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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 ·
[2603.13793] GhanaNLP Parallel Corpora: Comprehensive Multilingual Resources for Low-Resource Ghanaian Languages
Nlp

[2603.13793] GhanaNLP Parallel Corpora: Comprehensive Multilingual Resources for Low-Resource Ghanaian Languages

Abstract page for arXiv paper 2603.13793: GhanaNLP Parallel Corpora: Comprehensive Multilingual Resources for Low-Resource Ghanaian Langu...

arXiv - AI · 4 min ·

All Content

[2602.22236] CrossLLM-Mamba: Multimodal State Space Fusion of LLMs for RNA Interaction Prediction
Llms

[2602.22236] CrossLLM-Mamba: Multimodal State Space Fusion of LLMs for RNA Interaction Prediction

The article presents CrossLLM-Mamba, a novel framework for RNA interaction prediction that utilizes multimodal state space fusion of larg...

arXiv - Machine Learning · 4 min ·
[2602.22299] Decoding the Hook: A Multimodal LLM Framework for Analyzing the Hooking Period of Video Ads
Llms

[2602.22299] Decoding the Hook: A Multimodal LLM Framework for Analyzing the Hooking Period of Video Ads

This article presents a framework using multimodal large language models (MLLMs) to analyze the 'hooking period' of video ads, focusing o...

arXiv - Machine Learning · 4 min ·
[2602.22223] SQaLe: A Large Text-to-SQL Corpus Grounded in Real Schemas
Llms

[2602.22223] SQaLe: A Large Text-to-SQL Corpus Grounded in Real Schemas

The paper introduces SQaLe, a large-scale text-to-SQL dataset designed to enhance the development of models that convert natural language...

arXiv - Machine Learning · 3 min ·
[2602.22279] Learning to reconstruct from saturated data: audio declipping and high-dynamic range imaging
Machine Learning

[2602.22279] Learning to reconstruct from saturated data: audio declipping and high-dynamic range imaging

This paper presents a novel approach to reconstruct audio and images from clipped measurements using self-supervised learning, addressing...

arXiv - AI · 3 min ·
[2602.23360] Model Agreement via Anchoring
Machine Learning

[2602.23360] Model Agreement via Anchoring

The paper presents a method for reducing model disagreement in machine learning by using an anchoring technique, demonstrating its effect...

arXiv - AI · 4 min ·
[2602.23358] A Dataset is Worth 1 MB
Machine Learning

[2602.23358] A Dataset is Worth 1 MB

The paper presents PLADA, a novel method for efficient dataset transmission in machine learning, significantly reducing payload size whil...

arXiv - Machine Learning · 4 min ·
[2602.23349] FlashOptim: Optimizers for Memory Efficient Training
Machine Learning

[2602.23349] FlashOptim: Optimizers for Memory Efficient Training

FlashOptim introduces innovative optimizers that significantly reduce memory usage in neural network training, enhancing efficiency witho...

arXiv - AI · 4 min ·
[2602.22263] CryoNet.Refine: A One-step Diffusion Model for Rapid Refinement of Structural Models with Cryo-EM Density Map Restraints
Machine Learning

[2602.22263] CryoNet.Refine: A One-step Diffusion Model for Rapid Refinement of Structural Models with Cryo-EM Density Map Restraints

CryoNet.Refine introduces a one-step diffusion model for efficiently refining structural models using cryo-EM density maps, offering a si...

arXiv - AI · 4 min ·
[2602.22258] Poisoned Acoustics
Machine Learning

[2602.22258] Poisoned Acoustics

The paper 'Poisoned Acoustics' explores training-data poisoning attacks on deep neural networks, demonstrating significant vulnerabilitie...

arXiv - AI · 3 min ·
[2602.23341] Mean Estimation from Coarse Data: Characterizations and Efficient Algorithms
Machine Learning

[2602.23341] Mean Estimation from Coarse Data: Characterizations and Efficient Algorithms

This article presents efficient algorithms for estimating the mean from coarse data, addressing key questions in Gaussian mean estimation...

