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Found 7 papers, 4 papers with code
Learning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Neural Networks
Providing a model that achieves a strong predictive performance and at the same time is interpretable by humans is one of the most difficult challenges in machine learning research due to the conflicting nature of these two objectives.
WATTNet: Learning to Trade FX via Hierarchical Spatio-Temporal Representation of Highly Multivariate Time Series
Finance is a particularly challenging application area for deep learning models due to low noise-to-signal ratio, non-stationarity, and partial observability.
Multimodal Learning and Reasoning for Visual Question Answering
In this work we introduce a modular neural network model that learns a multimodal and multifaceted representation of the image and the question.
A Simple Loss Function for Improving the Convergence and Accuracy of Visual Question Answering Models
On the other hand, very little focus has been put on the models' loss function, arguably one of the most important aspects of training deep learning models.
Hyperparameter Transfer Learning through Surrogate Alignment for Efficient Deep Neural Network Training
Recently, several optimization methods have been successfully applied to the hyperparameter optimization of deep neural networks (DNNs).
Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates
Those methods adopt probabilistic surrogate models like Gaussian processes to approximate and minimize the validation error function of hyperparameter values.
A Focused Dynamic Attention Model for Visual Question Answering
Solving VQA problems requires techniques from both computer vision for understanding the visual contents of a presented image or video, as well as the ones from natural language processing for understanding semantics of the question and generating the answers.
