Zeroquant: Efficient and affordable post-training quantization for large-scale transformers
How to efficiently serve ever-larger trained natural language models in practice has become
exceptionally challenging even for powerful cloud servers due to their prohibitive …
exceptionally challenging even for powerful cloud servers due to their prohibitive …
Unsupervised EHR‐based phenoty** via matrix and tensor decompositions
Computational phenoty** allows for unsupervised discovery of subgroups of patients as
well as corresponding co‐occurring medical conditions from electronic health records …
well as corresponding co‐occurring medical conditions from electronic health records …
Zeroquant (4+ 2): Redefining llms quantization with a new fp6-centric strategy for diverse generative tasks
X Wu, H ** electronic health records
Phenoty** electronic health records (EHR) focuses on defining meaningful patient groups
(eg, heart failure group and diabetes group) and identifying the temporal evolution of …
(eg, heart failure group and diabetes group) and identifying the temporal evolution of …
Static and streaming tucker decomposition for dense tensors
Given a dense tensor, how can we efficiently discover hidden relations and patterns in static
and online streaming settings? Tucker decomposition is a fundamental tool to analyze …
and online streaming settings? Tucker decomposition is a fundamental tool to analyze …
Fast and accurate domain adaptation for irregular tensor decomposition
Given an irregular tensor from a newly emerging domain, how can we quickly and
accurately capture its patterns utilizing existing irregular tensors in multiple domains? The …
accurately capture its patterns utilizing existing irregular tensors in multiple domains? The …
Deepspeed data efficiency: Improving deep learning model quality and training efficiency via efficient data sampling and routing
Recent advances on deep learning models come at the price of formidable training cost. The
increasing model size is one of the root causes, but another less-emphasized fact is that …
increasing model size is one of the root causes, but another less-emphasized fact is that …
Random-ltd: Random and layerwise token drop** brings efficient training for large-scale transformers
Z Yao, X Wu, C Li, C Holmes, M Zhang, C Li… - ar** Using Electronic Health Records
E Konyar, MR Gahrooei - IEEE Journal of Biomedical and …, 2025 - ieeexplore.ieee.org
Computational phenoty** uses data mining methods to extract clusters of clinical
descriptors, known as phenotypes, from electronic health records (EHR). Tensor …
descriptors, known as phenotypes, from electronic health records (EHR). Tensor …