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Llms

[R] Hybrid attention for small code models: 50x faster inference, but data scaling still dominates

TLDR: Forked pytorch and triton internals . Changed attention so its linear first layer , middle quadratic layer, last linear layer Infer...

Reddit - Machine Learning · 1 min ·
UMKC Announces New Master of Science in Artificial Intelligence
Ai Infrastructure

UMKC Announces New Master of Science in Artificial Intelligence

UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...

AI News - General · 4 min ·
Google quietly launched an AI dictation app that works offline
Machine Learning

Google quietly launched an AI dictation app that works offline

Google's new offline-first dictation app uses Gemma AI models to take on the apps like Wispr Flow.

TechCrunch - AI · 4 min ·

All Content

[2602.17187] Anti-causal domain generalization: Leveraging unlabeled data
Machine Learning

[2602.17187] Anti-causal domain generalization: Leveraging unlabeled data

The paper explores anti-causal domain generalization, proposing methods to leverage unlabeled data for robust predictive modeling in vary...

arXiv - Machine Learning · 3 min ·
[2602.17115] Semi-Supervised Learning on Graphs using Graph Neural Networks
Machine Learning

[2602.17115] Semi-Supervised Learning on Graphs using Graph Neural Networks

The paper explores the effectiveness of Graph Neural Networks (GNNs) in semi-supervised learning, providing theoretical insights and empi...

arXiv - Machine Learning · 3 min ·
[2602.17104] Simplify to Amplify: Achieving Information-Theoretic Bounds with Fewer Steps in Spectral Community Detection
Machine Learning

[2602.17104] Simplify to Amplify: Achieving Information-Theoretic Bounds with Fewer Steps in Spectral Community Detection

This paper presents a streamlined spectral algorithm for community detection in the stochastic block model, achieving improved error boun...

arXiv - Machine Learning · 3 min ·
[2602.17036] LiveGraph: Active-Structure Neural Re-ranking for Exercise Recommendation
Machine Learning

[2602.17036] LiveGraph: Active-Structure Neural Re-ranking for Exercise Recommendation

The paper presents LiveGraph, a novel neural re-ranking framework aimed at improving exercise recommendations by addressing student engag...

arXiv - Machine Learning · 3 min ·
[2602.16979] Characterizing the Predictive Impact of Modalities with Supervised Latent-Variable Modeling
Llms

[2602.16979] Characterizing the Predictive Impact of Modalities with Supervised Latent-Variable Modeling

The paper presents PRIMO, a supervised latent-variable model that addresses the challenges of incomplete multimodal data by quantifying t...

arXiv - Machine Learning · 4 min ·
[2602.16951] BrainRVQ: A High-Fidelity EEG Foundation Model via Dual-Domain Residual Quantization and Hierarchical Autoregression
Llms

[2602.16951] BrainRVQ: A High-Fidelity EEG Foundation Model via Dual-Domain Residual Quantization and Hierarchical Autoregression

The paper presents BrainRVQ, a high-fidelity EEG foundation model that utilizes Dual-Domain Residual Quantization and Hierarchical Autore...

arXiv - Machine Learning · 3 min ·
[2602.16923] Poisson-MNL Bandit: Nearly Optimal Dynamic Joint Assortment and Pricing with Decision-Dependent Customer Arrivals
Machine Learning

[2602.16923] Poisson-MNL Bandit: Nearly Optimal Dynamic Joint Assortment and Pricing with Decision-Dependent Customer Arrivals

The paper presents the Poisson-MNL model for dynamic joint assortment and pricing, addressing customer arrival dependencies to optimize r...

arXiv - Machine Learning · 3 min ·
[2602.16914] A statistical perspective on transformers for small longitudinal cohort data
Machine Learning

[2602.16914] A statistical perspective on transformers for small longitudinal cohort data

This paper presents a simplified transformer architecture tailored for small longitudinal cohort data, enhancing predictive performance w...

arXiv - Machine Learning · 4 min ·
[2602.16908] Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials on organic and inorganic compounds
Machine Learning

[2602.16908] Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials on organic and inorganic compounds

This article presents a study on the multi-objective optimization of deep learning interatomic potentials, focusing on the trade-off betw...

arXiv - Machine Learning · 3 min ·
[2602.16830] The Impact of Formations on Football Matches Using Double Machine Learning. Is it worth parking the bus?
Machine Learning

[2602.16830] The Impact of Formations on Football Matches Using Double Machine Learning. Is it worth parking the bus?

