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...
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...
Hi everyone : ) I just released a new research prototype It’s a lossless BF16 compression format that stores weights in 12 bits by replac...
![[2602.22474] When to Act, Ask, or Learn: Uncertainty-Aware Policy Steering](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
This article presents a framework for uncertainty-aware policy steering in robotics, enabling adaptive robot behavior by addressing task ...
This paper introduces Spatial Credit Redistribution (SCR) to address hallucinations in vision-language models by redistributing activatio...
The paper introduces veScale-FSDP, a new system for Fully Sharded Data Parallel (FSDP) that enhances flexibility and performance for larg...
GetBatch introduces a new object store API that enhances batch retrieval in machine learning data loading, achieving significant performa...
This paper presents a novel algorithm for testably learning general Massart halfspaces under Gaussian noise, achieving near-optimal error...
The paper presents Contextual Memory Virtualisation (CMV), a novel system for managing state in large language models (LLMs) using a Dire...
The paper presents GRAU, a Generic Reconfigurable Activation Unit designed for neural network hardware accelerators, which significantly ...
This article discusses the application of foundation models in histopathology, highlighting a novel approach that improves robustness and...
This paper presents a novel adaptive multichain blockchain model that addresses scalability issues by employing a multiobjective optimiza...
This paper presents a novel approach to reconstruct audio and images from clipped measurements using self-supervised learning, addressing...
The paper presents PLADA, a novel method for efficient dataset transmission in machine learning, significantly reducing payload size whil...
FlashOptim introduces innovative optimizers that significantly reduce memory usage in neural network training, enhancing efficiency witho...
The paper introduces ParamMem, a parametric memory module designed to enhance language agents by enabling diverse reflective outputs, imp...
The paper presents TT-SEAL, a selective encryption framework designed for Tensor-Train Decomposed (TTD) networks, enhancing security and ...
This paper presents a novel approach to disaster recovery in distributed storage systems, addressing the limitations of cryptographic has...
InnerQ presents a novel hardware-aware quantization method for key-value caches in large language models, enhancing decoding efficiency w...
The paper presents FM-RME, a foundation model for radio map estimation that integrates self-supervised learning and physical propagation ...
The paper presents SmartChunk Retrieval, a query-aware framework that enhances retrieval-augmented generation (RAG) by adapting chunk siz...
DS SERVE is a framework designed to enhance neural retrieval systems by efficiently processing large-scale text datasets, achieving low l...
The paper discusses a method for recovering meter-scale surface weather data by integrating sparse surface measurements with high-resolut...
Get the latest news, tools, and insights delivered to your inbox.
Daily or weekly digest • Unsubscribe anytime