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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 ·
Accelerating science with AI and simulations
Machine Learning

Accelerating science with AI and simulations

MIT Professor Rafael Gómez-Bombarelli discusses the transformative potential of AI in scientific research, emphasizing its role in materi...

AI News - General · 10 min ·
[2512.24420] Virasoro Symmetry in Neural Network Field Theories
Machine Learning

[2512.24420] Virasoro Symmetry in Neural Network Field Theories

Abstract page for arXiv paper 2512.24420: Virasoro Symmetry in Neural Network Field Theories

arXiv - Machine Learning · 3 min ·

All Content

[2602.21415] Benchmarking State Space Models, Transformers, and Recurrent Networks for US Grid Forecasting
Machine Learning

[2602.21415] Benchmarking State Space Models, Transformers, and Recurrent Networks for US Grid Forecasting

This paper benchmarks various deep learning models for forecasting electricity demand across US power grids, revealing no single best mod...

arXiv - Machine Learning · 4 min ·
[2602.21342] Archetypal Graph Generative Models: Explainable and Identifiable Communities via Anchor-Dominant Convex Hulls
Machine Learning

[2602.21342] Archetypal Graph Generative Models: Explainable and Identifiable Communities via Anchor-Dominant Convex Hulls

The paper introduces GraphHull, an explainable generative model for graph representation learning, enhancing community detection and link...

arXiv - Machine Learning · 4 min ·
[2602.21340] HiPPO Zoo: Explicit Memory Mechanisms for Interpretable State Space Models
Machine Learning

[2602.21340] HiPPO Zoo: Explicit Memory Mechanisms for Interpretable State Space Models

The paper introduces the HiPPO Zoo, a framework enhancing state space models with explicit memory mechanisms for improved interpretabilit...

arXiv - Machine Learning · 4 min ·
[2602.21319] Uncertainty-Aware Diffusion Model for Multimodal Highway Trajectory Prediction via DDIM Sampling
Machine Learning

[2602.21319] Uncertainty-Aware Diffusion Model for Multimodal Highway Trajectory Prediction via DDIM Sampling

The paper presents cVMDx, an advanced diffusion model for multimodal highway trajectory prediction, enhancing efficiency and accuracy in ...

arXiv - Machine Learning · 3 min ·
[2602.00012] OGD4All: A Framework for Accessible Interaction with Geospatial Open Government Data Based on Large Language Models
Llms

[2602.00012] OGD4All: A Framework for Accessible Interaction with Geospatial Open Government Data Based on Large Language Models

The OGD4All framework enhances citizen interaction with geospatial Open Government Data using Large Language Models, achieving high accur...

arXiv - Machine Learning · 3 min ·
[2601.08026] FigEx2: Visual-Conditioned Panel Detection and Captioning for Scientific Compound Figures
Computer Vision

[2601.08026] FigEx2: Visual-Conditioned Panel Detection and Captioning for Scientific Compound Figures

The paper presents FigEx2, a framework for detecting and captioning panels in scientific compound figures, enhancing understanding and ac...

arXiv - AI · 4 min ·
[2511.00129] Data-Augmented Deep Learning for Downhole Depth Sensing and Validation
Machine Learning

[2511.00129] Data-Augmented Deep Learning for Downhole Depth Sensing and Validation

This paper presents a novel approach to downhole depth sensing using data-augmented deep learning techniques, addressing challenges in da...

arXiv - Machine Learning · 4 min ·
[2510.10472] FML-bench: Benchmarking Machine Learning Agents for Scientific Research
Llms

[2510.10472] FML-bench: Benchmarking Machine Learning Agents for Scientific Research

The paper introduces FML-bench, a new benchmark for evaluating machine learning agents in scientific research, focusing on exploration di...

arXiv - AI · 4 min ·
[2510.16071] MNO: Multiscale Neural Operator for 3D Computational Fluid Dynamics
Machine Learning

[2510.16071] MNO: Multiscale Neural Operator for 3D Computational Fluid Dynamics

The paper presents the Multiscale Neural Operator (MNO), a novel architecture designed for 3D computational fluid dynamics, enhancing acc...

