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Found 7 papers, 0 papers with code
Convergence of Deep Neural Networks with General Activation Functions and Pooling
In this current work, we study the convergence of deep neural networks as the depth tends to infinity for two other important activation functions: the leaky ReLU and the sigmoid function.
Convergence Analysis of Deep Residual Networks
By studying the two series, we establish a sufficient condition for pointwise convergence of ResNets.
Virtual Inertia Control of the Virtual Synchronous Generator: A Review
With the increasing impact of low inertia due to the high penetration of distributed generation, virtual synchronous generator (VSG) technology has been proposed to improve the stability of the inverter-interfaced distributed generator by providing "virtual inertia".
Exploring Multi-dimensional Data via Subset Embedding
The core of the approach is a subset embedding network (SEN) that represents a group of subsets as uniformly-formatted embeddings.
Information-theoretic interpretation of tuning curves for multiple motion directions
We have developed an efficient information-maximization method for computing the optimal shapes of tuning curves of sensory neurons by optimizing the parameters of the underlying feedforward network model.
An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax
Starting from the initial solution, an efficient algorithm based on gradient descent of the final objective function is proposed to learn representations from the input datasets, and the method works for complete, overcomplete, and undercomplete bases.
Information-Theoretic Bounds and Approximations in Neural Population Coding
While Shannon's mutual information has widespread applications in many disciplines, for practical applications it is often difficult to calculate its value accurately for high-dimensional variables because of the curse of dimensionality.
