Harkirat S. Behl

According to our database1, Harkirat S. Behl authored at least 17 papers between 2018 and 2023.

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Bibliography

2023
PEEKABOO: Interactive Video Generation via Masked-Diffusion.
CoRR, 2023

Textbooks Are All You Need.
CoRR, 2023

DAMEX: Dataset-aware Mixture-of-Experts for visual understanding of mixture-of-datasets.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Efficiently Robustify Pre-Trained Models.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022
Neural-Sim: Learning to Generate Training Data with NeRF.
Proceedings of the Computer Vision - ECCV 2022, 2022

2021
Automated and verified deep learning.
PhD thesis, 2021

Scaling the Convex Barrier with Sparse Dual Algorithms.
CoRR, 2021

Overcoming the Convex Barrier for Simplex Inputs.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Scaling the Convex Barrier with Active Sets.
Proceedings of the 9th International Conference on Learning Representations, 2021

Progressive Skeletonization: Trimming more fat from a network at initialization.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
STEER : Simple Temporal Regularization For Neural ODEs.
CoRR, 2020

STEER : Simple Temporal Regularization For Neural ODE.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Meta-Learning Deep Visual Words for Fast Video Object Segmentation.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

AutoSimulate: (Quickly) Learning Synthetic Data Generation.
Proceedings of the Computer Vision - ECCV 2020, 2020

2019
Alpha MAML: Adaptive Model-Agnostic Meta-Learning.
CoRR, 2019

2018
Meta Learning Deep Visual Words for Fast Video Object Segmentation.
CoRR, 2018

Incremental Tube Construction for Human Action Detection.
Proceedings of the British Machine Vision Conference 2018, 2018


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