GPUs, training clusters, MLOps, and deployment
![[2507.17506] Joint Multi-Target Detection-Tracking in Cognitive Massive MIMO Radar via POMCP](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
Abstract page for arXiv paper 2507.17506: Joint Multi-Target Detection-Tracking in Cognitive Massive MIMO Radar via POMCP
Abstract page for arXiv paper 2504.00890: Privacy-Preserving Transfer Learning for Community Detection using Locally Distributed Multiple...
Abstract page for arXiv paper 2510.00310: Robust Federated Inference
This paper presents an orchestration-free framework for customer service automation, utilizing Task-Oriented Flowcharts (TOFs) to enhance...
This study explores enhancements to Variational Autoencoders (VAEs) using Random Fourier Transformation (RFT) for anomaly detection in av...
The paper presents BEP, a novel Binary Error Propagation algorithm for training Binary Neural Networks (BNNs) that enables efficient back...
The paper presents the Error Correction Syndrome-Flow Consistency Model (ECCFM), which enhances one-step denoising error correction codes...
The paper presents NeuroSymActive, a novel framework for Knowledge Graph Question Answering that integrates differentiable neural-symboli...
The paper introduces Sparrow, a novel framework designed to enhance speculative decoding in Video Large Language Models (Vid-LLMs) by opt...
This article evaluates uncertainty estimates in binary classification models, comparing six probabilistic machine learning algorithms to ...
The paper presents AI-Paging, a framework for optimizing AI-as-a-Service by enabling network providers to manage model selection and exec...
This paper presents a framework for high-fidelity network management in Federated AI-as-a-Service, focusing on cross-domain orchestration...
This paper presents a novel method for identifying errors in stepwise reasoning using latent veracity inference, enhancing the reliabilit...
The paper introduces Qronos, a novel post-training quantization algorithm that enhances neural network performance by correcting quantiza...
This study analyzes AI research production across European regions at the NUTS-3 level, highlighting the specialization of peripheral reg...
This paper explores the integration of Generative AI in computing systems, identifying recurring challenges and design principles across ...
NeuroLifting introduces a novel approach for inference in large-scale Markov Random Fields (MRFs) using Graph Neural Networks, achieving ...
The paper introduces OpaqueToolsBench, a benchmark for evaluating Large Language Model (LLM) agents' performance with opaque tools, propo...
This paper discusses the limitations of layerwise approximate verification in neural inference, presenting a counterexample that challeng...
This article presents a novel approach to implementing low-latency machine learning on radiation-hard FPGAs, demonstrating its applicatio...
This article presents a novel real-time conversational assistant that utilizes audio and IMU data to guide users through procedural tasks...
The paper presents Safe-SDL, a framework for ensuring safety in AI-driven Self-Driving Laboratories, addressing the critical 'Syntax-to-S...
The paper introduces the Agent Communication Protocol (ACP), a framework for secure and efficient agent-to-agent orchestration, addressin...
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