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Found 8 papers, 2 papers with code
Decoupling Representation and Knowledge for Few-Shot Intent Classification and Slot Filling
Therefore, current works first train a model on source domains with sufficiently labeled data, and then transfer the model to target domains where only rarely labeled data is available.
Decoupled Rationalization with Asymmetric Learning Rates: A Flexible Lipschitz Restraint
However, such a cooperative game may incur the degeneration problem where the predictor overfits to the uninformative pieces generated by a not yet well-trained generator and in turn, leads the generator to converge to a sub-optimal model that tends to select senseless pieces.
Improving Adversarial Robustness with Self-Paced Hard-Class Pair Reweighting
Deep Neural Networks are vulnerable to adversarial attacks.
Adversarial Fine-tune with Dynamically Regulated Adversary
Adversarial training is an effective method to boost model robustness to malicious, adversarial attacks.
Dropout Prediction Uncertainty Estimation Using Neuron Activation Strength
Dropout has been commonly used to quantify prediction uncertainty, i. e, the variations of model predictions on a given input example.
Know Your Surroundings: Panoramic Multi-Object Tracking by Multimodality Collaboration
In this paper, we focus on the multi-object tracking (MOT) problem of automatic driving and robot navigation.
A Stochastic-Computing based Deep Learning Framework using Adiabatic Quantum-Flux-Parametron SuperconductingTechnology
Further, the application of SC has been investigated in DNNs in prior work, and the suitability has been illustrated as SC is more compatible with approximate computations.
An Area and Energy Efficient Design of Domain-Wall Memory-Based Deep Convolutional Neural Networks using Stochastic Computing
However, in these works, the memory design optimization is neglected for weight storage, which will inevitably result in large hardware cost.
