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Found 6 papers, 2 papers with code
Monitoring Shortcut Learning using Mutual Information
The failure of deep neural networks to generalize to out-of-distribution data is a well-known problem and raises concerns about the deployment of trained networks in safety-critical domains such as healthcare, finance and autonomous vehicles.
DeepRNG: Towards Deep Reinforcement Learning-Assisted Generative Testing of Software
Although machine learning (ML) has been successful in automating various software engineering needs, software testing still remains a highly challenging topic.
Domain-Agnostic Clustering with Self-Distillation
Recent advancements in self-supervised learning have reduced the gap between supervised and unsupervised representation learning.
FusedProp: Towards Efficient Training of Generative Adversarial Networks
Generative adversarial networks (GANs) are capable of generating strikingly realistic samples but state-of-the-art GANs can be extremely computationally expensive to train.
Tensor Switching Networks
We present a novel neural network algorithm, the Tensor Switching (TS) network, which generalizes the Rectified Linear Unit (ReLU) nonlinearity to tensor-valued hidden units.
Measuring and Understanding Sensory Representations within Deep Networks Using a Numerical Optimization Framework
A central challenge in sensory neuroscience is describing how the activity of populations of neurons can represent useful features of the external environment.
