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Found 12 papers, 2 papers with code
Randomized Polar Codes for Anytime Distributed Machine Learning
We present a novel distributed computing framework that is robust to slow compute nodes, and is capable of both approximate and exact computation of linear operations.
Moccasin: Efficient Tensor Rematerialization for Neural Networks
The deployment and training of neural networks on edge computing devices pose many challenges.
Distributed Sketching for Randomized Optimization: Exact Characterization, Concentration and Lower Bounds
Furthermore, we develop unbiased parameter averaging methods for randomized second order optimization for regularized problems that employ sketching of the Hessian.
Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions
In this work, we analyze the training of Wasserstein GANs with two-layer neural network discriminators through the lens of convex duality, and for a variety of generators expose the conditions under which Wasserstein GANs can be solved exactly with convex optimization approaches, or can be represented as convex-concave games.
Training Quantized Neural Networks to Global Optimality via Semidefinite Programming
Neural networks (NNs) have been extremely successful across many tasks in machine learning.
Neural Spectrahedra and Semidefinite Lifts: Global Convex Optimization of Polynomial Activation Neural Networks in Fully Polynomial-Time
In this paper, we develop exact convex optimization formulations for two-layer neural networks with second degree polynomial activations based on semidefinite programming.
Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization
In distributed second order optimization, a standard strategy is to average many local estimates, each of which is based on a small sketch or batch of the data.
Distributed Sketching Methods for Privacy Preserving Regression
In this work, we study distributed sketching methods for large scale regression problems.
Distributed Averaging Methods for Randomized Second Order Optimization
We consider distributed optimization problems where forming the Hessian is computationally challenging and communication is a significant bottleneck.
Distributed Black-Box Optimization via Error Correcting Codes
We introduce a novel distributed derivative-free optimization framework that is resilient to stragglers.
Straggler Resilient Serverless Computing Based on Polar Codes
We propose a serverless computing mechanism for distributed computation based on polar codes.
Convex Relaxations of Convolutional Neural Nets
We propose convex relaxations for convolutional neural nets with one hidden layer where the output weights are fixed.
