no code implementations • 13 Mar 2024 • Sangamesh Kodge, Deepak Ravikumar, Gobinda Saha, Kaushik Roy
We introduce Verifix, a novel Singular Value Decomposition (SVD) based algorithm that leverages a small, verified dataset to correct the model weights using a single update.
1 code implementation • 1 Dec 2023 • Sangamesh Kodge, Gobinda Saha, Kaushik Roy
Machine unlearning is a prominent and challenging field, driven by regulatory demands for user data deletion and heightened privacy awareness.
no code implementations • 9 Apr 2023 • Deepak Ravikumar, Gobinda Saha, Sai Aparna Aketi, Kaushik Roy
The goal of IDKD is to homogenize the data distribution across the nodes.
1 code implementation • 27 Mar 2023 • Sakshi Choudhary, Sai Aparna Aketi, Gobinda Saha, Kaushik Roy
Decentralized learning allows serverless training with spatially distributed data.
1 code implementation • 2 Feb 2023 • Gobinda Saha, Kaushik Roy
In neural networks, continual learning results in gradient interference among sequential tasks, leading to catastrophic forgetting of old tasks while learning new ones.
no code implementations • 6 Oct 2022 • Efstathia Soufleri, Gobinda Saha, Kaushik Roy
We evaluate our method on image classification dataset (CIFAR10) and show that our synthetic data can be used for training networks from scratch, producing reasonable classification performance.
1 code implementation • 10 Sep 2021 • Gobinda Saha, Kaushik Roy
One way to enable such learning is to store past experiences in the form of input examples in episodic memory and replay them when learning new tasks.
1 code implementation • ICLR 2021 • Gobinda Saha, Isha Garg, Kaushik Roy
The ability to learn continually without forgetting the past tasks is a desired attribute for artificial learning systems.
1 code implementation • 23 Jan 2020 • Gobinda Saha, Isha Garg, Aayush Ankit, Kaushik Roy
A minimal number of extra dimensions required to explain the current task are added to the Core space and the remaining Residual is freed up for learning the next task.