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Found 7 papers, 5 papers with code
Symbolic Learning to Optimize: Towards Interpretability and Scalability
Recent studies on Learning to Optimize (L2O) suggest a promising path to automating and accelerating the optimization procedure for complicated tasks.
Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks
Our framework is implemented by a cascade of a convolutional and a graph neural network (CNN / GNN), addressing agent-level visual perception and feature learning, as well as swarm-level communication, local information aggregation and agent action inference, respectively.
Undistillable: Making A Nasty Teacher That CANNOT teach students
Knowledge Distillation (KD) is a widely used technique to transfer knowledge from pre-trained teacher models to (usually more lightweight) student models.
Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free
The trained model could be adjusted among different standard and robust accuracies "for free" at testing time.
Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference
Deep networks were recently suggested to face the odds between accuracy (on clean natural images) and robustness (on adversarially perturbed images) (Tsipras et al., 2019).
VGAI: End-to-End Learning of Vision-Based Decentralized Controllers for Robot Swarms
More specifically, we consider that each robot has access to a visual perception of the immediate surroundings, and communication capabilities to transmit and receive messages from other neighboring robots.
Dual Dynamic Inference: Enabling More Efficient, Adaptive and Controllable Deep Inference
State-of-the-art convolutional neural networks (CNNs) yield record-breaking predictive performance, yet at the cost of high-energy-consumption inference, that prohibits their widely deployments in resource-constrained Internet of Things (IoT) applications.
