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Found 4 papers, 0 papers with code
Efficiently Distilling LLMs for Edge Applications
Supernet training of LLMs is of great interest in industrial applications as it confers the ability to produce a palette of smaller models at constant cost, regardless of the number of models (of different size / latency) produced.
Transfer-Once-For-All: AI Model Optimization for Edge
Hence, we propose Transfer-Once-For-All (TOFA) for supernet-style training on small data sets with constant computational training cost over any number of edge deployment scenarios.
Robustness and Personalization in Federated Learning: A Unified Approach via Regularization
We present a class of methods for robust, personalized federated learning, called Fed+, that unifies many federated learning algorithms.
Efficient algorithms for learning kernels from multiple similarity matrices with general convex loss functions
We present several provably convergent iterative algorithms, where each iteration requires either an SVM or a Multiple Kernel Learning (MKL) solver for m > 1 case.
