Xu Owen He

Joseph Heyward

Chuhan Zhang

Mehdi S. M. Sajjadi

George-Cristian Muraru

Mahdi Karami

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… (voir plus)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

2025-12-31

Trans. Mach. Learn. Res. (publié)

Fine-Tuned In-Context Learners for Efficient Adaptation

Jörg Bornschein

Clare Lyle

Yazhe Li

Amal Rannen-Triki

Razvan Pascanu

When adapting large language models (LLMs) to a specific downstream task, two primary approaches are commonly employed: (1) prompt engineeri… (voir plus)ng, often with in-context few-shot learning, leveraging the model's inherent generalization abilities, and (2) fine-tuning on task-specific data, directly optimizing the model's parameters. While prompt-based methods excel in few-shot scenarios, their effectiveness often plateaus as more data becomes available. Conversely, fine-tuning scales well with data but may underperform when training examples are scarce. We investigate a unified approach that bridges these two paradigms by incorporating in-context learning directly into the fine-tuning process. Specifically, we fine-tune the model on task-specific data augmented with in-context examples, mimicking the structure of k-shot prompts. This approach, while requiring per-task fine-tuning, combines the sample efficiency of in-context learning with the performance gains of fine-tuning, leading to a method that consistently matches and often significantly exceeds both these baselines. To perform hyperparameter selection in the low-data regime, we propose to use prequential evaluation, which eliminates the need for expensive cross-validation and leverages all available data for training while simultaneously providing a robust validation signal. We conduct an extensive empirical study to determine which adaptation paradigm - fine-tuning, in-context learning, or our proposed unified approach offers the best predictive performance on a concrete data downstream-tasks.

2025-12-21

ArXiv (prépublication)

Adaptive Computation Pruning for the Forgetting Transformer

Zhixuan Lin

Johan Samir Obando Ceron

Aaron Courville

The recently proposed Forgetting Transformer (FoX) incorporates a forget gate into softmax attention and has shown consistently better or on… (voir plus)-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 (accepté)

openreview.net

TAPNext: Tracking Any Point (TAP) as Next Token Prediction

Carl Doersch

Yi Yang

Skanda Koppula

Viorica Patraucean

Ignacio Rocco

Mehdi S. M. Sajjadi

Sarath Chandar

2025-04-08

ArXiv (prépublication)

TAPNext: Tracking Any Point (TAP) as Next Token Prediction

Carl Doersch

Yi Yang

Skanda Koppula

Viorica Patraucean

Ignacio Rocco

Mehdi S. M. Sajjadi

Sarath Chandar

2025-04-01

arXiv (publié)

TRecViT: A Recurrent Video Transformer

Viorica Puatruaucean

Joseph Heyward

Chuhan Zhang

Mehdi S. M. Sajjadi

George-Cristian Muraru

Mahdi Karami

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… (voir plus)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 (prépublication)