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Found 10 papers, 4 papers with code
Integrating Planning into Single-Turn Long-Form Text Generation
To overcome the scarcity of training data for these intermediate steps, we leverage LLMs to generate synthetic intermediate writing data such as outlines, key information and summaries from existing full articles.
Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity
Large Language Models (LLMs), renowned for their remarkable performance across diverse domains, present a challenge when it comes to practical deployment due to their colossal model size.
Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback
Deep neural networks (DNNs) demonstrate significant advantages in improving ranking performance in retrieval tasks.
Learning Neural Contextual Bandits Through Perturbed Rewards
Thanks to the power of representation learning, neural contextual bandit algorithms demonstrate remarkable performance improvement against their classical counterparts.
Learning Neural Ranking Models Online from Implicit User Feedback
Existing online learning to rank (OL2R) solutions are limited to linear models, which are incompetent to capture possible non-linear relations between queries and documents.
Calibrating Explore-Exploit Trade-off for Fair Online Learning to Rank
Online learning to rank (OL2R) has attracted great research interests in recent years, thanks to its advantages in avoiding expensive relevance labeling as required in offline supervised ranking model learning.
PairRank: Online Pairwise Learning to Rank by Divide-and-Conquer
Online Learning to Rank (OL2R) eliminates the need of explicit relevance annotation by directly optimizing the rankers from their interactions with users.
Active Collaborative Sensing for Energy Breakdown
However, very few homes in the world have installed sub-meters (sensors measuring individual appliance energy); and the cost of retrofitting a home with extensive sub-metering eats into the funds available for energy saving retrofits.
The FacT: Taming Latent Factor Models for Explainability with Factorization Trees
In this work, we integrate regression trees to guide the learning of latent factor models for recommendation, and use the learnt tree structure to explain the resulting latent factors.
Explainable Recommendation via Multi-Task Learning in Opinionated Text Data
Explaining automatically generated recommendations allows users to make more informed and accurate decisions about which results to utilize, and therefore improves their satisfaction.
