Search Results for author:
Found 19 papers, 7 papers with code
Multimodal Wildland Fire Smoke Detection
Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States.
FIgLib & SmokeyNet: Dataset and Deep Learning Model for Real-Time Wildland Fire Smoke Detection
Early detection of fire ignitions from initial smoke can assist the response to such fires before they become difficult to manage.
Learning Representations for Incomplete Time Series Clustering
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.
Generating Rationales in Visual Question Answering
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.
ReZero is All You Need: Fast Convergence at Large Depth
Deep networks often suffer from vanishing or exploding gradients due to inefficient signal propagation, leading to long training times or convergence difficulties.
Improving Neural Story Generation by Targeted Common Sense Grounding
Stories generated with neural language models have shown promise in grammatical and stylistic consistency.
LakhNES: Improving multi-instrumental music generation with cross-domain pre-training
We are interested in the task of generating multi-instrumental music scores.
Using Deep Siamese Neural Networks to Speed up Natural Products Research
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.
DeepJ: Style-Specific Music Generation
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
Deep-ESN: A Multiple Projection-encoding Hierarchical Reservoir Computing Framework
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.
Belief Tree Search for Active Object Recognition
We show that by optimizing the observation function and retraining the supervised LSTM network, the AOR performance on the test set improves significantly.
Recognizing and Curating Photo Albums via Event-Specific Image Importance
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.
Hierarchical Cellular Automata for Visual Saliency
The CCA can act as an efficient pixel-wise aggregation algorithm that can integrate state-of-the-art methods, resulting in even better results.
Skeleton Key: Image Captioning by Skeleton-Attribute Decomposition
Furthermore, our algorithm can generate descriptions with varied length, benefiting from the separate control of the skeleton and attributes.
Event-Specific Image Importance
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.
A Single Model Explains both Visual and Auditory Precortical Coding
Precortical neural systems encode information collected by the senses, but the driving principles of the encoding used have remained a subject of debate.
Deep Active Object Recognition by Joint Label and Action Prediction
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.
Basic Level Categorization Facilitates Visual Object Recognition
We instantiate this idea by training a deep CNN to perform basic level object categorization first, and then train it on subordinate level categorization.
Example Selection For Dictionary Learning
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.
