Search Results for author:
Found 18 papers, 8 papers with code
Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Sampling
Monte Carlo methods, Variational Inference, and their combinations play a pivotal role in sampling from intractable probability distributions.
Improving Antibody Design with Force-Guided Sampling in Diffusion Models
Our model, DiffForce, employs forces to guide the diffusion sampling process, effectively blending the two distributions.
DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised $h$-transform
In this work, we unify conditional training and sampling using the mathematically well-understood Doob's h-transform.
A framework for conditional diffusion modelling with applications in motif scaffolding for protein design
Many protein design applications, such as binder or enzyme design, require scaffolding a structural motif with high precision.
Transport meets Variational Inference: Controlled Monte Carlo Diffusions
Connecting optimal transport and variational inference, we present a principled and systematic framework for sampling and generative modelling centred around divergences on path space.
To smooth a cloud or to pin it down: Guarantees and Insights on Score Matching in Denoising Diffusion Models
Denoising diffusion models are a class of generative models which have recently achieved state-of-the-art results across many domains.
Dimensionality Reduction as Probabilistic Inference
Dimensionality reduction (DR) algorithms compress high-dimensional data into a lower dimensional representation while preserving important features of the data.
Denoising Diffusion Samplers
Denoising Diffusion Samplers (DDS) are obtained by approximating the corresponding time-reversal.
Kernelized Concept Erasure
One prominent approach for the identification of concepts in neural representations is searching for a linear subspace whose erasure prevents the prediction of the concept from the representations.
Shooting Schrödinger’s Cat
We present a variational inference scheme to learn a model that solves the Schrödinger Bridge Problem (SBP).
Bayesian Learning via Neural Schrödinger-Föllmer Flows
In this work we explore a new framework for approximate Bayesian inference in large datasets based on stochastic control (i. e. Schr\"odinger bridges).
Efficient privacy-preserving inference for convolutional neural networks
Others have proposed the use of model pruning and efficient data representations to reduce the number of HE operations required.
Solving Schrödinger Bridges via Maximum Likelihood
The Schr\"odinger bridge problem (SBP) finds the most likely stochastic evolution between two probability distributions given a prior stochastic evolution.
Exploring the Linear Subspace Hypothesis in Gender Bias Mitigation
Their method takes pre-trained word representations as input and attempts to isolate a linear subspace that captures most of the gender bias in the representations.
Multilingual Factor Analysis
In this work we approach the task of learning multilingual word representations in an offline manner by fitting a generative latent variable model to a multilingual dictionary.
Model Comparison for Semantic Grouping
We introduce a probabilistic framework for quantifying the semantic similarity between two groups of embeddings.
Towards Automatic Wild Animal Monitoring: Identification of Animal Species in Camera-trap Images using Very Deep Convolutional Neural Networks
Non intrusive monitoring of animals in the wild is possible using camera trapping framework, which uses cameras triggered by sensors to take a burst of images of animals in their habitat.
HMM and DTW for evaluation of therapeutical gestures using kinect
Automatic recognition of the quality of movement in human beings is a challenging task, given the difficulty both in defining the constraints that make a movement correct, and the difficulty in using noisy data to determine if these constraints were satisfied.
