3 citations · 5 across the 4 of their papers we have counts for
4 papers
Balancing Act: Constraining Disparate Impact in Sparse Models
Meraj Hashemizadeh, Juan Ramirez, Rohan Sukumaran +3
Model pruning is a popular approach to enable the deployment of large deep learning models on edge devices with restricted computational or storage capacities. Although sparse mode…
Omega: Optimistic EMA Gradients
Juan Ramirez, Rohan Sukumaran, Quentin Bertrand +1
Stochastic min-max optimization has gained interest in the machine learning community with the advancements in GANs and adversarial training. Although game optimization is fairly w…
Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints
Jose Gallego-Posada, Juan Ramirez, Akram Erraqabi +2
The performance of trained neural networks is robust to harsh levels of pruning. Coupled with the ever-growing size of deep learning models, this observation has motivated extensiv…
Lonie: Compressing COINs with L-constraints
Juan Ramirez, Jose Gallego-Posada
Advances in Implicit Neural Representations (INR) have motivated research on domain-agnostic compression techniques. These methods train a neural network to approximate an object,…