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Found 9 papers, 2 papers with code
Lookbehind-SAM: k steps back, 1 step forward
Sharpness-aware minimization (SAM) methods have gained increasing popularity by formulating the problem of minimizing both loss value and loss sharpness as a minimax objective.
Promoting Exploration in Memory-Augmented Adam using Critical Momenta
Adaptive gradient-based optimizers, particularly Adam, have left their mark in training large-scale deep learning models.
Feature diversity in self-supervised learning
With numerous scaling factors defined in previous works, it would be interesting to investigate how these factors may affect overall generalization performance in the context of self-supervised learning with CNN models.
An Introduction to Lifelong Supervised Learning
Following these different classes of learning algorithms, we discuss the commonly used evaluation benchmarks and metrics for lifelong learning (Chapter 6) and wrap up with a discussion of future challenges and important research directions in Chapter 7.
A Causal Approach for Unfair Edge Prioritization and Discrimination Removal
We prove this result for the non-trivial non-parametric model setting when the cumulative unfairness cannot be expressed in terms of edge unfairness.
TAG: Task-based Accumulated Gradients for Lifelong learning
We also show that our method performs better than several state-of-the-art methods in lifelong learning on complex datasets with a large number of tasks.
Fast constraint satisfaction problem and learning-based algorithm for solving Minesweeper
Results from these experiments show that the proposed method of MDP based classification model and deep Q-learning overall is the best methods in terms of accuracy for games with given mine densities.
A Causal Linear Model to Quantify Edge Flow and Edge Unfairness for UnfairEdge Prioritization and Discrimination Removal
Unlike previous works that only make cautionary claims of discrimination and de-biases data after its generation, this paper attempts to prioritize unfair sources before mitigating their unfairness in the real-world.
Contextual Care Protocol using Neural Networks and Decision Trees
A contextual care protocol is used by a medical practitioner for patient healthcare, given the context or situation that the specified patient is in.
