ESBN Transformer (wip)

An attempt to merge ESBN with Transformers, to endow Transformers with the ability to emergently bind symbols and improve extrapolation. The resulting architecture will be benchmarked with the Give-N task as outlined in this paper, commonly used to assess whether a child has acquired an understanding of counting.

Usage

import torch
from esbn_transformer import EsbnTransformer

model = EsbnTransformer(
    num_tokens = 256,
    dim = 512,
    depth = 4,
    max_seq_len = 512
)

x = torch.randint(0, 256, (1, 512))
out = model(x) # (1, 512, 256)

Citations

@misc{webb2020emergent,
    title   = {Emergent Symbols through Binding in External Memory}, 
    author  = {Taylor W. Webb and Ishan Sinha and Jonathan D. Cohen},
    year    = {2020},
    eprint  = {2012.14601},
    archivePrefix = {arXiv},
    primaryClass = {cs.AI}
}

@misc{dulberg2021modelling,
    title   = {Modelling the development of counting with memory-augmented neural networks}, 
    author  = {Zack Dulberg and Taylor Webb and Jonathan Cohen},
    year    = {2021},
    eprint  = {2105.10577},
    archivePrefix = {arXiv},
    primaryClass = {cs.AI}
}