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Found 8 papers, 6 papers with code
Balancing Act: Constraining Disparate Impact in Sparse Models
Model pruning is a popular approach to enable the deployment of large deep learning models on edge devices with restricted computational or storage capacities.
A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale
It constructs a block-diagonal preconditioner where each block consists of a coarse Kronecker product approximation to full-matrix AdaGrad for each parameter of the neural network.
Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints
The performance of trained neural networks is robust to harsh levels of pruning.
L$_0$onie: Compressing COINs with L$_0$-constraints
Advances in Implicit Neural Representations (INR) have motivated research on domain-agnostic compression techniques.
Equivariant Mesh Attention Networks
Equivariance to symmetries has proven to be a powerful inductive bias in deep learning research.
Flexible Learning of Sparse Neural Networks via Constrained $L_0$ Regularization
We propose to approach the problem of learning $L_0$-sparse networks using a constrained formulation of the optimization problem.
Simplicial Regularization
Inspired by the fuzzy topological representation of a dataset employed in UMAP (McInnes et al., 2018), we propose a regularization principle for supervised learning based on the preservation of the simplicial complex structure of the data.
GANGs: Generative Adversarial Network Games
We introduce Generative Adversarial Network Games (GANGs), which explicitly model a finite zero-sum game between a generator ($G$) and classifier ($C$) that use mixed strategies.
