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Chamin P Hewa Koneputugodage

4 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

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

Neural Experts: Mixture of Experts for Implicit Neural Representations

Yizhak Ben-Shabat, Chamin P Hewa Koneputugodage, Sameera Ramasinghe, Stephen Gould
NeurIPS 2024poster

Implicit neural representations (INRs) have proven effective in various tasks including image, shape, audio, and video reconstruction. These INRs typically learn the implicit field from sampled input points. This is often done using a single network for the entire domain, imposing many global constr…

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