Explore >> Select a destination
|
You are here 
|
kpzhang93.github.io | ||
| | | | |
ankane.org
|
|
| | | | | Welcome to another installment of deep learning in Ruby. Today, we'll look at FER+, a deep convolutional neural network for emotion recognition... | |
| | | | |
ssc.io
|
|
| | | | | 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... | |
| | | | |
sander.ai
|
|
| | | | | Slides for my talk at the Deep Learning London meetup | |
| | | | |
www.v7labs.com
|
|
| | | Learn about the different types of neural network architectures. | ||
