3 citations · 10 across the 4 of their papers we have counts for
7 papers
Accelerating Deep Learning Model Inference on Arm CPUs with Ultra-Low Bit Quantization and Runtime
Saad Ashfaq, MohammadHossein AskariHemmat, Sudhakar Sah +3
Deep Learning has been one of the most disruptive technological advancements in recent times. The high performance of deep learning models comes at the expense of high computationa…
QReg: On Regularization Effects of Quantization
MohammadHossein AskariHemmat, Reyhane Askari Hemmat, Alex Hoffman +6
In this paper we study the effects of quantization in DNN training. We hypothesize that weight quantization is a form of regularization and the amount of regularization is correlat…
On Causal Inference for Data-free Structured Pruning
Martin Ferianc, Anush Sankaran, Olivier Mastropietro +2
Neural networks (NNs) are making a large impact both on research and industry. Nevertheless, as NNs' accuracy increases, it is followed by an expansion in their size, required numb…
Deeplite Neutrino: An End-to-End Framework for Constrained Deep Learning Model Optimization
Anush Sankaran, Olivier Mastropietro, Ehsan Saboori +4
Designing deep learning-based solutions is becoming a race for training deeper models with a greater number of layers. While a large-size deeper model could provide competitive acc…
Hierarchical Adversarially Learned Inference
Mohamed Ishmael Belghazi, Sai Rajeswar, Olivier Mastropietro +3
We propose a novel hierarchical generative model with a simple Markovian structure and a corresponding inference model. Both the generative and inference model are trained using th…
Adversarially Learned Inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole +4
We introduce the adversarially learned inference (ALI) model, which jointly learns a generation network and an inference network using an adversarial process. The generation networ…