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kpzhang93.github.io | ||
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sander.ai
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| | | | | Slides for my talk at the Deep Learning London meetup | |
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colah.github.io
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| | | | | [AI summary] This article explains the structure, functionality, and significance of convolutional neural networks (CNNs) in pattern recognition and computer vision, highlighting their applications and breakthroughs. | |
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ssc.io
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| | | | | Retrieval augmentation enables large language models to take advantage of external knowledge, for example on tasks like question answering and data imputation. However, the performance of such retrieval-augmented models is limited by the data quality of their underlying retrieval corpus. In this paper, we propose an algorithm based on multilinear extension for evaluating the data importance of retrieved data points. There are exponentially many terms in the multilinear extension, and one key contribution of this paper is a polynomial time algorithm that computes exactly, given a retrieval-augmented model with an additive utility function and a validation set, the data importance of data points in the retrieval corpus using the multilinear extension of the mo... | |
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sirupsen.com
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| | | [AI summary] The article provides an in-depth explanation of how to build a neural network from scratch, focusing on the implementation of a simple average function and the introduction of activation functions for non-linear tasks. It discusses the use of matrix operations, the importance of GPUs for acceleration, and the role of activation functions like ReLU. The author also outlines next steps for further exploration, such as expanding the model, adding layers, and training on datasets like MNIST. | ||
