98 citations · 216 across the 13 of their papers we have counts for
14 papers
A Fourier Space Perspective on Diffusion Models
Fabian Falck, Teodora Pandeva, Kiarash Zahirnia +5
Diffusion models are state-of-the-art generative models on data modalities such as images, audio, proteins and materials. These modalities share the property of exponentially decay…
Towards Efficient Optimizer Design for LLM via Structured Fisher Approximation with a Low-Rank Extension
Wenbo Gong, Meyer Scetbon, Chao Ma +1
Designing efficient optimizers for large language models (LLMs) with low-memory requirements and fast convergence is an important and challenging problem. This paper makes a step t…
Gradient Multi-Normalization for Stateless and Scalable LLM Training
Meyer Scetbon, Chao Ma, Wenbo Gong +1
Training large language models (LLMs) typically relies on adaptive optimizers like Adam (Kingma & Ba, 2015) which store additional state information to accelerate convergence but i…
SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training
Chao Ma, Wenbo Gong, Meyer Scetbon +1
Adaptive optimizers such as Adam (Kingma & Ba, 2015) have been central to the success of large language models. However, they often require to maintain optimizer states throughout…
AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires
Melanie F. Pradier, Niranjani Prasad, Paidamoyo Chapfuwa +8
Recent advances in immunomics have shown that T-cell receptor (TCR) signatures can accurately predict active or recent infection by leveraging the high specificity of TCR binding t…
Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations
Paidamoyo Chapfuwa, Sherri Rose, Lawrence Carin +2
End-to-end learning of dynamical systems with black-box models, such as neural ordinary differential equations (ODEs), provides a flexible framework for learning dynamics from data…
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems
Geoffrey Roeder, Paul K. Grant, Andrew Phillips +2
We introduce a flexible, scalable Bayesian inference framework for nonlinear dynamical systems characterised by distinct and hierarchical variability at the individual, group, and…
Deterministic Variational Inference for Robust Bayesian Neural Networks
Anqi Wu, Sebastian Nowozin, Edward Meeds +3
Bayesian neural networks (BNNs) hold great promise as a flexible and principled solution to deal with uncertainty when learning from finite data. Among approaches to realize probab…
Soft Weight-Sharing for Neural Network Compression
Karen Ullrich, Edward Meeds, Max Welling
The success of deep learning in numerous application domains created the de- sire to run and train them on mobile devices. This however, conflicts with their computationally, memor…
Automatic Variational ABC
Alexander Moreno, Tameem Adel, Edward Meeds +2
Approximate Bayesian Computation (ABC) is a framework for performing likelihood-free posterior inference for simulation models. Stochastic Variational inference (SVI) is an appeali…
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference
Edward Meeds, Max Welling
We describe an embarrassingly parallel, anytime Monte Carlo method for likelihood-free models. The algorithm starts with the view that the stochasticity of the pseudo-samples gener…
Hamiltonian ABC
Edward Meeds, Robert Leenders, Max Welling
Approximate Bayesian computation (ABC) is a powerful and elegant framework for performing inference in simulation-based models. However, due to the difficulty in scaling likelihood…
POPE: Post Optimization Posterior Evaluation of Likelihood Free Models
Edward Meeds, Michael Chiang, Mary Lee +3
In many domains, scientists build complex simulators of natural phenomena that encode their hypotheses about the underlying processes. These simulators can be deterministic or stoc…
GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation
Edward Meeds, Max Welling
Scientists often express their understanding of the world through a computationally demanding simulation program. Analyzing the posterior distribution of the parameters given obser…