4 citations · 8 across the 4 of their papers we have counts for
4 papers · 1 filter
Block Parallelism For Efficient Distributed Long-Context Diffusion Language Model Training
Tarun Suresh, Pranshu Chaturvedi, Hangoo Kang +4
Block diffusion language models (BDLMs) combine autoregressive dependencies across blocks with parallel denoising within blocks, but long-context training is constrained by distrib…
The Hedgehog & the Porcupine: Expressive Linear Attentions with Softmax Mimicry
Michael Zhang, Kush Bhatia, Hermann Kumbong +1
Linear attentions have shown potential for improving Transformer efficiency, reducing attention's quadratic complexity to linear in sequence length. This holds exciting promise for…
FlashFFTConv: Efficient Convolutions for Long Sequences with Tensor Cores
Daniel Y. Fu, Hermann Kumbong, Eric Nguyen +1
Convolution models with long filters have demonstrated state-of-the-art reasoning abilities in many long-sequence tasks but lag behind the most optimized Transformers in wall-clock…
Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions
Stefano Massaroli, Michael Poli, Daniel Y. Fu +11
Recent advances in attention-free sequence models rely on convolutions as alternatives to the attention operator at the core of Transformers. In particular, long convolution sequen…