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Found 6 papers, 1 papers with code
Center-Based Relaxed Learning Against Membership Inference Attacks
Membership inference attacks (MIAs) are currently considered one of the main privacy attack strategies, and their defense mechanisms have also been extensively explored.
The Over-Certainty Phenomenon in Modern UDA Algorithms
When neural networks are confronted with unfamiliar data that deviate from their training set, this signifies a domain shift.
ReffAKD: Resource-efficient Autoencoder-based Knowledge Distillation
In our work, we propose an efficient method for generating these soft labels, thereby eliminating the need for a large teacher model.
Cooperative Learning for Cost-Adaptive Inference
The Teammate nets derive sub-networks and transfer knowledge to them, and to each other, while the Leader net guides Teammate nets to ensure accuracy.
Pruning has a disparate impact on model accuracy
Network pruning is a widely-used compression technique that is able to significantly scale down overparameterized models with minimal loss of accuracy.
Risk-Conditioned Distributional Soft Actor-Critic for Risk-Sensitive Navigation
Modern navigation algorithms based on deep reinforcement learning (RL) show promising efficiency and robustness.
