70 citations · 167 across the 14 of their papers we have counts for
14 papers
S4: a High-sparsity, High-performance AI Accelerator
Ian En-Hsu Yen, Zhibin Xiao, Dongkuan Xu
Exploiting sparsity underlying neural networks has become one of the most potential methodologies to reduce the memory footprint, I/O cost, and computation workloads during inferen…
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm
Shaoyi Huang, Dongkuan Xu, Ian E. H. Yen +8
Conventional wisdom in pruning Transformer-based language models is that pruning reduces the model expressiveness and thus is more likely to underfit rather than overfit. However,…
Rethinking Network Pruning -- under the Pre-train and Fine-tune Paradigm
Dongkuan Xu, Ian E. H. Yen, Jinxi Zhao +1
Transformer-based pre-trained language models have significantly improved the performance of various natural language processing (NLP) tasks in the recent years. While effective an…
Minimizing FLOPs to Learn Efficient Sparse Representations
Biswajit Paria, Chih-Kuan Yeh, Ian E. H. Yen +3
Deep representation learning has become one of the most widely adopted approaches for visual search, recommendation, and identification. Retrieval of such representations from a la…
Efficient Global String Kernel with Random Features: Beyond Counting Substructures
Lingfei Wu, Ian En-Hsu Yen, Siyu Huo +5
Analysis of large-scale sequential data has been one of the most crucial tasks in areas such as bioinformatics, text, and audio mining. Existing string kernels, however, either (i)…
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding
Lingfei Wu, Ian En-Hsu Yen, Zhen Zhang +5
Graph kernels are widely used for measuring the similarity between graphs. Many existing graph kernels, which focus on local patterns within graphs rather than their global propert…