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Found 5 papers, 5 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.
Omega: Optimistic EMA Gradients
Stochastic min-max optimization has gained interest in the machine learning community with the advancements in GANs and adversarial training.
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.
Covariance Estimation from Compressive Data Partitions using a Projected Gradient-based Algorithm
The algorithm estimates the covariance matrix of hyperspectral images from synthetic and real compressive samples.
