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 recently left a very toxic company that was taking a serious toll on my mental and physical health. I gave everything I had and it cost...
Hey all, Our ML team spent some time this week getting training and deployments working for Gemma-4, and wanted to document all the thing...
![[2502.15110] Variational phylogenetic inference with products over bipartitions](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
This paper presents a novel variational Bayesian method for inferring ultrametric phylogenetic trees, improving accuracy and efficiency i...
This article explores the use of representation learning to improve weighting methods in design-based causal inference, addressing challe...
The paper presents a novel framework for online tensor inference, addressing the challenges of real-time data processing in applications ...
This paper explores the concept of 'epistemic throughput' in attention-constrained inference, analyzing how generative AI systems can man...
The paper introduces Riemannian MeanFlow (RMF), a novel framework for generative modeling on Riemannian manifolds, significantly reducing...
The paper introduces tLoRA, a framework designed for efficient multi-LoRA training of large language models, improving training throughpu...
This paper explores membership and dataset inference attacks on large audio generative models, assessing their implications for copyright...
The paper presents Boundary Guidance, a reinforcement learning method designed to improve the safety and utility of generative models by ...
The paper introduces Multipole Semantic Attention (MuSe), a method that accelerates pretraining of transformers on long sequences by 36% ...
This article presents a framework for semantic caching in large language models (LLMs) to reduce inference costs by leveraging semantic s...
This paper presents a novel model-heterogeneous federated learning framework that enhances generalization performance for clients with di...
The paper introduces Instruction-based Time Series Editing, a novel approach that allows users to modify time series data using natural l...
The paper presents GAGA, a method enhancing the efficiency of 3D molecular generation by leveraging Gaussian approximations, improving bo...
This paper explores learning verifiers for Chain-of-Thought reasoning in natural language, addressing the challenges of incorrect inferen...
This paper presents a systematic framework for designing weight quantisation formats in deep learning, demonstrating that variable-length...
The paper discusses Memory Injection Attacks (MINJA) on LLM agents, demonstrating how attackers can manipulate agent memory through query...
This paper presents a novel framework for reconstructing realistic high-resolution face images from facial embeddings using diffusion mod...
FedHENet introduces a frugal federated learning framework that enhances energy efficiency and stability in heterogeneous environments whi...
This paper explores nonparametric contextual online bilateral trade, presenting an algorithm that optimizes trade pricing based on contex...
This article presents a theoretical analysis of how annealing strategies can mitigate mode collapse in variational inference, particularl...
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