Malik Boudiaf

Orcid: 0000-0003-2047-6447

According to our database1, Malik Boudiaf authored at least 22 papers between 2020 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Links

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Bibliography

2023
Adversarial Robustness Via Fisher-Rao Regularization.
IEEE Trans. Pattern Anal. Mach. Intell., March, 2023

Transductive Learning for Textual Few-Shot Classification in API-based Embedding Models.
CoRR, 2023

Bag of Tricks for Fully Test-Time Adaptation.
CoRR, 2023

In Search for a Generalizable Method for Source Free Domain Adaptation.
Proceedings of the International Conference on Machine Learning, 2023

Transductive Learning for Textual Few-Shot Classification in API-based Embedding Models.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

A Strong Baseline for Generalized Few-Shot Semantic Segmentation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Open-Set Likelihood Maximization for Few-Shot Learning.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Simplex Clustering via sBeta with Applications to Online Adjustment of Black-Box Predictions.
CoRR, 2022

Model-Agnostic Few-Shot Open-Set Recognition.
CoRR, 2022

FHIST: A Benchmark for Few-shot Classification of Histological Images.
CoRR, 2022

KNIFE: Kernelized-Neural Differential Entropy Estimation.
CoRR, 2022

Towards Practical Few-shot Query Sets: Transductive Minimum Description Length Inference.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

A Differential Entropy Estimator for Training Neural Networks.
Proceedings of the International Conference on Machine Learning, 2022

Parameter-free Online Test-time Adaptation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021
Mutual-Information Based Few-Shot Classification.
CoRR, 2021

Transductive Few-Shot Learning: Clustering is All You Need?
CoRR, 2021

Realistic evaluation of transductive few-shot learning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020
Transductive Information Maximization For Few-Shot Learning.
CoRR, 2020

Metric learning: cross-entropy vs. pairwise losses.
CoRR, 2020

Information Maximization for Few-Shot Learning.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A Unifying Mutual Information View of Metric Learning: Cross-Entropy vs. Pairwise Losses.
Proceedings of the Computer Vision - ECCV 2020, 2020


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