… neural networks as implicit representation of data has driven growing interest in neural
functionals: models that can process other neural … and efficient neural functional architectures that …

… , we propose a formulation that uses Transformers as hypernetworks for INRs, where it can
… Transformers in meta-learning for directly inferring the whole weights in a neural function of …

… We show empirically that standard Transformers can be trained from … train Transformers to
in-context learn more complex function classes— ie, sparse linear functions, two-layer neural …

… work has demonstrated that Transformer neural networks are … These Vision Transformers
(ViT) operate almost identically to … driven artificial neural networks reveals functional properties …

Vision transformers have delivered tremendous success in representation learning. This is …
To cope with this challenge, we propose Adaptive Fourier Neural Operator (AFNO) as an …

… function prediction. Inspired by the recent advance, we develop a method to use a pre-trained
protein language transformer … graph neural networks (EGNN) to predict protein function. …

… The particular decision function found by a machine learning … -depth behavioral analyses
of neural network models that go … -based network, the Vision Transformer (ViT), which relaxes …

… has no effect on the network’s function (Hecht-Nielsen… equivariant neural functional can
guarantee that under a neuron … horizon (T = 5,000 for the Transformer setting, and T = 2,000 …

… that neuron–astrocyte networks can naturally perform the core computation of a Transformer,
a … The neural activation function ϕ plays a special role in our circuit. In order to match the …

… reduce a Neural GPU to a Universal Transformer. Ignoring … be the identity function, we assume
the transition function to be a … a Neural GPU. Note that the last step is where the Universal …