Search Results for author:
Found 9 papers, 3 papers with code
Meaningful Learning: Advancing Abstract Reasoning in Large Language Models via Generic Fact Guidance
Large language models (LLMs) have developed impressive performance and strong explainability across various reasoning scenarios, marking a significant stride towards mimicking human-like intelligence.
Length Extrapolation of Transformers: A Survey from the Perspective of Positional Encoding
In this survey, we present these advances towards length extrapolation in a unified notation from the perspective of PE.
PEPT: Expert Finding Meets Personalized Pre-training
For alleviating this, we present a personalized pre-training and fine-tuning paradigm, which could effectively learn expert interest and expertise simultaneously.
PUNR: Pre-training with User Behavior Modeling for News Recommendation
Firstly, we introduce the user behavior masking pre-training task to recover the masked user behaviors based on their contextual behaviors.
Deep Generative Modeling on Limited Data with Regularization by Nontransferable Pre-trained Models
Deep generative models (DGMs) are data-eager because learning a complex model on limited data suffers from a large variance and easily overfits.
Going Negative Online? -- A Study of Negative Advertising on Social Media
This research collects the data of the related political ads in the context of the U. S. midterm elections since August to study the overall pattern of political ads on social media and uses sets of machine learning methods to conduct sentiment analysis on these ads to classify the negative ads.
An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms
However, the differences in architecture designs and implementations of existing frameworks and platforms bring new challenges for DL software development and deployment.
Neural Review Rating Prediction with Hierarchical Attentions and Latent Factors
In this paper, we propose a hierarchical attention model fusing latent factor model for rating prediction with reviews, which can focus on important words and informative reviews.
NRPA: Neural Recommendation with Personalized Attention
In this paper we propose a neural recommendation approach with personalized attention to learn personalized representations of users and items from reviews.
