Convolutional Neural Networks are specialized deep learning architectures designed for grid-structured data, particularly images. CNNs use learnable filters
Abstract page for arXiv paper 1703.01961: Multiplicative Normalizing Flows for Variational Bayesian Neural Networks
# Predicting Dynamic Properties of Heap Allocations Using Neural Networks Trained on Static Code Guoqing Harry Xu 2023 ACM SIGPLAN International Symposium on Memory Management (ISMM 2023) ## Abstract Memory allocators and runtime systems can leverage dynamic properties of heap allocations – such as object lifetimes, hotness or access correlations – to improve performance and resource consumption. A significant amount of work has focused on approaches that collect this information in performance profiles
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jarxiv Japanese arxiv コンテンツへスキップ ホーム ← On the attribution of confidence to large language models AutoBencher: Creating Salient, Novel, Difficult Datasets for Language Models → SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks 投稿日: 2024年7月12日 作成者: jarxiv 要約 大規模な言語モデルのサイズが拡大し続けるにつれて、それを実行するために必要な計算リソースも増加します。 スパイキング
LLMs generate code as flat token sequences, then typecheck after the fact. Can we make the output space itself be typed?
Neural Turing Machines - Graves et al. 2014 (Google DeepMind) A Neural Turing Machine is a Neural Network extended with a working memory, which as we'll see, gives it very impressive learning abilities. A Neural Turing Machine (NTM) architecture contains two basic components: a neural network controller and a memory bank. Like most neural networks
Python, Machine Learning, Machine Learning, Deep Learning, Neural Networks
Tree-structured recursive neural networks (TreeRNNs) for sentence meaning have been successful for many applications, but it remains an open question whether the fixed-length representations that
Knowledge Distillation shrinks massive neural networks by transferring their ‘know-how’ from a large, complex teacher model to a smaller, more efficient student model, retaining high performance with fewer resources. This technique enables smaller models to master the capabilities of giants like GPT-4, making powerful AI accessible in resource-constrained environments without sacrificing accuracy