3768 papers with code • 10 benchmarks • 8 datasets
Algorithms trying to solve the general task of classification.
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting.
We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks.
We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.
Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change.
We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning.