247 citations · 247 across the 5 of their papers we have counts for
5 papers
Benchmarking PNW Model for MedMNIST to 100% Accuracy
Bo Deng
In this paper, we introduce a new concept called Artificial Special Intelligence by which Machine Learning models for the classification problem can be trained error-free, thus acq…
FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation
Jieyun Bai, Yitong Tang, Zihao Zhou +36
Accurate segmentation of cervical structures in transvaginal ultrasound (TVS) is critical for assessing the risk of spontaneous preterm birth (PTB), yet the scarcity of labeled dat…
Toward Errorless Training ImageNet-1k
Bo Deng, Levi Heath
In this paper, we describe a feedforward artificial neural network trained on the ImageNet 2012 contest dataset [7] with the new method of [5] to an accuracy rate of 98.3% with a 9…
Error-free Training for Artificial Neural Network
Bo Deng
Conventional training methods for artificial neural network (ANN) models never achieve zero error rate systematically for large data. A new training method consists of three steps:…
TENER: Adapting Transformer Encoder for Named Entity Recognition
Hang Yan, Bocao Deng, Xiaonan Li +1
The Bidirectional long short-term memory networks (BiLSTM) have been widely used as an encoder in models solving the named entity recognition (NER) task. Recently, the Transformer…