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Xu Owen He

Alumni

Publications

Adaptive Computation Pruning for the Forgetting Transformer
Zhixuan Lin
Johan Samir Obando Ceron
Xu Owen He
Aaron Courville
The recently proposed Forgetting Transformer (FoX) incorporates a forget gate into softmax attention and has shown consistently better or on… (see more)-par performance compared to the standard RoPE-based Transformer. Notably, many attention heads in FoX tend to forget quickly, causing their output at each timestep to rely primarily on local context. Based on this observation, we propose Adaptive Computation Pruning (ACP) for FoX, a method that dynamically prunes computations involving input-output dependencies that are strongly decayed by the forget gate. In particular, our method performs *provably safe* pruning via a dynamically set pruning threshold that guarantees the pruned attention weights are negligible. We apply ACP to language model pretraining with FoX and show it consistently reduces the number of FLOPs and memory accesses in softmax attention by around 70\% across different model sizes and context lengths, resulting in a roughly 50\% to 70\% reduction in attention runtime (or a 2--3
2025-07-07
colmweb.org/COLM/2025/Conference (accepted)
openreview.net
openreview.net
TAPNext: Tracking Any Point (TAP) as Next Token Prediction
Artem Zholus
Carl Doersch
Yi Yang
Skanda Koppula
Viorica Patraucean
Xu Owen He
Ignacio Rocco
Mehdi S. M. Sajjadi
Sarath Chandar
Ross Goroshin
2025-04-08
ArXiv (preprint)
arxiv.org
arxiv.org
TAPNext: Tracking Any Point (TAP) as Next Token Prediction
Artem Zholus
Carl Doersch
Yi Yang
Skanda Koppula
Viorica Patraucean
Xu Owen He
Ignacio Rocco
Mehdi S. M. Sajjadi
Sarath Chandar
Ross Goroshin
2025-04-01
arXiv (published)
doi.org
arxiv.org
TRecViT: A Recurrent Video Transformer
Viorica Puatruaucean
Xu Owen He
Joseph Heyward
Chuhan Zhang
Mehdi S. M. Sajjadi
George-Cristian Muraru
Artem Zholus
Mahdi Karami
Ross Goroshin
Yutian Chen 0001
Simon Kayode Osindero
João Carreira
Razvan Pascanu
We propose a novel block for video modelling. It relies on a time-space-channel factorisation with dedicated blocks for each dimension: gate… (see more)d linear recurrent units (LRUs) perform information mixing over time, self-attention layers perform mixing over space, and MLPs over channels. The resulting architecture TRecViT performs well on sparse and dense tasks, trained in supervised or self-supervised regimes. Notably, our model is causal and outperforms or is on par with a pure attention model ViViT-L on large scale video datasets (SSv2, Kinetics400), while having
2024-12-18
ArXiv (preprint)
doi.org
arxiv.org