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CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks
Traditional compression methods such as pruning, distillation, and low-rank approximation focus on reducing the effective number of neurons in the network, while quantization focuses on reducing the numerical precision of individual weights to reduce the model size while keeping the number of neurons fixed.
Power Control with QoS Guarantees: A Differentiable Projection-based Unsupervised Learning Framework
Utilizing a differentiable projection function, two novel deep learning (DL) solutions are pursued.
Deep Unsupervised Learning for Generalized Assignment Problems: A Case-Study of User-Association in Wireless Networks
More specifically, we propose a new approach that facilitates to train a deep neural network (DNN) using a customized loss function.
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