Softmax encounters large computing cost when the output vocabulary size is very large. Some feasible approaches will be explained under the circumstance of skip-gram pretraining task.
Reasoning the relations between objects and their properties is a hallmark of intelligence. Here are some notes about the relational reasoning neural networks.
This is an introduction of variant Transformers.
Calculate the # of trainable parameters by hand.
Dynamic Programming (DP) is ubiquitous in NLP, such as Minimum Edit Distance, Viterbi Decoding, forward/backward algorithm, CKY algorithm, etc.
Some implementation magic.
The main aim of conv op is to extract useful features for downstream tasks. And different filters could intuitionally extract different aspect of features via backprop during training. Afterward, all the extracted features are combined to make decisions.