sequential
Memory Replay GANs: Learning to Generate New Categories without Forgetting
Chenshen Wu, Luis Herranz, Xialei Liu, yaxing wang, Joost van de Weijer, Bogdan Raducanu
In this paper we consider the case of generativemodels. In particular,we investigate generativeadversarialnetworks (GANs) in the task of learning new categories in a sequential fashion.
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Appendix 665 T able of Contents
In this section, we provide a detailed description of the dataset following'datasheet for datasets' [
A distributional simplicity bias in the learning dynamics of transformers
The remarkable capability of over-parameterised neural networks to generalise effectively has been explained by invoking a "simplicity bias": neural networks prevent overfitting by initially learning simple classifiers before progressing to
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Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models
Farnam Mansouri, Yuxin Chen, Ara Vartanian, Jerry Zhu, Adish Singla
Algorithmic machine teaching studies the interaction between a teacher and a learner where the teacher selects labeled examples aiming at teaching a target hypothesis.
Certified Machine Unlearning via Noisy Stochastic Gradient Descent
"The right to be forgotten" ensured by laws for user data privacy becomes increasingly important.
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Appendix
B.1 BaselineGHN:GHN-1 GHNs were designed for NAS, which typically make strong assumptions about the choice of operations and their possible dimensions tomakesearch and learning feasible.
