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Found 5 papers, 1 papers with code
Is Solving Graph Neural Tangent Kernel Equivalent to Training Graph Neural Network?
A rising trend in theoretical deep learning is to understand why deep learning works through Neural Tangent Kernel (NTK) [jgh18], a kernel method that is equivalent to using gradient descent to train a multi-layer infinitely-wide neural network.
Query Complexity of Active Learning for Function Family With Nearly Orthogonal Basis
Many machine learning algorithms require large numbers of labeled data to deliver state-of-the-art results.
HLIC: Harmonizing Optimization Metrics in Learned Image Compression by Reinforcement Learning
Learned image compression is making good progress in recent years.
Sample Complexity of Deep Active Learning
In this paper, we present the first deep active learning algorithm which has a provable sample complexity.
Checkerboard Context Model for Efficient Learned Image Compression
To the best of our knowledge, this is the first exploration on parallelization-friendly spatial context model for learned image compression.
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