The Ambiguity of Weight Sharing in Neural Network Architectures
When it comes to neural network architectures, weight sharing can be a tricky subject. While some parts may seem straightforward, others can be quite ambiguous. One such example is the weight sharing between embedding layers and pre-softmax linear transformations.
Typically, we may think that all we need is a linear layer and a softmax function to generate a vector of probabilities for the next word in a sequence. However, the reality can be more complex.
Upon deeper exploration, we come across the concept of sharing the same weight matrix between the two embedding layers and the pre-softmax linear transformation. This raises questions and confusion:
- Why are we sharing weights between the embedding layer of the Decoder and its last layer?
- Shouldn’t the weights be shared with the Encoder’s embedding layer, considering the different vocabularies?
While pondering over these questions, a valuable insight from the community sheds some light on the matter:
- The decision to share source and target embeddings is often a design choice, influenced by the token vocabulary.
As we delve deeper into the world of neural network architectures, it becomes evident that weight sharing is not just about reducing parameters but also about optimizing model performance and handling varying vocabularies.
So, the next time you encounter weight sharing in a neural network architecture, remember that there’s more to it than meets the eye. Embrace the ambiguity, explore the possibilities, and uncover the true power of weight sharing in shaping intelligent systems.