This study explores the impact of football formations on match outcomes using Double Machine Learning, questioning the effectiveness of d...

arXiv - Machine Learning · 4 min ·
[2602.16749] U-FedTomAtt: Ultra-lightweight Federated Learning with Attention for Tomato Disease Recognition
Machine Learning

[2602.16749] U-FedTomAtt: Ultra-lightweight Federated Learning with Attention for Tomato Disease Recognition

The paper presents U-FedTomAtt, an ultra-lightweight federated learning framework designed for tomato disease recognition, optimizing per...

arXiv - Machine Learning · 4 min ·
[2602.16794] Beyond Procedure: Substantive Fairness in Conformal Prediction
Machine Learning

[2602.16794] Beyond Procedure: Substantive Fairness in Conformal Prediction

This paper explores substantive fairness in conformal prediction, analyzing its impact on downstream decision-making and proposing method...

arXiv - Machine Learning · 3 min ·
[2602.16738] Self-Evolving Multi-Agent Network for Industrial IoT Predictive Maintenance
Llms

[2602.16738] Self-Evolving Multi-Agent Network for Industrial IoT Predictive Maintenance

The paper presents SEMAS, a self-evolving multi-agent network designed for predictive maintenance in Industrial IoT, enhancing real-time ...

arXiv - Machine Learning · 4 min ·
[2602.16737] Exploring the Utility of MALDI-TOF Mass Spectrometry and Antimicrobial Resistance in Hospital Outbreak Detection
Data Science

[2602.16737] Exploring the Utility of MALDI-TOF Mass Spectrometry and Antimicrobial Resistance in Hospital Outbreak Detection

This article explores the use of MALDI-TOF mass spectrometry and antimicrobial resistance patterns as cost-effective alternatives to whol...

arXiv - Machine Learning · 3 min ·
[2602.17625] Catastrophic Forgetting Resilient One-Shot Incremental Federated Learning
Machine Learning

[2602.17625] Catastrophic Forgetting Resilient One-Shot Incremental Federated Learning

This paper introduces One-Shot Incremental Federated Learning (OSI-FL), a novel framework that mitigates catastrophic forgetting and comm...

arXiv - Machine Learning · 4 min ·
[2602.17614] Guarding the Middle: Protecting Intermediate Representations in Federated Split Learning
Machine Learning

[2602.17614] Guarding the Middle: Protecting Intermediate Representations in Federated Split Learning

This paper presents KD-UFSL, a method to enhance privacy in federated split learning by minimizing data leakage through intermediate repr...

arXiv - Machine Learning · 4 min ·
[2602.17584] Canonicalizing Multimodal Contrastive Representation Learning
Machine Learning

[2602.17584] Canonicalizing Multimodal Contrastive Representation Learning

This article explores the geometric relationships between independently trained multimodal contrastive models, revealing that an orthogon...

arXiv - Machine Learning · 4 min ·
[2602.17559] Revisiting Weight Regularization for Low-Rank Continual Learning
Machine Learning

[2602.17559] Revisiting Weight Regularization for Low-Rank Continual Learning

This paper explores weight regularization techniques in low-rank continual learning, proposing EWC-LoRA to mitigate task interference whi...

arXiv - Machine Learning · 4 min ·
[2602.17554] A Theoretical Framework for Modular Learning of Robust Generative Models
Llms

[2602.17554] A Theoretical Framework for Modular Learning of Robust Generative Models

This article presents a theoretical framework for modular learning in robust generative models, exploring the combination of domain-speci...

arXiv - Machine Learning · 4 min ·
[2602.17530] Provably Explaining Neural Additive Models
Machine Learning

[2602.17530] Provably Explaining Neural Additive Models

The paper presents a novel algorithm for generating provably minimal explanations for Neural Additive Models (NAMs), improving efficiency...

arXiv - Machine Learning · 4 min ·
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