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Found 6 papers, 1 papers with code
Particle-MALA and Particle-mGRAD: Gradient-based MCMC methods for high-dimensional state-space models
In experiments, for both highly and weakly informative prior dynamics, our methods substantially improve upon both CSMC and sophisticated 'classical' MCMC approaches.
Advancing continual lifelong learning in neural information retrieval: definition, dataset, framework, and empirical evaluation
To address this challenge, a systematic task formulation of continual neural information retrieval is presented, along with a multiple-topic dataset that simulates continuous information retrieval.
Morphological Image Analysis and Feature Extraction for Reasoning with AI-based Defect Detection and Classification Models
As the use of artificial intelligent (AI) models becomes more prevalent in industries such as engineering and manufacturing, it is essential that these models provide transparent reasoning behind their predictions.
Identifying Early Help Referrals For Local Authorities With Machine Learning And Bias Analysis
This paper investigates the utilisation of machine learning (ML) to assist experts in identifying families that may need to be referred for Early Help assessment and support.
VITR: Augmenting Vision Transformers with Relation-Focused Learning for Cross-Modal Information Retrieval
This paper introduces VITR, a novel network that enhances ViT by extracting and reasoning about image region relations based on a local encoder.
On importance-weighted autoencoders
The importance weighted autoencoder (IWAE) (Burda et al., 2016) is a popular variational-inference method which achieves a tighter evidence bound (and hence a lower bias) than standard variational autoencoders by optimising a multi-sample objective, i. e. an objective that is expressible as an integral over $K > 1$ Monte Carlo samples.
