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Found 5 papers, 1 papers with code
Investigating Power laws in Deep Representation Learning
Access to task relevant labels at scale is often scarce or expensive, motivating the need to learn from unlabelled datasets with self-supervised learning (SSL).
Towards Scaling Difference Target Propagation by Learning Backprop Targets
As such, it is important to explore learning algorithms that come with strong theoretical guarantees and can match the performance of backpropagation (BP) on complex tasks.
Your head is there to move you around: Goal-driven models of the primate dorsal pathway
To test this hypothesis, we trained a 3D ResNet to predict an agent's self-motion parameters from visual stimuli in a simulated environment.
The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning
We show that when we train a deep neural network architecture with two parallel pathways using a self-supervised predictive loss function, we can outperform other models in fitting mouse visual cortex.
From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence
Machine learning has long since become a keystone technology, accelerating science and applications in a broad range of domains.
