Feature Selection on Deep Neural Networks for Image Classification – Most existing methods for deep neural network models are trained on the representations of image data, which are of interest to a wide range of applications, including image matching, object retrieval and computer vision. We present an interactive learning approach that learns to predict a model from a model representation using both labeled and unlabeled data. We analyze the problem, provide both qualitative and quantitative performance evaluations, and present them as an open-source and open-sourced solution.

In many domains, the task of evaluating an inference algorithm is to determine how to best represent the domain and, in a particular, to estimate the parameters of a model. Motivated by the popularity of machine learning from the 1960s and 70s, a new approach with an intuitive and clear theoretical formulation of inference based on probabilistic models has been proposed. The goal of the paper is to show that an alternative theory of inference, called the probabilistic inference approach, can be viewed as a generalization of the probabilistic approach. This approach is presented in terms of probabilistic inference. It is shown that an inference algorithm can be regarded as using an probabilistic model of the domain to assess the probability of using the model. This approach gives a generalization-free intuition to the probabilistic inference approach that can be used to decide on the parameters of a machine learning system. The computational complexity of the probabilistic inference approach is established.

A Survey on Link Prediction in Abstracts

Robustness of non-linear classifiers to non-linear adversarial attacks

# Feature Selection on Deep Neural Networks for Image Classification

Towards a Principled Optimisation of Deep Learning Hardware Design

Online Model Interpretability in Machine Learning ApplicationsIn many domains, the task of evaluating an inference algorithm is to determine how to best represent the domain and, in a particular, to estimate the parameters of a model. Motivated by the popularity of machine learning from the 1960s and 70s, a new approach with an intuitive and clear theoretical formulation of inference based on probabilistic models has been proposed. The goal of the paper is to show that an alternative theory of inference, called the probabilistic inference approach, can be viewed as a generalization of the probabilistic approach. This approach is presented in terms of probabilistic inference. It is shown that an inference algorithm can be regarded as using an probabilistic model of the domain to assess the probability of using the model. This approach gives a generalization-free intuition to the probabilistic inference approach that can be used to decide on the parameters of a machine learning system. The computational complexity of the probabilistic inference approach is established.