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Yan Zuo

12 accepted papers

2026

Taming Curvature: Architecture Warm-up for Stable Transformer Training

Sameera Ramasinghe, Thalaiyasingam Ajanthan, Hadi Mohaghegh Dolatabadi, Chamin P Hewa Koneputugodage, Gil Avraham, Violetta Shevchenko +3
ICLR 2026poster

Training billion-parameter Transformers is often brittle, with transient loss spikes and divergence that waste compute. Even though the recently developed Edge of Stability (EoS) theory provides a powerful tool to understand and control the stability of optimization methods via the (preconditioned)…

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2025

Mixtures of Subspaces for Bandwidth Efficient Context Parallel Training

Sameera Ramasinghe, Thalaiyasingam Ajanthan, Hadi Mohaghegh Dolatabadi, Gil Avraham, Violetta Shevchenko, Yan Zuo +2
NeurIPS 2025poster

Pretraining language models with extended context windows enhances their ability to leverage rich information during generation. Existing methods split input sequences into chunks, broadcast them across multiple devices, and compute attention block by block which incurs significant communication ove…

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2025

Nesterov Method for Asynchronous Pipeline Parallel Optimization

Thalaiyasingam Ajanthan, Sameera Ramasinghe, Yan Zuo, Gil Avraham, Alexander Long
ICML 2025poster

Pipeline Parallelism (PP) enables large neural network training on small, interconnected devices by splitting the model into multiple stages. To maximize pipeline utilization, asynchronous optimization is appealing as it offers 100% pipeline utilization by construction. However, it is inherently cha…

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2025

Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism

Sameera Ramasinghe, Thalaiyasingam Ajanthan, Gil Avraham, Yan Zuo, Alexander Long
NeurIPS 2025poster

Scaling models has led to significant advancements in deep learning, but training these models in decentralized settings remains challenging due to communication bottlenecks. While existing compression techniques are effective in data-parallel, they do not extend to model parallelism. Unlike data-pa…

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2025

Unextractable Protocol Models: Collaborative Training and Inference without Weight Materialization

Alexander Long, Chamin P Hewa Koneputugodage, Thalaiyasingam Ajanthan, Yan Zuo, Gil Avraham, Violetta Shevchenko +2
NeurIPS 2025poster

We consider a decentralized setup in which the participants collaboratively train and serve a large neural network, and where each participant only processes a subset of the model. In this setup, we explore the possibility of unmaterializable weights, where a full weight set is never available to a…

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2024

ViewFusion: Towards Multi-View Consistency via Interpolated Denoising

Xianghui Yang, Yan Zuo, Sameera Ramasinghe, Loris Bazzani, Gil Avraham, Anton van den Hengel
CVPR 2024poster

Novel-view synthesis through diffusion models has demonstrated remarkable potential for generating diverse and high-quality images. Yet the independent process of image generation in these prevailing methods leads to challenges in maintaining multiple-view consistency. To address this we introduce V…

Cited by 5Source ↗PDF ↗code ↗Scholar ↗
2023

Mixed-Variable Black-Box Optimisation Using Value Proposal Trees

Yan Zuo, Vu Nguyen, Amir Dezfouli, David Alexander, Benjamin Ward Muir, Iadine Chades
AAAI 2023technical

Many real-world optimisation problems are defined over both categorical and continuous variables, yet efficient optimisation methods such as Bayesian Optimisation (BO) are ill-equipped to handle such mixed-variable search spaces. The optimisation breadth introduced by categorical variables in the mi…

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2022

Learning Instance and Task-Aware Dynamic Kernels for Few-Shot Learning

Rongkai Ma, Pengfei Fang, Gil Avraham, Yan Zuo, Tianyu Zhu, Tom Drummond +1
ECCV 2022poster

"Learning and generalizing to novel concepts with few samples (Few-Shot Learning) is still an essential challenge to real-world applications. A principle way of achieving few-shot learning is to realize a model that can rapidly adapt to the context of a given task. Dynamic networks have been shown c…

Cited by 16Source ↗PDF ↗code ↗Scholar ↗
2019

EMPNet: Neural Localisation and Mapping Using Embedded Memory Points

Gil Avraham, Yan Zuo, Thanuja Dharmasiri, Tom Drummond
ICCV 2019poster

Continuously estimating an agent's state space and a representation of its surroundings has proven vital towards full autonomy. A shared common ground among systems which successfully achieve this feat is the integration of previously encountered observations into the current state being estimated.…

Cited by 24PDF ↗Scholar ↗