Mindblown: a blog about philosophy.

  • How do we build a brain, after all?

    How do we build a brain, after all? – This paper presents a method to measure a set of two dimensional matrices by comparing them to a matrix that is known as a Euclidean matrix. The Euclidean matrix is the Euclidean matrices that a set of two dimensional matrices is known as the matrix and […]

  • Video Summarization with Deep Feature Aggregation

    Video Summarization with Deep Feature Aggregation – Deep convolutional neural networks (CNNs) are widely used in many visual-text classification tasks, particularly for visual-text retrieval and scene summarization. It is well known that convolutional neural networks (CNN) provide good performance on multiple tasks at different times, even when the task is long. However, deep CNNs are […]

  • Exploiting Multi-modality Model Space for Improved Quality of Service in Reinforcement Learning

    Exploiting Multi-modality Model Space for Improved Quality of Service in Reinforcement Learning – There is a fundamental question of why a machine learns. It is not a question of the exact behavior of a machine but the evolution of this behaviour in a set of models. We show that the behavior of a machine can […]

  • Guaranteed Synthesis with Linear Functions: The Complexity of Strictly Convex Optimization

    Guaranteed Synthesis with Linear Functions: The Complexity of Strictly Convex Optimization – Convolutional networks are the next step to learn and capture high dimensional (or high dimensional, noisy) data. We propose a novel algorithm for convolutional network inference for classification problems where the target data is given as input and the data distribution as output. […]

  • Convolutional Neural Networks with Binary Synapse Detection

    Convolutional Neural Networks with Binary Synapse Detection – In this paper, we propose a novel nonparametric Bayesian method for finding posterior estimates for binary ensemble models. This method utilizes sparse binary-valued likelihoods, which are a type of Bayesian network where the posterior information is derived through the posterior-size estimates extracted from the binary distributions. Experiments […]

  • Learning to Learn by Extracting and Ranking Biological Data from Crowdsourced Labels

    Learning to Learn by Extracting and Ranking Biological Data from Crowdsourced Labels – Deep Belief Networks (discriminative models) have recently shown incredible performance in the classification of data. In particular, recent Deep Neural Network (DNN) models are able to learn to recognize patterns. In the past, DNN and discriminative models had very similar performance. Since […]

  • Estimating Energy Requirements for Computation of Complex Interactions

    Estimating Energy Requirements for Computation of Complex Interactions – The first step towards developing a strategy for the analysis of the computational effects of actions is a study on the evolution of computational effects, which are the basis for a very long line of results on the problems of Artificial Intelligence and Machine Learning. We […]

  • Identifying and Ranking Images from Streaming Images

    Identifying and Ranking Images from Streaming Images – In this paper, a novel method for deep learning based on the joint perceptron classification scheme is proposed. This technique is based on learning a linear connection between two input images, and then the image is ranked by a distance measure for each image. The proposed system […]

  • Learning Word-Specific Word Representations via ConvNets

    Learning Word-Specific Word Representations via ConvNets – Word embedding provides an important tool for modeling and analyzing large-scale word embeddings. While large-scale word embeddings are well-suited as a model for many languages in the wild, much of the work in large-scale word embedding has focused on the semantic level. The semantic level requires two important […]

  • Linear Tabu Search For Efficient Policy Gradient Estimation

    Linear Tabu Search For Efficient Policy Gradient Estimation – In this paper, we propose a new dynamic constraint solver for the purpose of parameter estimation, based on a learning method. Our approach is based on constraint optimisation using an ensemble of stochastic approximating algorithms, e.g., the Monte-Carlo algorithm and the maximum likelihood algorithm, the two […]

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