Find Everything About Machine Intelligence

Machine Intelligence Research Papers
    Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
    Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems. U...
    - Explore

    Generating Videos with Scene Dynamics
    We capitalize on large amounts of unlabeled video in order to learn a model of scene dynamics for both video recognition tasks (e.g. action classification) and video generation tasks (e.g. future pred...
    - Explore

    Why does deep and cheap learning work so well?
    We show how the success of deep learning depends not only on mathematics but also on physics: although well-known mathematical theorems guarantee that neural networks can approximate arbitrary functio...
    - Explore

    ARTIFICIAL INTELLIGENCE AND LIFE IN 2030
    The One Hundred Year Study on Artificial Intelligence, launched in the fall of 2014, is a long-term investigation of the field of Artificial Intelligence (AI) and its influences on people, their commu...
    - Explore

    Conversational Contextual Cues: The Case of Personalization and History for Response Ranking
    We investigate the task of modeling open-domain, multi-turn, unstructured, multi-participant, conversational dialogue. We specifically study the effect of incorporating different elements of the conve...
    - Explore

    Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation
    We introduce the multiresolution recurrent neural network, which extends the sequence-to-sequence framework to model natural language generation as two parallel discrete stochastic processes: a sequen...
    - Explore

    A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks
    Research in neuroevolution, i.e. evolving artificial neural networks (ANNs) through evolutionary algorithms, is inspired by the evolution of biological brains. Because natural evolution discovered int...
    - Explore

    Autonomous Evolution of Topographic Regularities in Artificial Neural Networks
    Looking to nature as inspiration, for at least the last 25 years researchers in the field of neuroevolution (NE) have developed evolutionary algorithms designed specifically to evolve artificial neura...
    - Explore

    Helper agent: designing an assistant for human-human interaction in a virtual meeting space
    This paper introduces a new application area for agents in the computer interface: the support of human-human interaction. We discuss an interface agent prototype that is designed to support human-hum...
    - Explore

    Design and Evaluation of a Personal Digital Assistant-based Research Platform for Cochlear Implants
    This paper discusses the design, development, features, and clinical evaluation of a personal digital assistant (PDA)-based platform for cochlear implant research. This highly versatile and portable r...
    - Explore

    Sirius: An Open End-to-End Voice and Vision Personal Assistant and Its Implications for Future Warehouse Scale Computers
    As user demand scales for intelligent personal assistants (IPAs) such as Apple's Siri, Google's Google Now, and Microsoft's Cortana, we are approaching the computational limits of current datacenter a...
    - Explore

    Variations of the Similarity Function of TextRank for Automated Summarization
    This article presents new alternatives to the similarity function for the TextRank algorithm for automatic summarization of texts. We describe the generalities of the algorithm and the different funct...
    - Explore

    Comparing different knowledge sources for the automatic summarization of biomedical literature
    Automatic summarization of biomedical literature usually relies on domain knowledge from external sources to build rich semantic representations of the documents to be summarized. In this paper, we in...
    - Explore

    The Automatic Creation of Literature Abstracts
    Excerpts of technical papers and magazine articles that serve the purposes of conventional abstracts have been created entirely by automatic means. In the exploratory research described, the complete ...
    - Explore

    Deep Exploration via Bootstrapped DQN
    Efficient exploration in complex environments remains a major challenge for reinforcement learning. We propose bootstrapped DQN, a simple algorithm that explores in a computationally and statistically...
    - Explore

    Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
    We present weight normalization: a reparameterization of the weight vectors in a neural network that decouples the length of those weight vectors from their direction. By reparameterizing the weights ...
    - Explore

    Continuous Deep Q-Learning with Model-based Acceleration
    Model-free reinforcement learning has been successfully applied to a range of challenging problems, and has recently been extended to handle large neural network policies and value functions. However,...
    - Explore

    Deep Reinforcement Learning from Self-Play in Imperfect-Information Games
    Many real-world applications can be described as large-scale games of imperfect information. To deal with these challenging domains, prior work has focused on computing Nash equilibria in a handcrafte...
    - Explore

    Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
    Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. The primary difficulty arises due to insufficient exploration, resultin...
    - Explore

    End-to-End Training of Deep Visuomotor Policies
    olicy search methods can allow robots to learn control policies for a wide range of tasks, but practical applications of policy search often require hand-engineered components for perception, state es...
    - Explore

    Asynchronous Methods for Deep Reinforcement Learning
    We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers. We present async...
    - Explore

    Memory-based control with recurrent neural networks
    Partially observed control problems are a challenging aspect of reinforcement learning. We extend two related, model-free algorithms for continuous control -- deterministic policy gradient and stochas...
    - Explore

    Deep Reinforcement Learning in Parameterized Action Space
    Recent work has shown that deep neural networks are capable of approximating both value functions and policies in reinforcement learning domains featuring continuous state and action spaces. However, ...
    - Explore

    Compatible Value Gradients for Reinforcement Learning of Continuous Deep Policies
    This paper proposes GProp, a deep reinforcement learning algorithm for continuous policies with compatible function approximation. The algorithm is based on two innovations. Firstly, we present a temp...
    - Explore

    Continuous control with deep reinforcement learning
    We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model-free algorithm based on the deterministic policy gradient that can opera...
    - Explore

    High-Dimensional Continuous Control Using Generalized Advantage Estimation
    Policy gradient methods are an appealing approach in reinforcement learning because they directly optimize the cumulative reward and can straightforwardly be used with nonlinear function approximators...
    - Explore

    Learning Visual Predictive Models of Physics for Playing Billiards
    The ability to plan and execute goal specific actions in varied, unexpected settings is a central requirement of intelligent agents. In this paper, we explore how an agent can be equipped with an inte...
    - Explore

    On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models
    This paper addresses the general problem of reinforcement learning (RL) in partially observable environments. In 2013, our large RL recurrent neural networks (RNNs) learned from scratch to drive simul...
    - Explore

    Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models
    Data-efficient reinforcement learning (RL) in continuous state-action spaces using very high-dimensional observations remains a key challenge in developing fully autonomous systems. We consider a part...
    - Explore

    Action-Conditional Video Prediction using Deep Networks in Atari Games
    Motivated by vision-based reinforcement learning (RL) problems, in particular Atari games from the recent benchmark Aracade Learning Environment (ALE), we consider spatio-temporal prediction problems ...
    - Explore

Teky.Me : A Machine Intelligence Community
x

Interested In Machine Intelligence?

Explore