Interactive Parallel Inference for Latent Variable Models with Continuous Signals – We present the development of the first neural-network-based fully convolutional reinforcement learning (CNN-RL) model, named R-CNN, which is a fully generative, adversarial, data-driven, multi-objective reinforcement learning (DRL). The RL model learns a non-parametric representation of the context on a set of items, which predicts the items’ behaviors. This representation is then used to perform reinforcement. We show that state-of-the-art CNN-RL models with state-of-the-art reinforcement learning (RLs) succeed in achieving good performance on the task of reinforcement learning, but they do not learn accurate prediction performance. We develop a novel learning algorithm, called Fast RL-R, that learns to predict the most valuable items for each item, by leveraging the ability of multiple representations. The model is shown to outperform RL-RL models that use only a few items in the training data.
In this paper, we propose a novel network architecture that jointly learns to move both simultaneously through the input space and the input data space. We first learn to coordinate the input space jointly by leveraging the prior knowledge of both the input and the hidden space. We then generalize our model onto the input space by proposing an efficient multi-dimensional feature learning algorithm that is optimized by an optimization algorithm. Experimental results demonstrate the merits of our architecture compared to other existing algorithms and its advantages of adapting between different representations.
We present our analysis of a machine learning approach to nonparametric Bayesian model evaluation. The goal of the analysis is to obtain algorithms that outperform the state of the art for this task. The proposed tools are implemented in a single Python package that contains a set of example functions (such as the model of the user, a query and a user’s preferences) for evaluation from a computer. This package is a repository for a database of data that are used to analyze human performance on this task. The goal is to obtain algorithms that outperform the state of the art for this task.
CNN-based image annotation for Arabic text-based text
Adaptive Feature Framework for Classification and Graph Matching
Interactive Parallel Inference for Latent Variable Models with Continuous Signals
Adaptive Sparse Convolutional Features For Deep Neural Network-based Audio Classification
Learning to Move with Recurrent Neural Networks: A Deep Unsupervised Learning ApproachIn this paper, we propose a novel network architecture that jointly learns to move both simultaneously through the input space and the input data space. We first learn to coordinate the input space jointly by leveraging the prior knowledge of both the input and the hidden space. We then generalize our model onto the input space by proposing an efficient multi-dimensional feature learning algorithm that is optimized by an optimization algorithm. Experimental results demonstrate the merits of our architecture compared to other existing algorithms and its advantages of adapting between different representations.
We present our analysis of a machine learning approach to nonparametric Bayesian model evaluation. The goal of the analysis is to obtain algorithms that outperform the state of the art for this task. The proposed tools are implemented in a single Python package that contains a set of example functions (such as the model of the user, a query and a user’s preferences) for evaluation from a computer. This package is a repository for a database of data that are used to analyze human performance on this task. The goal is to obtain algorithms that outperform the state of the art for this task.