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...
TLDR: Nvidia is partnering with 17 major companies to build a platform specifically for enterprise AI agents, basically trying to become ...
I keep seeing people focus heavily on prompt optimization. But in practice, a lot of failures I’ve observed don’t come from the prompt it...
The Department of Education (DepEd) in the Philippines has issued guidelines allowing the responsible use of artificial intelligence (AI)...
The University of Alberta welcomes a leading expert in medical AI as the new Killam Memorial Chair, enhancing its research capabilities i...
Mark Schaan discusses how Canada's higher education system is integral to the country's success in artificial intelligence, emphasizing t...
Nova Systems partners with RMIT University to enhance AI training and research, focusing on modelling and simulation for Defence professi...
This article discusses building a simple 2-layer MNIST MLP using Apple's metal-cpp library, focusing on GPU programming and performance o...
The article discusses the clash between AI regulation advocates and corporate interests, highlighting Pete Hegseth's role in opposing sen...
Anthropic's AI chatbot, Claude, experienced a brief outage affecting thousands of users. The company reported a fix was deployed shortly ...
![[2602.09206] EExApp: GNN-Based Reinforcement Learning for Radio Unit Energy Optimization in 5G O-RAN](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
The paper presents EExApp, a GNN-based reinforcement learning application designed to optimize energy consumption in 5G Open Radio Access...
This article presents a novel approach to likelihood-free inference (LFI) in robotics, addressing the issue of potentially misspecified d...
The paper presents ATLAS, a study on adaptive transfer scaling laws for multilingual pretraining, finetuning, and decoding, based on exte...
This paper explores the concept of copyright protection for generative models, introducing a framework that defines conditions under whic...
This article presents a novel framework for adapting object-centric agents in manipulating deformable linear objects using visual percept...
The paper presents PipeRec, a hardware-accelerated ETL engine designed to enhance the efficiency of recommender model training by integra...
The paper presents QTALE, a framework that integrates token-adaptive layer execution with quantization for large language models, improvi...
The paper presents BRIDGE, a framework for improving program synthesis through structured prompting, enhancing correctness and efficiency...
The paper presents NRGPT, an energy-based alternative to GPT, proposing a novel approach that integrates energy-based modeling with langu...
The paper introduces ImpMIA, a novel Membership Inference Attack that leverages implicit bias in neural networks to identify training sam...
This paper explores in-training compression techniques for State Space Models (SSMs), demonstrating how selective dimension preservation ...
This article presents a novel approach to merging pretrained models called Chain of Merges (CoM), which addresses the limitations of exis...
The paper presents DOTResize, a novel method for reducing the width of Large Language Models (LLMs) through Discrete Optimal Transport-ba...
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