Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
3
0
4
29
Forget trial-and-error: this paper mathematically derives the *optimal* positional encoding for Transformers using multidimensional scaling, revealing that ALiBi is surprisingly close.
Imagine transformers that grow and prune themselves, learning the optimal architecture for any task without human guesswork.
Ditch k-means: DDCL's end-to-end differentiable architecture for unsupervised representation learning outperforms non-differentiable ablations by 65% and DeepCluster by 122% in clustering accuracy.