sametoymak
No user record in our sample, but sametoymak has activity below (stories or comments). Likely we have partial data — the full bulk-load will fill profiles in.
No user record in our sample, but sametoymak has activity below (stories or comments). Likely we have partial data — the full bulk-load will fill profiles in.
IMO it is important to understand transformer mechanics through core ML themes like SVM and feature-selection. Our results are not interpretation, they are mathematically rigorous and numerically verifiable. That said,…
Here is what I meant: Standard SVM classifier: Maps an input sequence to a 0-1 label. Example: Take a paragraph and return its sentiment. During training, label is specified. Transformer's SVM: Takes input sequence,…
This seems related to NTK literature i.e. wide neural nets behave like kernel regression. NTK is a great tool but a notable weakness is kernel view doesn't explain how the model learns new features. Transformer is also…
This SVM summarizes the training dynamics of the attention layer, so there is no hidden-layer. It operates on the token embeddings of that layer. Essentially, weights of the attention layer converge (in direction) to…
I am one of the authors. The most critical aspect is that transformer is a "different kind of SVM". It solves an SVM that separates 'good' tokens within each input sequence from 'bad' tokens. This SVM serves as a…