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Found 3 papers, 1 papers with code
Understanding and Minimising Outlier Features in Neural Network Training
Outlier Features (OF) are neurons whose activation magnitudes significantly exceed the average over a neural network's (NN) width.
Faster Causal Attention Over Large Sequences Through Sparse Flash Attention
While many works have proposed schemes to sparsify the attention patterns and reduce the computational overhead of self-attention, those are often limited by implementations concerns and end up imposing a simple and static structure over the attention matrix.
Graph Neural Networks Go Forward-Forward
We present the Graph Forward-Forward (GFF) algorithm, an extension of the Forward-Forward procedure to graphs, able to handle features distributed over a graph's nodes.
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