Papers with TPRs
Tensor Product Generation Networks for Deep NLP Modeling (N18-1)
Copied to clipboard
| Challenge: | Using Tensor Product Representations (TPRs) we propose a new architecture for natural language processing based on the principle that hypothesis space for learning includes network hypotheses that are independently known to be suitable for performing the target task. |
| Approach: | They propose a Tensor Product Generation Network (TPGN) which is capable of carrying out TPR computation but uses unconstrained deep learning to design its internal representations. |
| Outcome: | The proposed architecture outperforms baselines on the COCO dataset and can interpret internal representations and operations. |
Invertible Tree Embeddings using a Cryptographic Role Embedding Scheme (2020.coling-main)
Copied to clipboard
| Challenge: | Unlike previous attempts, this method does not come at the cost of intractable representation size; it works well when there is sufficient randomness in the representation scheme for simple data and providing an upper bound on its error. |
| Approach: | They propose a method for embedding trees in a vector space based on Tensor-Product Representations (TPRs) that allows for inversion: the retrieval of the original tree structure and nodes from the vectorial embeddment. |
| Outcome: | The proposed method can provide invertibility with error 1% that previous methods would require 8.6 1057 dimensions to represent. |