51 citations · 207 across the 17 of their papers we have counts for
9 papers · 1 filter
Detecting Gender Bias in Transformer-based Models: A Case Study on BERT
Bingbing Li, Hongwu Peng, Rajat Sainju +7
In this paper, we propose a novel gender bias detection method by utilizing attention map for transformer-based models. We 1) give an intuitive gender bias judgement method by comp…
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,…
Dr. Top-k: Delegate-Centric Top-k on GPUs
Anil Gaihre, Da Zheng, Scott Weitze +5
Recent top- computation efforts explore the possibility of revising various sorting algorithms to answer top- queries on GPUs. These endeavors, unfortunately, perform signifi…
Optimizing FPGA-based Accelerator Design for Large-Scale Molecular Similarity Search
Hongwu Peng, Shiyang Chen, Zhepeng Wang +9
Molecular similarity search has been widely used in drug discovery to identify structurally similar compounds from large molecular databases rapidly. With the increasing size of ch…
Binary Complex Neural Network Acceleration on FPGA
Hongwu Peng, Shanglin Zhou, Scott Weitze +9
Being able to learn from complex data with phase information is imperative for many signal processing applications. Today' s real-valued deep neural networks (DNNs) have shown effi…
PM-LSH: a fast and accurate in-memory framework for high-dimensional approximate NN and closest pair search
Bolong Zheng, Xi Zhao, Lianggui Weng +3
Nearest neighbor (NN) search is inherently computationally expensive in high-dimensional spaces due to the curse of dimensionality. As a well-known solution, locality-sensitive has…
FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-signal DNN Accelerator
Geng Yuan, Payman Behnam, Zhengang Li +8
Recent works demonstrated the promise of using resistive random access memory (ReRAM) as an emerging technology to perform inherently parallel analog domain in-situ matrix-vector m…
TRUST: Triangle Counting Reloaded on GPUs
Santosh Pandey, Zhibin Wang, Sheng Zhong +8
Triangle counting is a building block for a wide range of graph applications. Traditional wisdom suggests that i) hashing is not suitable for triangle counting, ii) edge-centric tr…
TAG: Gradient Attack on Transformer-based Language Models
Jieren Deng, Yijue Wang, Ji Li +4
Although federated learning has increasingly gained attention in terms of effectively utilizing local devices for data privacy enhancement, recent studies show that publicly shared…