43 citations · 54 across the 11 of their papers we have counts for
7 papers · 1 filter
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…
Topic Analysis for Text with Side Data
Biyi Fang, Kripa Rajshekhar, Diego Klabjan
Although latent factor models (e.g., matrix factorization) obtain good performance in predictions, they suffer from several problems including cold-start, non-transparency, and sub…
Tricks and Plugins to GBM on Images and Sequences
Biyi Fang, Jean Utke, Diego Klabjan
Convolutional neural networks (CNNs) and transformers, which are composed of multiple processing layers and blocks to learn the representations of data with multiple abstract level…
Only Train Once: A One-Shot Neural Network Training And Pruning Framework
Tianyi Chen, Bo Ji, Tianyu Ding +7
Structured pruning is a commonly used technique in deploying deep neural networks (DNNs) onto resource-constrained devices. However, the existing pruning methods are usually heuris…
Neural Network Compression Via Sparse Optimization
Tianyi Chen, Bo Ji, Yixin Shi +4
The compression of deep neural networks (DNNs) to reduce inference cost becomes increasingly important to meet realistic deployment requirements of various applications. There have…
Convergence Analyses of Online ADAM Algorithm in Convex Setting and Two-Layer ReLU Neural Network
Biyi Fang, Diego Klabjan
Nowadays, online learning is an appealing learning paradigm, which is of great interest in practice due to the recent emergence of large scale applications such as online advertisi…