arXiv - Machine Learning · 4 min ·
[2510.00024] EpidemIQs: Prompt-to-Paper LLM Agents for Epidemic Modeling and Analysis
Llms

[2510.00024] EpidemIQs: Prompt-to-Paper LLM Agents for Epidemic Modeling and Analysis

The paper presents EpidemIQs, a multi-agent framework utilizing large language models for efficient epidemic modeling, demonstrating impr...

arXiv - AI · 4 min ·
[2509.23597] Characteristic Root Analysis and Regularization for Linear Time Series Forecasting
Machine Learning

[2509.23597] Characteristic Root Analysis and Regularization for Linear Time Series Forecasting

This paper explores the effectiveness of linear models for time series forecasting, focusing on characteristic roots and their impact on ...

arXiv - Machine Learning · 4 min ·
[2509.02452] Do LLMs Adhere to Label Definitions? Examining Their Receptivity to External Label Definitions
Llms

[2509.02452] Do LLMs Adhere to Label Definitions? Examining Their Receptivity to External Label Definitions

This article investigates whether large language models (LLMs) adhere to external label definitions or rely on internal representations, ...

arXiv - Machine Learning · 3 min ·
[2508.08337] Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants
Machine Learning

[2508.08337] Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants

This paper argues for a shift in machine learning fairness research to focus on structural injustice through social determinants, rather ...

arXiv - Machine Learning · 4 min ·
[2507.14206] A Comprehensive Benchmark for Electrocardiogram Time-Series
Machine Learning

[2507.14206] A Comprehensive Benchmark for Electrocardiogram Time-Series

This article presents a comprehensive benchmark for electrocardiogram (ECG) time-series analysis, highlighting its unique characteristics...

arXiv - Machine Learning · 4 min ·
[2506.01085] Learning What Matters: Prioritized Concept Learning via Relative Error-driven Sample Selection
Llms

[2506.01085] Learning What Matters: Prioritized Concept Learning via Relative Error-driven Sample Selection

The paper presents PROGRESS, a framework for prioritized concept learning in vision-language models, enabling efficient sample selection ...

arXiv - AI · 4 min ·
[2504.06533] Rethinking Flexible Graph Similarity Computation: One-step Alignment with Global Guidance
Machine Learning

[2504.06533] Rethinking Flexible Graph Similarity Computation: One-step Alignment with Global Guidance

The paper presents a novel approach to graph similarity computation through the Graph Edit Network (GEN), which integrates cost-aware est...

arXiv - Machine Learning · 4 min ·
[2502.14183] Glycemic-Aware and Architecture-Agnostic Training Framework for Blood Glucose Forecasting in Type 1 Diabetes
Machine Learning

[2502.14183] Glycemic-Aware and Architecture-Agnostic Training Framework for Blood Glucose Forecasting in Type 1 Diabetes

The paper presents GLIMMER, a novel training framework for predicting blood glucose levels in Type 1 Diabetes, emphasizing accuracy in dy...

arXiv - Machine Learning · 4 min ·
[2503.01927] QCS-ADME: Quantum Circuit Search for Drug Property Prediction with Imbalanced Data and Regression Adaptation
Machine Learning

[2503.01927] QCS-ADME: Quantum Circuit Search for Drug Property Prediction with Imbalanced Data and Regression Adaptation

The paper presents QCS-ADME, a novel quantum circuit search framework for predicting drug properties, addressing challenges in imbalanced...

arXiv - Machine Learning · 4 min ·
[2411.03941] Modular Deep Learning for Multivariate Time-Series: Decoupling Imputation and Downstream Tasks
Machine Learning

[2411.03941] Modular Deep Learning for Multivariate Time-Series: Decoupling Imputation and Downstream Tasks

This paper proposes a modular approach to deep learning for multivariate time-series data, separating imputation from downstream tasks to...

arXiv - Machine Learning · 4 min ·
[2407.15738] Parallel Split Learning with Global Sampling
Machine Learning

[2407.15738] Parallel Split Learning with Global Sampling

The paper presents a novel server-driven sampling strategy for distributed deep learning, enhancing scalability and accuracy in resource-...

arXiv - Machine Learning · 3 min ·
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