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...
![[2604.07823] LPM 1.0: Video-based Character Performance Model](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
Abstract page for arXiv paper 2604.07823: LPM 1.0: Video-based Character Performance Model
Abstract page for arXiv paper 2603.08639: UNBOX: Unveiling Black-box visual models with Natural-language
This paper explores the efficiency of offline policy selection (OPS) in reinforcement learning, connecting it to off-policy evaluation (O...
The paper introduces a novel regression algorithm called Learning with Subset Stacking (LESS), which effectively learns from heterogeneou...
Orcheo is an open-source platform designed to streamline conversational search by offering a modular architecture, production-ready infra...
The paper presents Qute, a quantum-native database that integrates quantum computation into database operations, enhancing performance ov...
LLMStructBench introduces a benchmark for evaluating large language models on structured data extraction, emphasizing the impact of promp...
The paper presents CoCoDiff, a novel framework for fine-grained style transfer in images, emphasizing semantic correspondence and achievi...
This article presents an adaptation of VACE for real-time autoregressive video generation, enhancing video control while addressing laten...
The paper presents DP-KSA, a novel algorithm that integrates differential privacy into retrieval-augmented generation (RAG) systems, addr...
The paper presents a game-theoretic framework called Socially-Weighted Alignment (SWA) for managing multi-agent large language model (LLM...
The paper presents LRD-MPC, a method that enhances the efficiency of secure multi-party computation (MPC) in machine learning by utilizin...
The paper presents Floe, a federated learning framework that enhances real-time inference of large language models (LLMs) while addressin...
The paper presents MILD, a proactive framework for failure prediction in intent-based networking, enhancing root-cause intent disambiguat...
The paper presents the Scale Mixture EM (SM-EM) algorithm for optimizing machine learning losses, demonstrating significant performance i...
This article explores the use of Pinching Antenna Systems (PASSs) to enhance energy efficiency in over-the-air federated learning, presen...
The paper presents STATe-of-Thoughts, a new method for improving output diversity and interpretability in inference-time compute methods,...
This paper presents a novel framework for Federated Ensemble Learning that enhances model personalization while addressing statistical he...
The paper presents Sali-Cache, a novel optimization framework for Vision-Language Models (VLMs) that addresses memory bottlenecks in long...
The paper introduces the Gaussian Thought Sampler (GTS), a novel approach to inference-time scaling in latent reasoning models, enhancing...
The paper presents Spherical Barycentric Aggregation (SBA), a new method for aggregating outputs in Mixture-of-Experts (MoE) embedding mo...
This article presents a multi-scale agentic AI framework for Open Radio Access Networks (O-RAN), enhancing real-time network control and ...
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