Search Results for author:
Found 8 papers, 3 papers with code
Partially Blinded Unlearning: Class Unlearning for Deep Networks a Bayesian Perspective
In order to adhere to regulatory standards governing individual data privacy and safety, machine learning models must systematically eliminate information derived from specific subsets of a user's training data that can no longer be utilized.
Adapt then Unlearn: Exploiting Parameter Space Semantics for Unlearning in Generative Adversarial Networks
To the best of our knowledge, our approach stands as first method addressing unlearning in GANs.
A Unified Framework for Discovering Discrete Symmetries
At the core of the framework is a novel architecture composed of linear, matrix-valued and non-linear functions that expresses functions invariant to these subgroups in a principled manner.
GenSelfDiff-HIS: Generative Self-Supervision Using Diffusion for Histopathological Image Segmentation
The basic idea of SSL is to train a network to perform one or many pseudo or pretext tasks on unannotated data and use it subsequently as the basis for a variety of downstream tasks.
Constructing Bayesian Pseudo-Coresets using Contrastive Divergence
Bayesian Pseudo-Coreset (BPC) and Dataset Condensation are two parallel streams of work that construct a synthetic set such that, a model trained independently on this synthetic set, yields the same performance as training on the original training set.
Systematic Generalization in Neural Networks-based Multivariate Time Series Forecasting Models
Systematic generalization aims to evaluate reasoning about novel combinations from known components, an intrinsic property of human cognition.
RespVAD: Voice Activity Detection via Video-Extracted Respiration Patterns
The Respiration Pattern is first extracted from the video focusing on the abdominal-thoracic region of a speaker using an optical flow based method.
Effect of The Latent Structure on Clustering with GANs
We derive from first principles, the necessary and sufficient conditions needed to achieve faithful clustering in the GAN framework: (i) presence of a multimodal latent space with adjustable priors, (ii) existence of a latent space inversion mechanism and (iii) imposition of the desired cluster priors on the latent space.