arXiv - Machine Learning · 4 min ·
[2602.23336] Differentiable Zero-One Loss via Hypersimplex Projections
Machine Learning

[2602.23336] Differentiable Zero-One Loss via Hypersimplex Projections

This paper presents a novel differentiable approximation to the zero-one loss, enhancing gradient-based optimization in machine learning ...

arXiv - Machine Learning · 3 min ·
[2602.23305] A Proper Scoring Rule for Virtual Staining
Machine Learning

[2602.23305] A Proper Scoring Rule for Virtual Staining

The paper introduces a novel scoring rule for evaluating generative virtual staining models in high-throughput screening, emphasizing the...

arXiv - Machine Learning · 3 min ·
[2602.23303] Inferential Mechanics Part 1: Causal Mechanistic Theories of Machine Learning in Chemical Biology with Implications
Machine Learning

[2602.23303] Inferential Mechanics Part 1: Causal Mechanistic Theories of Machine Learning in Chemical Biology with Implications

This article introduces 'Inferential Mechanics,' a framework combining causal theories with machine learning in chemical biology, address...

arXiv - Machine Learning · 4 min ·
[2602.22247] Multi-Dimensional Spectral Geometry of Biological Knowledge in Single-Cell Transformer Representations
Llms

[2602.22247] Multi-Dimensional Spectral Geometry of Biological Knowledge in Single-Cell Transformer Representations

This article explores how single-cell foundation models like scGPT encode biological knowledge through high-dimensional gene representati...

arXiv - Machine Learning · 4 min ·
[2602.23296] Conformalized Neural Networks for Federated Uncertainty Quantification under Dual Heterogeneity
Machine Learning

[2602.23296] Conformalized Neural Networks for Federated Uncertainty Quantification under Dual Heterogeneity

This article presents FedWQ-CP, a novel approach to federated uncertainty quantification that addresses dual heterogeneity in data and mo...

arXiv - Machine Learning · 4 min ·
[2602.23280] Physics Informed Viscous Value Representations
Nlp

[2602.23280] Physics Informed Viscous Value Representations

This paper presents a novel approach to offline goal-conditioned reinforcement learning by introducing a physics-informed regularization ...

arXiv - Machine Learning · 3 min ·
[2602.23219] Takeuchi's Information Criteria as Generalization Measures for DNNs Close to NTK Regime
Machine Learning

[2602.23219] Takeuchi's Information Criteria as Generalization Measures for DNNs Close to NTK Regime

This paper investigates Takeuchi's Information Criterion (TIC) as a measure for generalization in deep neural networks (DNNs) near the ne...

arXiv - Machine Learning · 4 min ·
[2602.22237] Optimized Disaster Recovery for Distributed Storage Systems: Lightweight Metadata Architectures to Overcome Cryptographic Hashing Bottleneck
Nlp

[2602.22237] Optimized Disaster Recovery for Distributed Storage Systems: Lightweight Metadata Architectures to Overcome Cryptographic Hashing Bottleneck

This paper presents a novel approach to disaster recovery in distributed storage systems, addressing the limitations of cryptographic has...

arXiv - AI · 3 min ·
[2602.22235] Unsupervised Denoising of Diffusion-Weighted Images with Bias and Variance Corrected Noise Modeling
Machine Learning

[2602.22235] Unsupervised Denoising of Diffusion-Weighted Images with Bias and Variance Corrected Noise Modeling

This article presents a novel approach for unsupervised denoising of diffusion-weighted images (dMRI) by addressing noise bias and varian...

arXiv - AI · 4 min ·
[2602.23188] Efficient Real-Time Adaptation of ROMs for Unsteady Flows Using Data Assimilation
Machine Learning

[2602.23188] Efficient Real-Time Adaptation of ROMs for Unsteady Flows Using Data Assimilation

This article presents a novel retraining strategy for Reduced Order Models (ROMs) that enhances real-time adaptation for unsteady flows u...

arXiv - Machine Learning · 4 min ·
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