PURPOSE To probe medical students' narrative essays as a rich source of data on the hidden curriculum, a powerful influence shaping the values, roles, and identity of medical trainees. METHOD In 2008, the authors used grounded theory to conduct a thematic analysis of third-year Harvard Medical School students' reflection papers on the hidden curriculum. RESULTS Four overarching concepts wer...

Deep Neural Network (DNN) is difficult to train and easy to overfit in training. We address these two issues by introducing EigenNet, an architecture that not only accelerates training but also adjusts number of hidden neurons to reduce over-fitting. They are achieved by whitening the information flows of DNNs and removing those eigenvectors that may capture noises. The former improves conditio...

Training of continuous density hidden Markov models (CDHMMs) is usually time-consuming and tedious due to the large number of model parameters involved. Recently we proposed a new derivative of CDHMM, the sub-space distribution clustering hidden Markov model (SD-CHMM) which tie CDHMMs at the ner level of subspace distributions, resulting in many fewer model parameters. An SDCHMM training algori...

We develop a novel method for training of GANs for unsupervised and class conditional generation of images, called Linear Discriminant GAN (LD-GAN). The discriminator of an LD-GAN is trained to maximize the linear separability between distributions of hidden representations of generated and targeted samples, while the generator is updated based on the decision hyper-planes computed by performin...

Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image ...

The recently published backpercolation algorithm for the training of neural networks will be compared with the backpropagation and quickpropagation algorithm by means of "artificial" classification problems (e.g. XOR, M-N-M decoder) and serveral others. Within all classification schemes the backpercolation algorithm is much more efficent and even successful where the other training schemes are ...

Dropout is a popular technique for regularizing artificial neural networks. Dropout networks are generally trained by minibatch gradient descent with a dropout mask turning off some of the units—a different pattern of dropout is applied to every sample in the minibatch. We explore a very simple alternative to the dropout mask. Instead of masking dropped out units by setting them to zero, we per...

This paper describes a robust automatic speech recognition (ASR) system with less computation. Acoustic models of a hidden Markov model (HMM)-based classifier include various types of hidden factors such as speaker-specific characteristics, coarticulation, and an acoustic environment, etc. If there exists a canonicalization process that can recover the degraded margin of acoustic likelihoods be...