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Data Science

"OpenAI quietly removed the one safety mechanism that could shut the whole thing down — and nobody is talking about it"

OpenAI was founded as a nonprofit for one specific reason — to ensure AI development couldn't be hijacked by profit motives. Their origin...

Reddit - Artificial Intelligence · 1 min ·
Machine Learning

[P] citracer: a small CLI tool to trace where a concept comes from in a citation graph

Hi all, I made a small tool that I've been using for my own literature reviews and figured I'd share in case it's useful to anyone else. ...

Reddit - Machine Learning · 1 min ·
Data Science

What actually makes something the best AI meeting recorder?

I’ve been trying a few meeting tools lately and realized I care way less about flashy summaries than I thought. What I actually want is p...

Reddit - Artificial Intelligence · 1 min ·

All Content

[2602.16530] FEKAN: Feature-Enriched Kolmogorov-Arnold Networks
Machine Learning

[2602.16530] FEKAN: Feature-Enriched Kolmogorov-Arnold Networks

The paper introduces Feature-Enriched Kolmogorov-Arnold Networks (FEKAN), an advanced model that enhances computational efficiency and pr...

arXiv - Machine Learning · 4 min ·
[2602.15861] CAST: Achieving Stable LLM-based Text Analysis for Data Analytics
Llms

[2602.15861] CAST: Achieving Stable LLM-based Text Analysis for Data Analytics

The paper presents CAST, a framework designed to improve the stability of LLM-based text analysis in data analytics by enhancing output c...

arXiv - AI · 3 min ·
[2602.16503] Interpretability-by-Design with Accurate Locally Additive Models and Conditional Feature Effects
Machine Learning

[2602.16503] Interpretability-by-Design with Accurate Locally Additive Models and Conditional Feature Effects

This paper introduces Conditionally Additive Local Models (CALMs), which enhance the interpretability of Generalized Additive Models (GAM...

arXiv - AI · 3 min ·
[2602.16507] Small molecule retrieval from tandem mass spectrometry: what are we optimizing for?
Machine Learning

[2602.16507] Small molecule retrieval from tandem mass spectrometry: what are we optimizing for?

This paper explores the optimization of loss functions in deep learning models for small molecule retrieval from tandem mass spectrometry...

arXiv - Machine Learning · 3 min ·
[2602.16498] Fast and Scalable Analytical Diffusion
Machine Learning

[2602.16498] Fast and Scalable Analytical Diffusion

The paper presents GoldDiff, a novel framework for analytical diffusion that enhances scalability and speed in generative modeling by dyn...

arXiv - AI · 4 min ·
[2602.16456] Beyond SGD, Without SVD: Proximal Subspace Iteration LoRA with Diagonal Fractional K-FAC
Machine Learning

[2602.16456] Beyond SGD, Without SVD: Proximal Subspace Iteration LoRA with Diagonal Fractional K-FAC

This paper presents a novel approach called LoRSum for optimizing Low-Rank Adaptation (LoRA) in machine learning, enhancing efficiency in...

arXiv - Machine Learning · 4 min ·
[2602.16468] HPMixer: Hierarchical Patching for Multivariate Time Series Forecasting
Machine Learning

[2602.16468] HPMixer: Hierarchical Patching for Multivariate Time Series Forecasting

The paper presents HPMixer, a novel approach for multivariate time series forecasting that effectively models periodic patterns and resid...

arXiv - Machine Learning · 3 min ·
[2602.16449] GICDM: Mitigating Hubness for Reliable Distance-Based Generative Model Evaluation
Machine Learning

[2602.16449] GICDM: Mitigating Hubness for Reliable Distance-Based Generative Model Evaluation

The paper presents GICDM, a method to mitigate hubness in distance-based evaluations of generative models, enhancing reliability and alig...

arXiv - AI · 3 min ·
[2602.15851] Narrative Theory-Driven LLM Methods for Automatic Story Generation and Understanding: A Survey
Llms

[2602.15851] Narrative Theory-Driven LLM Methods for Automatic Story Generation and Understanding: A Survey

This survey explores the intersection of narrative theory and large language models (LLMs) for automatic story generation and understandi...

