Title: QUANTIZATION AWARE FACTORIZATION FOR DEEP NEURAL NETWORK COMPRESSION. Abstract: Tensor approximation of convolutional and fully-connected weights is an effective way to reduce  parameters and FLOP in neural networks. Due to memory and power consumption limitations of mobile or embedded devices, the quantization step is usually necessary when pre-trained models are deployed. A conventional post-training quantization approach applied to networks with decomposed weights yields a drop in accuracy. Therefore, our goal is to develop an algorithm that finds tensor approximation directly with quantized factors and thus benefit from both compression techniques while keeping the prediction quality of the model. Namely, we propose to use Alternating Direction Method of Multipliers (ADMM) for approximating a float tensor by a tensor of Canonical Polyadic format (CP), whose factors are close to their quantized versions. This leads to lower approximation error after quantization and smaller quality drop in model predictions while maintaining the same compression rate. 