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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 ·
Llms

[D] Tested model routing on financial AI datasets — good savings and curious what benchmarks others use.

Ran a benchmark evaluating whether prompt complexity-based routing delivers meaningful savings. Used public HuggingFace datasets. Here's ...

Reddit - Machine Learning · 1 min ·

All Content

[2510.13582] ArtNet: Hierarchical Clustering-Based Artificial Netlist Generator for ML and DTCO Application
Machine Learning

[2510.13582] ArtNet: Hierarchical Clustering-Based Artificial Netlist Generator for ML and DTCO Application

ArtNet introduces a novel artificial netlist generator that enhances machine learning model generalization and design-technology co-optim...

arXiv - Machine Learning · 4 min ·
[2509.22138] Slicing Wasserstein Over Wasserstein Via Functional Optimal Transport
Data Science

[2509.22138] Slicing Wasserstein Over Wasserstein Via Functional Optimal Transport

This article presents a novel approach to the Wasserstein over Wasserstein (WoW) distance by introducing the double-sliced Wasserstein (D...

arXiv - Machine Learning · 4 min ·
[2509.04583] Instance-Wise Adaptive Sampling for Dataset Construction in Approximating Inverse Problem Solutions
Machine Learning

[2509.04583] Instance-Wise Adaptive Sampling for Dataset Construction in Approximating Inverse Problem Solutions

This article presents a novel instance-wise adaptive sampling framework designed to enhance the efficiency of training datasets for super...

arXiv - Machine Learning · 4 min ·
[2505.17786] Supervised Graph Contrastive Learning for Gene Regulatory Networks
Machine Learning

[2505.17786] Supervised Graph Contrastive Learning for Gene Regulatory Networks

This article presents SupGCL, a novel supervised graph contrastive learning method for gene regulatory networks, leveraging biological pe...

arXiv - Machine Learning · 4 min ·
[2502.00204] Nearly-Optimal Bandit Learning in Stackelberg Games with Side Information
Machine Learning

[2502.00204] Nearly-Optimal Bandit Learning in Stackelberg Games with Side Information

This paper presents algorithms for nearly-optimal bandit learning in Stackelberg games, achieving improved regret rates and extending app...

arXiv - Machine Learning · 4 min ·
[2409.12709] SeqRisk: Transformer-augmented latent variable model for robust survival prediction with longitudinal data
Machine Learning

[2409.12709] SeqRisk: Transformer-augmented latent variable model for robust survival prediction with longitudinal data

SeqRisk introduces a transformer-augmented latent variable model for enhanced survival prediction using longitudinal healthcare data, add...

arXiv - Machine Learning · 3 min ·
[2410.23029] Risk-Aware Decision Making in Restless Bandits: Theory and Algorithms for Planning and Learning
Machine Learning

[2410.23029] Risk-Aware Decision Making in Restless Bandits: Theory and Algorithms for Planning and Learning

This paper explores risk-aware decision-making in restless bandits, proposing new algorithms for planning and learning that incorporate r...

arXiv - Machine Learning · 4 min ·
[2405.11454] Gradient Testing and Estimation by Comparisons
Machine Learning

[2405.11454] Gradient Testing and Estimation by Comparisons

The paper presents algorithms for gradient testing and estimation using a comparison oracle, optimizing query efficiency for smooth funct...

arXiv - Machine Learning · 3 min ·
[2403.11332] Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects
Machine Learning

[2403.11332] Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects

This paper presents a novel methodology combining graph machine learning and double machine learning to estimate causal effects in social...

arXiv - Machine Learning · 4 min ·
[2402.00851] Data Augmentation Scheme for Raman Spectra with Highly Correlated Annotations
Machine Learning

[2402.00851] Data Augmentation Scheme for Raman Spectra with Highly Correlated Annotations

This article presents a data augmentation scheme for Raman spectra, enhancing model training by generating additional data points with in...

arXiv - Machine Learning · 4 min ·
[2602.17654] Mine and Refine: Optimizing Graded Relevance in E-commerce Search Retrieval
Machine Learning

[2602.17654] Mine and Refine: Optimizing Graded Relevance in E-commerce Search Retrieval

The paper presents a two-stage framework called 'Mine and Refine' for optimizing graded relevance in e-commerce search retrieval, enhanci...

arXiv - Machine Learning · 4 min ·
[2602.17587] Asymptotically Optimal Sequential Testing with Markovian Data
Machine Learning

[2602.17587] Asymptotically Optimal Sequential Testing with Markovian Data

This paper presents a novel approach to sequential hypothesis testing for Markovian data, establishing new lower bounds and proposing an ...

arXiv - Machine Learning · 3 min ·
[2602.17577] Simultaneous Blackwell Approachability and Applications to Multiclass Omniprediction
Machine Learning

[2602.17577] Simultaneous Blackwell Approachability and Applications to Multiclass Omniprediction

This paper explores the concept of omniprediction in a multiclass setting, extending existing algorithms to address suboptimality bounds ...

arXiv - Machine Learning · 3 min ·
[2602.17565] Optimal Unconstrained Self-Distillation in Ridge Regression: Strict Improvements, Precise Asymptotics, and One-Shot Tuning
Machine Learning

[2602.17565] Optimal Unconstrained Self-Distillation in Ridge Regression: Strict Improvements, Precise Asymptotics, and One-Shot Tuning

This paper explores optimal unconstrained self-distillation in ridge regression, demonstrating strict improvements in prediction risk and...

arXiv - Machine Learning · 4 min ·
[2602.17543] genriesz: A Python Package for Automatic Debiased Machine Learning with Generalized Riesz Regression
Machine Learning

[2602.17543] genriesz: A Python Package for Automatic Debiased Machine Learning with Generalized Riesz Regression

The article presents 'genriesz', an open-source Python package designed for automatic debiased machine learning using generalized Riesz r...

arXiv - Machine Learning · 4 min ·
[2602.17314] Open Datasets in Learning Analytics: Trends, Challenges, and Best PRACTICE
Data Science

[2602.17314] Open Datasets in Learning Analytics: Trends, Challenges, and Best PRACTICE

This article surveys open datasets in learning analytics, identifying trends, challenges, and best practices to enhance research reproduc...

arXiv - Machine Learning · 4 min ·
[2602.17346] Partial Optimality in the Preordering Problem
Data Science

[2602.17346] Partial Optimality in the Preordering Problem

This paper explores the preordering problem, a complex issue in discrete mathematics, presenting new conditions and algorithms to enhance...

arXiv - Machine Learning · 3 min ·
[2602.17287] Representation Collapse in Machine Translation Through the Lens of Angular Dispersion
Machine Learning

[2602.17287] Representation Collapse in Machine Translation Through the Lens of Angular Dispersion

This paper explores representation collapse in neural machine translation models, particularly focusing on the Transformer architecture a...

arXiv - Machine Learning · 3 min ·
[2602.17211] MGD: Moment Guided Diffusion for Maximum Entropy Generation
Machine Learning

[2602.17211] MGD: Moment Guided Diffusion for Maximum Entropy Generation

The paper introduces Moment Guided Diffusion (MGD), a novel method for generating maximum entropy distributions by guiding moments toward...

arXiv - Machine Learning · 3 min ·
[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 ·
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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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