A Primer on PAC-Bayesian Learning
Generalised Bayesian learning algorithms are increasingly popular in machine learning, due to their PAC generalisation properties and flexibility. The present paper aims at providing a self-contained survey on the resulting PAC-Bayes framework and some of its main theoretical and algorithmic developments.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningSurveySimilar Papers 제목 키워드 기반
A Primer on Bayesian Neural Networks: Review and Debates
Neural networks have achieved remarkable performance across various problem domains, but their widespread applicability is hindered by inherent limitations such as overconfidence in predictions, lack of interpretability,…
Bayesian InferencePrimer C-VAE: An interpretable deep learning primer design method to detect emerging virus variants
Motivation: PCR is more economical and quicker than Next Generation Sequencing for detecting target organisms, with primer design being a critical step. In epidemiology with rapidly mutating viruses, designing effective …
EpidemiologyTopoPrimer: The Missing Topological Context in Forecasting Models
We introduce TopoPrimer, a framework that makes the global topological structure of the series population an explicit input to any forecasting model. TopoPrimer improves accuracy across diverse domains, stabilizes foreca…
Primer: Searching for Efficient Transformers for Language Modeling
Large Transformer models have been central to recent advances in natural language processing. The training and inference costs of these models, however, have grown rapidly and become prohibitively expensive. Here we aim …
Language ModelingLanguage ModellingPRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization
We introduce PRIMERA, a pre-trained model for multi-document representation with a focus on summarization that reduces the need for dataset-specific architectures and large amounts of fine-tuning labeled data. PRIMERA us…
Abstractive Text SummarizationDecoderDocument SummarizationMulti-Document Summarization+2