Search Results for author:
Found 4 papers, 3 papers with code
On the Learning Dynamics of Attention Networks
Attention models are typically learned by optimizing one of three standard loss functions that are variously called -- soft attention, hard attention, and latent variable marginal likelihood (LVML) attention.
On the Interpretability of Attention Networks
In trained models with an attention mechanism, the outputs of an intermediate module that encodes the segment of input responsible for the output is often used as a way to peek into the `reasoning` of the network.
Using noise resilience for ranking generalization of deep neural networks
Recent papers have shown that sufficiently overparameterized neural networks can perfectly fit even random labels.
Structural Health Monitoring of Cantilever Beam, a Case Study -- Using Bayesian Neural Network AND Deep Learning
Deep learning architectures like CNN (Convolutional neural network) and LSTM(Long Short Term Memory) are good candidates for representation learning from high dimensional data.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