arXiv - AI · 4 min ·
[2602.16436] Learning with Locally Private Examples by Inverse Weierstrass Private Stochastic Gradient Descent
Nlp

[2602.16436] Learning with Locally Private Examples by Inverse Weierstrass Private Stochastic Gradient Descent

This paper presents a novel method for correcting bias in binary classification tasks using locally private examples, leveraging the Inve...

arXiv - Machine Learning · 3 min ·
[2602.15844] Language Model Representations for Efficient Few-Shot Tabular Classification
Llms

[2602.15844] Language Model Representations for Efficient Few-Shot Tabular Classification

This paper explores the use of language model representations for efficient few-shot classification of tabular data, proposing a new para...

arXiv - AI · 4 min ·
[2602.16400] Easy Data Unlearning Bench
Machine Learning

[2602.16400] Easy Data Unlearning Bench

The paper introduces the Easy Data Unlearning Bench, a unified benchmarking suite aimed at simplifying the evaluation of machine unlearni...

arXiv - Machine Learning · 3 min ·
[2602.15843] The Perplexity Paradox: Why Code Compresses Better Than Math in LLM Prompts
Llms

[2602.15843] The Perplexity Paradox: Why Code Compresses Better Than Math in LLM Prompts

This article explores the 'perplexity paradox' in large language models (LLMs), demonstrating that code compresses better than mathematic...

arXiv - AI · 3 min ·
[2602.16357] Optical Inversion and Spectral Unmixing of Spectroscopic Photoacoustic Images with Physics-Informed Neural Networks
Machine Learning

[2602.16357] Optical Inversion and Spectral Unmixing of Spectroscopic Photoacoustic Images with Physics-Informed Neural Networks

This article presents the Spectroscopic Photoacoustic Optical Inversion Autoencoder (SPOI-AE), a novel approach using physics-informed ne...

arXiv - Machine Learning · 3 min ·
[2602.15832] What Persona Are We Missing? Identifying Unknown Relevant Personas for Faithful User Simulation
Machine Learning

[2602.15832] What Persona Are We Missing? Identifying Unknown Relevant Personas for Faithful User Simulation

This article explores the identification of unknown user personas in simulations, introducing the PICQ dataset and evaluating leading LLM...

arXiv - AI · 3 min ·
[2602.16341] Explainability for Fault Detection System in Chemical Processes
Machine Learning

[2602.16341] Explainability for Fault Detection System in Chemical Processes

This article evaluates two explainability methods, Integrated Gradients and SHAP, for fault detection in chemical processes using an LSTM...

arXiv - Machine Learning · 3 min ·
[2602.16327] Guide-Guard: Off-Target Predicting in CRISPR Applications
Machine Learning

[2602.16327] Guide-Guard: Off-Target Predicting in CRISPR Applications

The paper presents Guide-Guard, a machine learning solution designed to predict off-target effects in CRISPR applications with 84% accura...

arXiv - AI · 3 min ·
[2602.16316] A Graph Meta-Network for Learning on Kolmogorov-Arnold Networks
Machine Learning

[2602.16316] A Graph Meta-Network for Learning on Kolmogorov-Arnold Networks

This paper introduces WS-KAN, a novel weight-space architecture for Kolmogorov-Arnold Networks (KANs), demonstrating its superior perform...

arXiv - Machine Learning · 4 min ·
[2602.16481] Leveraging Large Language Models for Causal Discovery: a Constraint-based, Argumentation-driven Approach
Llms

[2602.16481] Leveraging Large Language Models for Causal Discovery: a Constraint-based, Argumentation-driven Approach

This article explores the use of large language models (LLMs) in causal discovery, proposing a constraint-based, argumentation-driven app...

arXiv - AI · 3 min ·
[2602.16435] Causally-Guided Automated Feature Engineering with Multi-Agent Reinforcement Learning
Robotics

[2602.16435] Causally-Guided Automated Feature Engineering with Multi-Agent Reinforcement Learning

The paper presents CAFE, a novel framework for automated feature engineering that combines causal discovery with multi-agent reinforcemen...

arXiv - Machine Learning · 4 min ·
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Related Topics

Machine Learning Large Language Models Generative AI Natural Language Processing Computer Vision Robotics & Embodied AI

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