GPUs, training clusters, MLOps, and deployment
https://arxiv.org/abs/2604.05091 Abstract: "We present MegaTrain, a memory-centric system that efficiently trains 100B+ parameter large l...
No chat interface. No identity. No instructions. Just the API in raw autocomplete mode. The model receives text, predicts the next tokens...
Anthropic’s Project Glasswing caught my attention less as a cybersecurity headline than as a signal about how frontier AI may be commerci...
![[2510.13582] ArtNet: Hierarchical Clustering-Based Artificial Netlist Generator for ML and DTCO Application](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
ArtNet introduces a novel artificial netlist generator that enhances machine learning model generalization and design-technology co-optim...
AXLearn presents a modular and hardware-agnostic approach to training large deep learning models, enhancing scalability and performance w...
The paper presents a novel approach to continual learning in machine learning models, introducing a parameter-efficient fine-tuning modul...
This article surveys open datasets in learning analytics, identifying trends, challenges, and best practices to enhance research reproduc...
The paper presents new privacy-preserving protocols for verifiable inference of large language models (LLMs), addressing the challenges o...
The paper presents PRIMO, a supervised latent-variable model that addresses the challenges of incomplete multimodal data by quantifying t...
This paper presents Greedy Multi-Path Block Verification (GBV), a method that enhances the efficiency of speculative decoding in machine ...
This article presents a study on the multi-objective optimization of deep learning interatomic potentials, focusing on the trade-off betw...
The paper introduces NeST, a novel framework for enhancing safety in large language models (LLMs) by selectively tuning a small subset of...
The paper presents U-FedTomAtt, an ultra-lightweight federated learning framework designed for tomato disease recognition, optimizing per...
The paper presents SEMAS, a self-evolving multi-agent network designed for predictive maintenance in Industrial IoT, enhancing real-time ...
DARTH-PUM proposes a hybrid Processing-Using-Memory architecture that integrates analog and digital PUM to enhance computational efficien...
The paper presents KVFetcher, a novel solution for efficient remote key-value (KV) cache reuse using GPU-native video codecs, significant...
The A.R.I.S. system utilizes deep learning to enhance e-waste recycling by accurately classifying materials in real-time, improving recov...
This paper presents KD-UFSL, a method to enhance privacy in federated split learning by minimizing data leakage through intermediate repr...
This paper explores the loss landscape of one-hidden-layer ReLU networks, demonstrating that overparameterization leads to smoother lands...
The paper introduces a novel approach to variational inference (VI) by optimizing radial profiles, enhancing the approximation of high-di...
This article presents a novel approach to episodic Markov decision process (MDP) planning by framing it as Bayesian inference over polici...
The paper presents 2Mamba, a linear attention transformer variant that achieves competitive accuracy compared to softmax attention while ...
The paper introduces Unified Latents (UL), a framework for training latent representations using a diffusion prior, achieving competitive...
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