Jun 11, 2020 · This paper suggests a new interpretation and generalized structure of the attention in Transformer and GAT.

Given that attention uses a kernel implicitly, we note that the kernel function measures the similarity under the induc- tive bias embedded in the kernel.

We propose a method which benefits from the observation that Vaswani et al.'s attention formulation comprises an implicit kernel distance calculation and report ...

Attention computes the dependency between representations, and it encourages the model to focus on the important selective features. Attention-based models ...

Attention computes the dependency between representations, and it encourages the model to focus on the important selective features. Attention-based models ...

In this paper, we present the hyper-convolution, a novel building block that implicitly encodes the convolutional kernel using spatial coordinates.

This paper presents an Implicit Composite Kernel (ICK) framework by combining a neural-network-implied kernel with a chosen kernel function.

In this paper we propose to learn the probability distribution representing a random feature kernel that we wish to use within kernel ridge regression (KRR).

"""Applies windowed causal kernel attention with query, key, value tensors. We partition the T-length input sequence into N chunks, each of.

Attention mechanisms play a crucial role in cognitive systems by allowing them to flexibly allocate cognitive resources. Transformers, in particular ...