Announce Type: new
Abstract: The distribution of relief supplies to shelters is a critical aspect of post-disaster humanitarian logistics. In major disasters, prepositioned supplies often fall short of meeting all demands. We address the problem of planning vehicle routes from a ...
Interpolative Decoding: Exploring the Spectrum of Personality Traits in LLMs
arXiv:2512.19937v1 Announce Type: new
Abstract: Recent research has explored using very large language models (LLMs) as proxies for humans in tasks such as simulation, surveys, and studies. While LLMs do not possess a human psychology, they often can emulate human behaviors with sufficiently high f...
Zero-Shot Segmentation through Prototype-Guidance for Multi-Label Plant Species Identification
arXiv:2512.19957v1 Announce Type: new
Abstract: This paper presents an approach developed to address the PlantClef 2025 challenge, which consists of a fine-grained multi-label species identification, over high-resolution images. Our solution focused on employing class prototypes obtained from the t...
FGDCC: Fine-Grained Deep Cluster Categorization -- A Framework for Intra-Class Variability Problems in Plant Classification
arXiv:2512.19960v1 Announce Type: new
Abstract: Intra-class variability is given according to the significance in the degree of dissimilarity between images within a class. In that sense, depending on its intensity, intra-class variability can hinder the learning process for DL models, specially wh...
Can AI fix the operating room? This startup thinks so
There’s plenty of hype around AI and robots in healthcare, but the problem that’s actually costing hospitals money right now is operating room coordination. Two to four hours of OR time is lost every single day, not because of the surgeries themselves, but because of everything in between from manua...
The Machine Learning “Advent Calendar” Day 23: CNN in Excel
A step-by-step 1D CNN for text, built in Excel, where every filter, weight, and decision is fully visible.
The post The Machine Learning “Advent Calendar” Day 23: CNN in Excel appeared first on Towards Data Science.
John Carreyrou and other authors bring new lawsuit against six major AI companies
These authors rejected Anthropic's class action settlement, arguing that "LLM companies should not be able to so easily extinguish thousands upon thousands of high-value claims at bargain-basement rates."
This article is divided into two parts; they are: • What Is Perplexity and How to Compute It • Evaluate the Perplexity of a Language Model with HellaSwag Dataset Perplexity is a measure of how well a language model predicts a sample of text.
Lemon Slice nabs $10.5M from YC and Matrix to build out its digital avatar tech
Digital avatar generation company Lemon Slice is working to add a video layer to AI chatbots with a new diffusion model that can create digital avatars from a single image.
Understanding the process behind agentic planning and task management in LangChain
The post How Agents Plan Tasks with To-Do Lists appeared first on Towards Data Science.
Stop Retraining Blindly: Use PSI to Build a Smarter Monitoring Pipeline
A data scientist's guide to population stability index (PSI)
The post Stop Retraining Blindly: Use PSI to Build a Smarter Monitoring Pipeline appeared first on Towards Data Science.
Google Code Wiki: Live Docs, Diagrams & Chat for Any GitHub Repo
Coding experts tend to use 30-40% of their time only for comprehending the already existing code. These are two entire working days every week that are wasted on going through obsolete documentation, understanding ambiguous code, and desperately searching for developers who quit months ago. On the ...
Synergy in Clicks: Harsanyi Dividends for E-Commerce
A brief overview of the math behind the Harsanyi Dividend and a real-world application in Streamlit
The post Synergy in Clicks: Harsanyi Dividends for E-Commerce appeared first on Towards Data Science.
How social media encourages the worst of AI boosterism
Demis Hassabis, CEO of Google DeepMind, summed it up in three words: “This is embarrassing.” Hassabis was replying on X to an overexcited post by Sébastien Bubeck, a research scientist at the rival firm OpenAI, announcing that two mathematicians had used OpenAI’s latest large language model, GPT-5...
Comparative Evaluation of Explainable Machine Learning Versus Linear Regression for Predicting County-Level Lung Cancer Mortality Rate in the United States
arXiv:2512.17934v1 Announce Type: new
Abstract: Lung cancer (LC) is a leading cause of cancer-related mortality in the United States. Accurate prediction of LC mortality rates is crucial for guiding targeted interventions and addressing health disparities. Although traditional regression-based mode...
What's the Price of Monotonicity? A Multi-Dataset Benchmark of Monotone-Constrained Gradient Boosting for Credit PD
arXiv:2512.17945v1 Announce Type: new
Abstract: Financial institutions face a trade-off between predictive accuracy and interpretability when deploying machine learning models for credit risk. Monotonicity constraints align model behavior with domain knowledge, but their performance cost - the pric...