Titlebook: Neural Information Processing; 22nd International C Sabri Arik,Tingwen Huang,Qingshan Liu Conference proceedings 2015 Springer Internationa

Invertebrate

Max-Pooling Dropout for Regularization of Convolutional Neural Networks,ayers. However, its effect in pooling layers is still not clear. This paper demonstrates that .-. is equivalent to randomly picking activation based on a multinomial distribution at training time. In light of this insight, we advocate employing our proposed probabilistic weighted pooling, instead of

squander

Predicting Box Office Receipts of Movies with Pruned Random Forest,e use pruned random forest to predict the box office of the first week in Chinese theatres one month before movies’ theatrical release. In our model, the prediction problem is converted into a classification problem, where the box office receipt of a movie is discretized into eight categories. Exper

SLAY

A Novel ,-graph Based Image Classification Algorithm,en the training sample and the other classes. However, different classes’ features are visually similar and correlated (. facial images), which means the association between the training sample and the different classes contain important information, and must be taken into consideration. In this pap

VEN

TRACE

FACET

Supervised Topic Classification for Modeling a Hierarchical Conference Structure, form of expert information over document-topic correspondence. To exploit the expert information we use a regularization term that penalizes the difference between a predicted and an expert-given model. We hence add the regularization term to the log-likelihood function and use a stochastic EM base

平常

A Framework for Online Inter-subjects Classification in Endogenous Brain-Computer Interfaces,during the last years. However, few works tried to conceive classification models that take advantage of both techniques. In this paper we propose an online inter-subjects classification framework for endogenous BCIs. Inter-subjects classification is performed using a weighted average ensemble in wh

樂意

A Bayesian Sarsa Learning Algorithm with Bandit-Based Method,inforcement learning. We adopt probability distributions to estimate Q-values and compute posterior distributions about Q-values by Bayesian Inference. It can improve the accuracy of Q-values function estimation. In the process of algorithm learning, we use a Bandit-based method to solve the explora

事與愿違

Myocyte

Learning to Reconstruct 3D Structure from Object Motion,try or factorization, a Deep Neural Network (DNN) based method is proposed without assuming the camera model explicitly. In the proposed method, the targets are first split into connected 3D corners, and then the DNN regressor is trained to estimate the relative 3D structure of each corner from the