Early detection of fire ignitions from initial smoke can assist the response to such fires before they become difficult to manage.
Also, to reduce the error propagation from imputation to clustering, we introduce a discriminator to make the distribution of imputation values close to the true one and train CRLI in an alternating train- ing manner.
Despite recent advances in Visual QuestionAnswering (VQA), it remains a challenge todetermine how much success can be attributedto sound reasoning and comprehension ability. We seek to investigate this question by propos-ing a new task ofrationale generation.
Deep networks often suffer from vanishing or exploding gradients due to inefficient signal propagation, leading to long training times or convergence difficulties.
Stories generated with neural language models have shown promise in grammatical and stylistic consistency.
We are interested in the task of generating multi-instrumental music scores.
Computing a similarity score between 2D NMR spectra for a novel compound and a compound whose structure is known helps determine the structure of the novel compound.
Recent advances in deep neural networks have enabled algorithms to compose music that is comparable to music composed by humans.
Sound Audio and Speech Processing
As an efficient recurrent neural network (RNN) model, reservoir computing (RC) models, such as Echo State Networks, have attracted widespread attention in the last decade.
We show that by optimizing the observation function and retraining the supervised LSTM network, the AOR performance on the test set improves significantly.
Automatic organization of personal photos is a problem with many real world ap- plications, and can be divided into two main tasks: recognizing the event type of the photo collection, and selecting interesting images from the collection.
The CCA can act as an efficient pixel-wise aggregation algorithm that can integrate state-of-the-art methods, resulting in even better results.
Furthermore, our algorithm can generate descriptions with varied length, benefiting from the separate control of the skeleton and attributes.
In this paper, we show that the selection of important images is consistent among different viewers, and that this selection process is related to the event type of the album.
Precortical neural systems encode information collected by the senses, but the driving principles of the encoding used have remained a subject of debate.
The results of experiments suggest that the proposed model equipped with Dirichlet state encoding is superior in performance, and selects images that lead to better training and higher accuracy of label prediction at test time.
We instantiate this idea by training a deep CNN to perform basic level object categorization first, and then train it on subordinate level categorization.
In unsupervised learning, an unbiased uniform sampling strategy is typically used, in order that the learned features faithfully encode the statistical structure of the training data.