Sökning: "mnist"

Visar resultat 1 - 5 av 76 uppsatser innehållade ordet mnist.

1. 1. Comparing energy efficiency of Leaky integrate-and-fire and Spike response neuron models in Spiking Neural Networks

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Majd Dawli; Imran Bahed Diva; [2023]
   Nyckelord :;

   Sammanfattning : Spiking Neural Networks (SNNs) are a type of neural network that is designed to mimic the way neurons function in our brains. While there have been notable advancements in developing SNNs, energy consumption hasn't been studied to the same extent. This gets especially relevant with steadily increasing network sizes. LÄS MER

3. 2. Implementing a Network Optimized Federated Learning Method From the Ground up

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Gustav Källander; Henning Norén; [2023]
   Nyckelord :;

   Sammanfattning : This bachelor thesis presents the implementation ofa simple fully connected neural network (FCNN) and federatedneural network with stochastic quantization from scratch andcompares their performance. Federated learning enables multipleparties to contribute to a machine learning model withoutsharing their sensitive data. LÄS MER

4. 3. Confidential Federated Learning with Homomorphic Encryption

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Zekun Wang; [2023]
   Nyckelord :Cloud Technology; Confidential Computing; Federated Learning; Homomorphic Encryption; Trusted Execution Environment; Molnteknik; Konfidentiell databehandling; Federerad inlärning; Homomorfisk kryptering; Betrodd körningsmiljö;

   Sammanfattning : Federated Learning (FL), one variant of Machine Learning (ML) technology, has emerged as a prevalent method for multiple parties to collaboratively train ML models in a distributed manner with the help of a central server normally supplied by a Cloud Service Provider (CSP). Nevertheless, many existing vulnerabilities pose a threat to the advantages of FL and cause potential risks to data security and privacy, such as data leakage, misuse of the central server, or the threat of eavesdroppers illicitly seeking sensitive information. LÄS MER

5. 4. Modelling synaptic rewiring in brain-like neural networks for representation learning

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Kunal Bhatnagar; [2023]
   Nyckelord :Adaptive Sparsity; Computational Neuroscience; Rewiring; Structural Plasticity; Brain-like Computing; Neural Networks; Hebbian Learning; Adaptiv gleshet; beräkningsneurovetenskap; omkoppling; strukturell plasticitet; Hjärnliknande beräkning; Neurala Nätverk; Hebbskt lärande;

   Sammanfattning : This research investigated the concept of a sparsity method inspired by the principles of structural plasticity in the brain in order to create a sparse model of the Bayesian Confidence Propagation Neural Networks (BCPNN) during the training phase. This was done by extending the structural plasticity in the implementation of the BCPNN. LÄS MER

7. 5. Building a Deep Neural Network From Scratch

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Fredrik Sundström; Samppa Raittila; [2023]
   Nyckelord :;

   Sammanfattning : Machine learning is becoming increasingly common in our society and is predictedto have a major impact in the future. Therefore, it would be both interesting and valuable tohave a deep understanding of one of the most used algorithms in machine learning, deepneural network. LÄS MER