GPUs, training clusters, MLOps, and deployment
UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...
![[2604.07486] Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
Abstract page for arXiv paper 2604.07486: Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation
Abstract page for arXiv paper 2601.14477: XD-MAP: Cross-Modal Domain Adaptation via Semantic Parametric Maps for Scalable Training Data G...
The paper presents Evolutionary Context Search (ECS), a novel method for automated skill acquisition in large language models, enhancing ...
The paper presents LGQ, a novel image tokenizer that learns discretization geometry to enhance scalability and stability in visual genera...
The paper introduces Cross-Layer Attention Aggregation (CLAA) to enhance the efficiency of long-context LLM inference by addressing token...
The paper presents Federated Split Decision Transformers (FSDT) for optimizing resource allocation in mobile edge computing for the metav...
This paper explores saddle point reformulation in Vertical Federated Learning (VFL), presenting methods for efficient model training acro...
The paper discusses the phenomenon of 'Retrieval Collapse,' where AI-generated content dominates search results, leading to a decline in ...
The paper presents a novel approach called MultiFaceted Learnable Index (MFLI) for enhancing ANN-based retrieval in large-scale recommend...
This paper presents Quality-constrained Entropy Maximization Policy Optimization (QEMPO), a method to enhance diversity in large language...
This paper presents NOMAD, a novel approach for training autonomous vehicles to navigate new cities without relying on human driving demo...
The paper introduces ODYN, a novel non-interior-point method for quadratic programming, designed for efficiency in robotics and AI applic...
The paper introduces P-RAG, a novel hybrid architecture that enhances Retrieval-Augmented Generation (RAG) for biomedical question answer...
This paper explores the design choices of Model Context Protocols (MCPs) and introduces Code Execution MCP (CE-MCP) as a solution to scal...
This paper discusses the importance of causality in interpretability research for large language models, highlighting pitfalls in general...
The paper presents a method for assessing privacy vulnerability in machine learning models using a generalized leverage score, enabling e...
The paper presents Doc-to-LoRA, a hypernetwork that enables Large Language Models to internalize contexts efficiently, reducing memory us...
The paper presents a novel approach to Membership Inference Attacks (MIAs) by developing an optimal attack strategy, SeMI*, leveraging mo...
This article investigates the temporal variability in the performance of the GPT-4o model, revealing significant daily and weekly pattern...
NeuroSleep presents a neuromorphic event-driven system for efficient EEG sleep staging, achieving high accuracy with reduced computationa...
This article presents a detailed analysis of sampling from reward-tilted diffusion models, focusing on quadratic rewards and their comput...
This paper presents a novel framework for improving recipe generation from food images by enhancing action and ingredient modeling, addre...
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