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Found 9 papers, 2 papers with code
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets.
Harmonizing Global Voices: Culturally-Aware Models for Enhanced Content Moderation
We train large language models on extensive datasets of media news and articles to create culturally attuned models.
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
Many real-life decision-making situations allow further relevant information to be acquired at a specific cost, for example, in assessing the health status of a patient we may decide to take additional measurements such as diagnostic tests or imaging scans before making a final assessment.
A Birth-Death Process for Feature Allocation
We propose a Bayesian nonparametric prior over feature allocations for sequential data, the birth-death feature allocation process (BDFP).
Bayesian Nonparametrics for Sparse Dynamic Networks
In this paper we propose a Bayesian nonparametric approach to modelling sparse time-varying networks.
A reversible infinite HMM using normalised random measures
We present a nonparametric prior over reversible Markov chains.
A dependent partition-valued process for multitask clustering and time evolving network modelling
The fundamental aim of clustering algorithms is to partition data points.
A nonparametric variable clustering model
Factor analysis models effectively summarise the covariance structure of high dimensional data, but the solutions are typically hard to interpret.
SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases
The Internet has enabled the creation of a growing number of large-scale knowledge bases in a variety of domains containing complementary information.
