Many tasks in AI require the collaboration of multiple agents. Typically, the communication protocol between agents is manually specified and not altered during training. In this paper we explore a simple neural model, called CommNN, that uses continuous communication for fully cooperative tasks. The model consists of multiple agents and the communication between them is learned alongside their...

Deep neural network architectures have recently produced excellent results in a variety of areas in artificial intelligence and visual recognition, well surpassing traditional shallow architectures trained using hand-designed features. The power of deep networks stems both from their ability to perform local computations followed by pointwise non-linearities over increasingly larger receptive f...

Human eye disease classification adapted by many pieces of research in the last decade due to importance organ for humans and evolution techniques. External diseases classified this paper using backpropagation with non-linear cyclic learning rate based on welch estimation as a step improving performance backpropagation. As result, Classification accuracy achieved is (93.22%).

This paper focuses on on-line learning procedures for locally recurrent neural networks with emphasis on multilayer perceptron (MLP) with infinite impulse response (IIR) synapses and its variations which include generalized output and activation feedback multilayer networks (MLN's). We propose a new gradient-based procedure called recursive backpropagation (RBP) whose on-line version, causal re...

Logo mobil sendiri sangat dalam membedakan sebuah adalah logo kendaraan yang berfungsi untuk mengenalkan kepada masyarakat tentang brand mereka. Pada klasifikasi ini agar paham mengetahui telah ada. Oleh karena itu, penelitian mengusulkan menggunakan JST -backpropagation dan decision tree . Penelitian bertujuan mengklasifikasikan jenis ada di indonesia. Serta dapat memudahkan awam pengenalan mo...

Building a world model takes exponential computational costs with the number of obstacles. In real world applications are usually many obstacles, possibly changing their positions over time. In order to cope with a changing environment, a solution has to be adaptive. In order to plan complex trajectories, a system that plans hierarchically shows many advantages. In this article we report on res...

Sample efficiency is an important topic in reinforcement learning. With limited data and experience, how can we converge to a good policy more quickly? In this paper, we propose a new experience replay method called Reward Backpropagation, which gives higher minibatch sampling priority to those (s, a, r, s′) with r 6= 0 and then propagate the priority backward to its previous transition once it...

The inverse scattering problem for an acoustic medium is formulated by using the variable background Born approximation. A constant density acoustic medium is probed by a wide-band point source, and the scattered field is observed along a curved receiver array located outside the region where the medium velocity is different from the assumed background velocity function. The solution that we pr...

Backpropagation algorithm is the cornerstone for neural network analysis. Paper extends it for training any derivatives of neural network’s output with respect to its input. By the dint of it feedforward networks can be used to solve or verify solutions of partial or simple, linear or nonlinear differential equations. This method vastly differs from traditional ones like finite differences on a...

Truncated Backpropagation Through Time (truncated BPTT, [Jae05]) is a widespread method for learning recurrent computational graphs. Truncated BPTT keeps the computational benefits of Backpropagation Through Time (BPTT [Wer90]) while relieving the need for a complete backtrack through the whole data sequence at every step. However, truncation favors short-term dependencies: the gradient estimat...