GPUs, training clusters, MLOps, and deployment
Asking from a technical standpoint because I feel like the term is doing a lot of work in coverage of this space right now. Genuine real-...
I recently updated my FlashAttention-PyTorch repo so it now includes educational implementations of FA1, FA2, FA3, and FA4 in plain PyTor...
UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...
![[2410.11855] Online GPU Energy Optimization with Switching-Aware Bandits](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
This paper presents EnergyUCB, a novel online GPU energy optimization method using a multi-armed bandit approach to balance performance a...
The paper presents a novel approach to Text-to-SQL systems by introducing dynamic workflows that adapt during inference, enhancing perfor...
This paper presents a non-intrusive data-driven model order reduction method for circuits using Hammerstein architectures, demonstrating ...
The paper introduces VLM-DEWM, a novel cognitive architecture designed to enhance vision-language planning in manufacturing by addressing...
The paper presents Horizon Imagination (HI), an innovative on-policy imagination process for reinforcement learning using diffusion-based...
The paper presents Shape-Gain Decomposition for Neural Audio Codecs, enhancing bitrate-distortion performance and reducing complexity by ...
Green-NAS presents a multi-objective neural architecture search framework aimed at optimizing weather forecasting models for low-resource...
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 ...
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