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Violetta Shevchenko

9 accepted papers

2026

Factored Gossip DiLoCo: Reducing Blocking Communication within DiLoCo

Chamin Hewa Koneputugodage, Thalaiyasingam Ajanthan, Sameera Ramasinghe, Hadi Mohaghegh Dolatabadi, Shamane Siriwardhana, Gil Avraham +4
ICML 2026poster

To make large-scale distributed training practical outside high-bandwidth datacenters, we must reduce blocking, high-volume synchronization. While DiLoCo communicates infrequently, its outer synchronization remains bandwidth-heavy and brittle to stragglers and transient failures. We relax exact sync…

Cited by 0Source ↗Scholar ↗
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

SRSR: Enhancing Semantic Accuracy in Real-World Image Super-Resolution with Spatially Re-Focused Text-Conditioning

Chen Chen, Majid Abdolshah, Violetta Shevchenko, Hongdong Li, Chang Xu, Pulak Purkait
NeurIPS 2025poster

Existing diffusion-based super-resolution approaches often exhibit semantic ambiguities due to inaccuracies and incompleteness in their text conditioning, coupled with the inherent tendency for cross-attention to divert towards irrelevant pixels. These limitations can lead to semantic misalignment a…

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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

A sampling theory perspective on activations for implicit neural representations

Hemanth Saratchandran, Sameera Ramasinghe, Violetta Shevchenko, Alexander Long, Simon Lucey
ICML 2024poster

Implicit Neural Representations (INRs) have gained popularity for encoding signals as compact, differentiable entities. While commonly using techniques like Fourier positional encodings or non-traditional activation functions (e.g., Gaussian, sinusoid, or wavelets) to capture high-frequency content,…

Cited by 6Source ↗PDF ↗Scholar ↗
2024

Accept the Modality Gap: An Exploration in the Hyperbolic Space

Sameera Ramasinghe, Violetta Shevchenko, Gil Avraham, Ajanthan Thalaiyasingam
CVPR 2024highlight

Recent advancements in machine learning have spotlighted the potential of hyperbolic spaces as they effectively learn hierarchical feature representations. While there has been progress in leveraging hyperbolic spaces in single-modality contexts its exploration in multimodal settings remains under e…

Cited by 10Source ↗PDF ↗Scholar ↗
2024

BLiRF: Bandlimited Radiance Fields for Dynamic Scene Modeling

Sameera Ramasinghe, Violetta Shevchenko, Gil Avraham, Anton van den Hengel
AAAI 2024technical

Inferring the 3D structure of a non-rigid dynamic scene from a single moving camera is an under-constrained problem. Inspired by the remarkable progress of neural radiance fields (NeRFs) in photo-realistic novel view synthesis of static scenes, it has also been extended to dynamic settings. Such me…

Cited by 7Source ↗PDF ↗Scholar ↗
2024

Improving the Convergence of Dynamic NeRFs via Optimal Transport

Sameera Ramasinghe, Violetta Shevchenko, Gil Avraham, Hisham Husain, Anton van den Hengel
ICLR 2024poster

Synthesizing novel views for dynamic scenes from a collection of RGB inputs poses significant challenges due to the inherent under-constrained nature of the problem. To mitigate this ill-posedness, practitioners in the field of neural radiance fields (NeRF) often resort to the adoption of intricate…

Cited by 1Source ↗PDF ↗Scholar ↗