Aditya Golatkar

According to our database1, Aditya Golatkar authored at least 19 papers between 2018 and 2024.

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Bibliography

2024
B'MOJO: Hybrid State Space Realizations of Foundation Models with Eidetic and Fading Memory.
CoRR, 2024

Diffusion Soup: Model Merging for Text-to-Image Diffusion Models.
CoRR, 2024

Tangent Transformers for Composition, Privacy and Removal.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Role of Over-Parameterization in Generalization of 3-layer ReLU Networks.
Proceedings of the Second Tiny Papers Track at ICLR 2024, 2024

CPR: Retrieval Augmented Generation for Copyright Protection.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023
Unlearning and Privacy in Deep Neural Networks
PhD thesis, 2023

Training Data Protection with Compositional Diffusion Models.
CoRR, 2023

SAFE: Machine Unlearning With Shard Graphs.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022
Integral Continual Learning Along the Tangent Vector Field of Tasks.
CoRR, 2022

On Leave-One-Out Conditional Mutual Information For Generalization.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Mixed Differential Privacy in Computer Vision.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021
Scene Uncertainty and the Wellington Posterior of Deterministic Image Classifiers.
CoRR, 2021

Mixed-Privacy Forgetting in Deep Networks.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

LQF: Linear Quadratic Fine-Tuning.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020
Forgetting Outside the Box: Scrubbing Deep Networks of Information Accessible from Input-Output Observations.
Proceedings of the Computer Vision - ECCV 2020, 2020

Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019
Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018
Sparse Kernel PCA for Outlier Detection.
Proceedings of the 17th IEEE International Conference on Machine Learning and Applications, 2018

Classification of Breast Cancer Histology Using Deep Learning.
Proceedings of the Image Analysis and Recognition - 15th International Conference, 2018


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