Search Results for author:
Found 6 papers, 1 papers with code
Convergence of stochastic gradient descent under a local Lojasiewicz condition for deep neural networks
We study the convergence of stochastic gradient descent (SGD) for non-convex objective functions.
Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss
We consider a deep matrix factorization model of covariance matrices trained with the Bures-Wasserstein distance.
Combining resampling and reweighting for faithful stochastic optimization
When the loss function is a sum of multiple terms, a popular method is the stochastic gradient descent.
Hierarchical RNNs-Based Transformers MADDPG for Mixed Cooperative-Competitive Environments
This paper proposed a hierarchical transformers MADDPG based on RNN which we call it Hierarchical RNNs-Based Transformers MADDPG(HRTMADDPG).
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Why resampling outperforms reweighting for correcting sampling bias with stochastic gradients
We consider two commonly-used techniques, resampling and reweighting, that rebalance the proportions of the subgroups to maintain the desired objective function.
Stochastic modified equations for the asynchronous stochastic gradient descent
The resulting SME of Langevin type extracts more information about the ASGD dynamics and elucidates the relationship between different types of stochastic gradient algorithms.
