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Limits of Transformer Language Models on Learning Algorithmic Compositions
We analyze the capabilities of Transformer language models on learning discrete algorithms.
TCNCA: Temporal Convolution Network with Chunked Attention for Scalable Sequence Processing
We build upon their approach by replacing the linear recurrence with a special temporal convolutional network which permits larger receptive field size with shallower networks, and reduces the computational complexity to $O(L)$.
Factorizers for Distributed Sparse Block Codes
We provide a methodology to flexibly integrate our factorizer in the classification layer of CNNs with a novel loss function.
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