2,813 research outputs found
Sampled Policy Gradient for Learning to Play the Game Agar.io
In this paper, a new offline actor-critic learning algorithm is introduced:
Sampled Policy Gradient (SPG). SPG samples in the action space to calculate an
approximated policy gradient by using the critic to evaluate the samples. This
sampling allows SPG to search the action-Q-value space more globally than
deterministic policy gradient (DPG), enabling it to theoretically avoid more
local optima. SPG is compared to Q-learning and the actor-critic algorithms
CACLA and DPG in a pellet collection task and a self play environment in the
game Agar.io. The online game Agar.io has become massively popular on the
internet due to intuitive game design and the ability to instantly compete
against players around the world. From the point of view of artificial
intelligence this game is also very intriguing: The game has a continuous input
and action space and allows to have diverse agents with complex strategies
compete against each other. The experimental results show that Q-Learning and
CACLA outperform a pre-programmed greedy bot in the pellet collection task, but
all algorithms fail to outperform this bot in a fighting scenario. The SPG
algorithm is analyzed to have great extendability through offline exploration
and it matches DPG in performance even in its basic form without extensive
sampling
Comparative Analysis of MLP, CNN, and RNN Models in Automatic Speech Recognition: Dissecting Performance Metric
This study conducts a comparative analysis of three prominent machine learning models: Multi-Layer Perceptrons (MLP), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) in the field of automatic speech recognition (ASR). This research is distinct in its use of the LibriSpeech 'test-clean' dataset, selected for its diversity in speaker accents and varied recording conditions, establishing it as a robust benchmark for ASR performance evaluation. Our approach involved preprocessing the audio data to ensure consistency and extracting Mel-Frequency Cepstral Coefficients (MFCCs) as the primary features, crucial for capturing the nuances of human speech. The models were meticulously configured with specific architectural details and hyperparameters. The MLP and CNN models were designed to maximize their pattern recognition capabilities, while the RNN (LSTM) was optimized for processing temporal data. To assess their performance, we employed metrics such as precision, recall, and F1-score. The MLP and CNN models demonstrated exceptional accuracy, with scores of 0.98 across these metrics, indicating their effectiveness in feature extraction and pattern recognition. In contrast, the LSTM variant of RNN showed lower efficacy, with scores below 0.60, highlighting the challenges in handling sequential speech data. The results of this study shed light on the differing capabilities of these models in ASR. While the high accuracy of MLP and CNN suggests potential overfitting, the underperformance of LSTM underscores the necessity for further refinement in sequential data processing. This research contributes to the understanding of various machine learning approaches in ASR and paves the way for future investigations. We propose exploring hybrid model architectures and enhancing feature extraction methods to develop more sophisticated, real-world ASR systems. Additionally, our findings underscore the importance of considering model-specific strengths and limitations in ASR applications, guiding the direction of future research in this rapidly evolving field
CLASSIFICATION OF COMPLEX TWO-DIMENSIONAL IMAGES IN A PARALLEL DISTRIBUTED PROCESSING ARCHITECTURE
Neural network analysis is proposed and evaluated as a method of analysis of
marine biological data, specifically images of plankton specimens. The
quantification of the various plankton species is of great scientific importance, from
modelling global climatic change to predicting the economic effects of toxic red
tides. A preliminary evaluation of the neural network technique is made by the
development of a back-propagation system that successfully learns to distinguish
between two co-occurring morphologically similar species from the North Atlantic
Ocean, namely Ceratium arcticum and C. longipes. Various techniques are
developed to handle the indeterminately labelled source data, pre-process the
images and successfully train the networks. An analysis of the network solutions
is made, and some consideration given to how the system might be extended.Plymouth Marine Laborator
A Machine Learning Tutorial for Operational Meteorology, Part II: Neural Networks and Deep Learning
Over the past decade the use of machine learning in meteorology has grown
rapidly. Specifically neural networks and deep learning have been used at an
unprecedented rate. In order to fill the dearth of resources covering neural
networks with a meteorological lens, this paper discusses machine learning
methods in a plain language format that is targeted for the operational
meteorological community. This is the second paper in a pair that aim to serve
as a machine learning resource for meteorologists. While the first paper
focused on traditional machine learning methods (e.g., random forest), here a
broad spectrum of neural networks and deep learning methods are discussed.
Specifically this paper covers perceptrons, artificial neural networks,
convolutional neural networks and U-networks. Like the part 1 paper, this
manuscript discusses the terms associated with neural networks and their
training. Then the manuscript provides some intuition behind every method and
concludes by showing each method used in a meteorological example of diagnosing
thunderstorms from satellite images (e.g., lightning flashes). This paper is
accompanied with an open-source code repository to allow readers to explore
neural networks using either the dataset provided (which is used in the paper)
or as a template for alternate datasets
Application of Super Resolution Convolutional Neural Networks (SRCNNs) to enhance medical images resolution
The importance of resolution is crucial when working with medical images. The possibility to visualize details lead to a more accurate diagnosis and makes segmentation easier. However, obtention of high-resolution medical images requires of long acquisition times. In clinical environments, lack of time leads to the acquisition of low-resolution images.
Super Resolution (SR) consist in post-processing images in order to enhance its resolution. During the last years, a branch of SR is getting promising results. This branch focuses in the application of Convolutional Neural Networks (CNNs) to the images.
This project is intended to create a network able to enhance resolution of knee MR stored in DICOM format. Different networks are proposed, and evaluation is made by computing Peak Signal-to-Noise Ratio (PSNR) and normalized Cross-Correlation. One of the networks proposed, SR-DCNN, presented better results than the conventional method, bicubic interpolation. Finally, visual comparison of the SR-DCNN and bicubic interpolation also showed that the network proposed outperforms the conventional methods.IngenierÃa Biomédic
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