2 papers
cs.LG2021
Data Cleansing for Deep Neural Networks with Storage-efficient Approximation of Influence Functions
Kenji Suzuki, Yoshiyuki Kobayashi, Takuya Narihira
Identifying the influence of training data for data cleansing can improve the accuracy of deep learning. An approach with stochastic gradient descent (SGD) called SGD-influence to…
cs.LG2021
Neural Network Libraries: A Deep Learning Framework Designed from Engineers' Perspectives
Takuya Narihira, Javier Alonsogarcia, Fabien Cardinaux +14
While there exist a plethora of deep learning tools and frameworks, the fast-growing complexity of the field brings new demands and challenges, such as more flexible network design…