1 citations · 1 across the 13 of their papers we have counts for
13 papers
Masked Language Flow Models
Iskander Azangulov, Kianoosh Ashouritaklimi, Leo Zhang +2
Masked Diffusion Models (MDMs) promise fast, parallel language generation, but their reverse transition factorises across token positions -- an approximation that breaks down in th…
Variance-Tilted Diffusion Models for Diverse Sampling
Iskander Azangulov, Leo Zhang, Kianoosh Ashouritaklimi
Diffusion models are typically sampled independently, even when the downstream objective is to obtain a diverse set of candidates. We introduce a variance-weighted batch distributi…
Image-to-Video Diffusion: From Foundations to Open Frontiers
Xianlong Wang, Wenbo Pan, Shijia Zhou +6
Diffusion-based \textit{image-to-video} (I2V) generation has become a central direction in generative models by turning a reference image, with optional conditions, into a temporal…
Sampling from Flow Language Models via Marginal-Conditioned Bridges
Iskander Azangulov, Leo Zhang
Flow Language Models (FLMs) are a recently introduced class of language models which adapt continuous flow matching for one-hot encoded token sequences. Their denoisers have a spec…
Towards a Frugal Photosynthesis Sensing Toolkit for Data-Driven Plant Science Education and Exploration
Qitong Li, Raj Nileshbhai Dave, Rhema Amanda Phiri +7
Rapid environmental change and advances in data-driven analysis highlight the need not only to use computational tools, but also to foster understanding of the natural world and in…
Orthogonal Self-Attention
Leo Zhang, James Martens
Softmax Self-Attention (SSA) is a key component of Transformer architectures. However, when utilised within skipless architectures, which aim to improve representation learning, re…
Yuan3.0 Flash: An Open Multimodal Large Language Model for Enterprise Applications
YuanLab. ai, :, Shawn Wu +24
We introduce Yuan3.0 Flash, an open-source Mixture-of-Experts (MoE) MultiModal Large Language Model featuring 3.7B activated parameters and 40B total parameters, specifically desig…
Yuan3.0 Ultra: A Trillion-Parameter Enterprise-Oriented MoE LLM
YuanLab. ai, :, Shawn Wu +25
We introduce Yuan3.0 Ultra, an open-source Mixture-of-Experts (MoE) large language model featuring 68.8B activated parameters and 1010B total parameters, specially designed to enha…
Parameter Efficient Machine Unlearning on Hybrid Resistive Memory based Compute-in-Memory Accelerators
Ning Lin, Jichang Yang, Yangu He +17
Resistive memory compute-in-memory accelerators provide energy efficient analogue matrix vector multiplication for neural network inference, but frequent reprogramming of analogue…
SigmaDock: Untwisting Molecular Docking With Fragment-Based SE(3) Diffusion
Alvaro Prat, Leo Zhang, Charlotte M. Deane +2
Determining the binding pose of a ligand to a protein, known as molecular docking, is a fundamental task in drug discovery. Generative approaches promise faster, improved, and more…
SymDiff: Equivariant Diffusion via Stochastic Symmetrisation
Leo Zhang, Kianoosh Ashouritaklimi, Yee Whye Teh +1
We propose SymDiff, a method for constructing equivariant diffusion models using the framework of stochastic symmetrisation. SymDiff resembles a learned data augmentation that is d…
Metric Flow Matching for Smooth Interpolations on the Data Manifold
Kacper Kapuśniak, Peter Potaptchik, Teodora Reu +5
Matching objectives underpin the success of modern generative models and rely on constructing conditional paths that transform a source distribution into a target distribution. Des…
SambaLingo: Teaching Large Language Models New Languages
Zoltan Csaki, Bo Li, Jonathan Li +7
Despite the widespread availability of LLMs, there remains a substantial gap in their capabilities and availability across diverse languages. One approach to address these issues h…