4 papers
Tricks and Plug-ins for Gradient Boosting in Image Classification
Biyi Fang, Truong Vo, Jean Utke +1
Convolutional Neural Networks (CNNs) have achieved remarkable success across a wide range of machine learning tasks by leveraging hierarchical feature learning through deep archite…
Topic Analysis with Side Information: A Neural-Augmented LDA Approach
Biyi Fang, Truong Vo, Kripa Rajshekhar +1
Traditional topic models such as Latent Dirichlet Allocation (LDA) have been widely used to uncover latent structures in text corpora, but they often struggle to integrate auxiliar…
Tricks and Plug-ins for Gradient Boosting with Transformers
Biyi Fang, Truong Vo, Jean Utke +1
Transformer architectures dominate modern NLP but often demand heavy computational resources and intricate hyperparameter tuning. To mitigate these challenges, we propose a novel f…
TrInk: Ink Generation with Transformer Network
Zezhong Jin, Shubhang Desai, Xu Chen +8
In this paper, we propose TrInk, a Transformer-based model for ink generation, which effectively captures global dependencies. To better facilitate the alignment between the input…