1 citations · 1 across the 6 of their papers we have counts for
7 papers
Stochasticity in Tokenisation Improves Robustness
Sophie Steger, Rui Li, Sofiane Ennadir +4
The widespread adoption of large language models (LLMs) has increased concerns about their robustness. Vulnerabilities in perturbations of tokenisation of the input indicate that m…
Sparsely Supervised Diffusion
Wenshuai Zhao, Zhiyuan Li, Yi Zhao +5
Diffusion models have shown remarkable success across a wide range of generative tasks. However, they often suffer from spatially inconsistent generation, arguably due to the inher…
Approximate Bayesian Inference via Bitstring Representations
Aleksanteri Sladek, Martin Trapp, Arno Solin
The machine learning community has recently put effort into quantized or low-precision arithmetics to scale large models. This paper proposes performing probabilistic inference in…
Post-hoc Probabilistic Vision-Language Models
Anton Baumann, Rui Li, Marcus Klasson +5
Vision-language models (VLMs), such as CLIP and SigLIP, have found remarkable success in classification, retrieval, and generative tasks. For this, VLMs deterministically map image…
Streamlining Prediction in Bayesian Deep Learning
Rui Li, Marcus Klasson, Arno Solin +1
The rising interest in Bayesian deep learning (BDL) has led to a plethora of methods for estimating the posterior distribution. However, efficient computation of inferences, such a…
Flatness Improves Backbone Generalisation in Few-shot Classification
Rui Li, Martin Trapp, Marcus Klasson +1
Deployment of deep neural networks in real-world settings typically requires adaptation to new tasks with few examples. Few-shot classification (FSC) provides a solution to this pr…
Subtractive Mixture Models via Squaring: Representation and Learning
Lorenzo Loconte, Aleksanteri M. Sladek, Stefan Mengel +4
Mixture models are traditionally represented and learned by adding several distributions as components. Allowing mixtures to subtract probability mass or density can drastically re…