Searched for: subject%3A%22Pruning%22
(1 - 8 of 8)
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Dekhovich, A. (author), Tax, D.M.J. (author), Sluiter, M.H.F. (author), Bessa, M.A. (author)
Current deep neural networks (DNNs) are overparameterized and use most of their neuronal connections during inference for each task. The human brain, however, developed specialized regions for different tasks and performs inference with a small fraction of its neuronal connections. We propose an iterative pruning strategy introducing a simple...
journal article 2024
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Sochirca, Dan (author)
Code generation models have become more popular recently, due to the fact that they assist developers in writing code in a more productive manner. While these large models deliver impressive performance, they require significant computational resources and memory, making them difficult to deploy and expensive to train. Additionally, their large...
bachelor thesis 2023
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Vos, D.A. (author), Verwer, S.E. (author)
Decision trees are popular models for their interpretation properties and their success in ensemble models for structured data. However, common decision tree learning algorithms produce models that suffer from adversarial examples. Recent work on robust decision tree learning mitigates this issue by taking adversarial perturbations into...
conference paper 2023
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Klazinga, Rembrandt (author)
Autoencoders seek to encode their input into a bottleneck of latent neurons, and then decode it to reconstruct the input. However, if the input data has an intrinsic dimension (ID) smaller than the number of latent neurons in the bottleneck, this encoding becomes redundant. <br/>In this paper, we study using the Early-Bird (EB) technique, a...
master thesis 2022
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Băcuieți, Norica (author), Batina, Lejla (author), Picek, S. (author)
At CRYPTO’19, A. Gohr proposed neural distinguishers for the lightweight block cipher Speck32/64, achieving better results than the state-of-the-art at that point. However, the motivation for using that particular architecture was not very clear; therefore, in this paper, we study the depth-10 and depth-1 neural distinguishers proposed by...
conference paper 2022
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Yilmaz, Kaan (author), Yorke-Smith, N. (author)
In line with the growing trend of using machine learning to help solve combinatorial optimisation problems, one promising idea is to improve node selection within a mixed integer programming (MIP) branch-and-bound tree by using a learned policy. Previous work using imitation learning indicates the feasibility of acquiring a node selection policy...
journal article 2021
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Yilmaz, M.K. (author)
In line with the growing trend of using machine learning to improve solving of combinatorial optimisation problems, one promising idea is to improve node selection within a mixed integer programming branch-and-bound tree by using a learned policy. In contrast to previous work using imitation learning, our policy is focused on learning which of a...
master thesis 2020
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Jonker, Arnoud (author)
Due to a high contribution of human error in fatal traffic accidents, efforts in research and industry for automating vehicles steadily increased the last 3 decades. To reduce accidents with other road users, automated recognition and classification of road users is crucial. The most accurate models are large convolutional neural networks,...
master thesis 2020
Searched for: subject%3A%22Pruning%22
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