Search Results for author:
Found 6 papers, 4 papers with code
BoRA: Bayesian Hierarchical Low-Rank Adaption for Multi-Task Large Language Models
This paper introduces Bayesian Hierarchical Low-Rank Adaption (BoRA), a novel method for finetuning multi-task Large Language Models (LLMs).
NLEBench+NorGLM: A Comprehensive Empirical Analysis and Benchmark Dataset for Generative Language Models in Norwegian
Norwegian, spoken by only 5 million population, is under-representative within the most impressive breakthroughs in NLP tasks.
FINN.no Slates Dataset: A new Sequential Dataset Logging Interactions, allViewed Items and Click Responses/No-Click for Recommender Systems Research
Although the usage of exposure data in recommender systems is growing, to our knowledge there is no open large-scale recommender systems dataset that includes the slates of items presented to the users at each interaction.
Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling
We introduce a variational Bayesian Recurrent Neural Net recommender system that acts on time series of interactions between the internet platform and the user, and which scales to real world industrial situations.
Deep neural network marketplace recommenders in online experiments
Recommendations are broadly used in marketplaces to match users with items relevant to their interests and needs.
Five lessons from building a deep neural network recommender
Recommendation algorithms are widely adopted in marketplaces to help users find the items they are looking for.
