5 papers · 1 filter
DSAI: Unbiased and Interpretable Latent Feature Extraction for Data-Centric AI
Hyowon Cho, Soonwon Ka, Daechul Park +3
Large language models (LLMs) often struggle to objectively identify latent characteristics in large datasets due to their reliance on pre-trained knowledge rather than actual data…
Compiler-Level Matrix Multiplication Optimization for Deep Learning
Huaqing Zhang, Xiaolin Cheng, Hui Zang +1
An important linear algebra routine, GEneral Matrix Multiplication (GEMM), is a fundamental operator in deep learning. Compilers need to translate these routines into low-level cod…
Gradient-Coherent Strong Regularization for Deep Neural Networks
Dae Hoon Park, Chiu Man Ho, Yi Chang +1
Regularization plays an important role in generalization of deep neural networks, which are often prone to overfitting with their numerous parameters. L1 and L2 regularizers are co…
Sequenced-Replacement Sampling for Deep Learning
Chiu Man Ho, Dae Hoon Park, Wei Yang +1
We propose sequenced-replacement sampling (SRS) for training deep neural networks. The basic idea is to assign a fixed sequence index to each sample in the dataset. Once a mini-bat…
Interpreting Deep Classifier by Visual Distillation of Dark Knowledge
Kai Xu, Dae Hoon Park, Chang Yi +1
Interpreting black box classifiers, such as deep networks, allows an analyst to validate a classifier before it is deployed in a high-stakes setting. A natural idea is to visualize…