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Found 6 papers, 0 papers with code
Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer in Prompt Tuning
In this paper, we focus on improving the prompt transfer from dialogue state tracking to dialogue summarization and propose Skeleton-Assisted Prompt Transfer (SAPT), which leverages skeleton generation as extra supervision that functions as a medium connecting the distinct source and target task and resulting in the model's better consumption of dialogue state information.
Lerna: Transformer Architectures for Configuring Error Correction Tools for Short- and Long-Read Genome Sequencing
We also show that the alignment rate and assembly quality computed for the corrected reads are strongly negatively correlated with the perplexity, enabling the automated selection of k-mer values for better error correction, and hence, improved assembly quality.
Automatic Forecasting via Meta-Learning
In this work, we develop techniques for fast automatic selection of the best forecasting model for a new unseen time-series dataset, without having to first train (or evaluate) all the models on the new time-series data to select the best one.
Graph Deep Factors for Forecasting
GraphDF is a hybrid forecasting framework that consists of a relational global and relational local model.
A Context Integrated Relational Spatio-Temporal Model for Demand and Supply Forecasting
We propose a novel context integrated relational model, Context Integrated Graph Neural Network (CIGNN), which leverages the temporal, relational, spatial, and dynamic contextual dependencies for multi-step ahead demand forecasting.
Fast Distributed Bandits for Online Recommendation Systems
This algorithm lazily creates clusters in a distributed manner, and dramatically reduces the network data sharing requirement, achieving high scalability.
