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...
![[2411.18954] NeuroLifting: Neural Inference on Markov Random Fields at Scale](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
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...
The paper presents ExpertWeaver, a framework that enhances the conversion of dense LLMs into sparse Mixture-of-Experts (MoE) models using...
This article introduces a novel operator learning method for incompressible flows, enhancing computational efficiency while preserving es...
This article discusses the integration of accelerated computing (AC) and artificial intelligence (AI) in computational lithography, highl...
This article explores Ankara's public transport crisis, attributing it to structural issues rather than mere inefficiencies. It highlight...
GaiaFlow presents a novel framework for carbon-efficient search, employing semantic-guided diffusion tuning to balance retrieval accuracy...
This article discusses the use of large language models (LLMs) as synthetic participants in social science experiments, evaluating their ...
The paper presents FlashMem, a memory streaming framework designed to optimize the execution of large-scale deep neural networks (DNNs) o...
The paper introduces PERSONA, a novel framework for dynamic personality control in Large Language Models (LLMs) using activation vector a...
The paper presents SCENE, a novel estimator for over-the-air federated distillation that enhances aggregation without requiring pilot sig...
This paper presents Exploration-Exploitation Distillation (E^2D), a method for efficient large-scale dataset distillation that balances a...
The paper presents a novel adaptive abstention system for Large Language Models (LLMs) that balances safety and utility by dynamically ad...
This paper presents the Layer Smoothing Attack (LSA), a novel backdoor attack exploiting layer-specific vulnerabilities in federated lear...
The paper explores how a pretrained transformer can effectively solve empirical Bayes problems by leveraging universal priors, demonstrat...
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