100 citations · 202 across the 4 of their papers we have counts for
8 papers
Structured Multi-Hashing for Model Compression
Elad Eban, Yair Movshovitz-Attias, Hao Wu +4
Despite the success of deep neural networks (DNNs), state-of-the-art models are too large to deploy on low-resource devices or common server configurations in which multiple models…
Sampling the "Inverse Set" of a Neuron: An Approach to Understanding Neural Nets
Suryabhan Singh Hada, Miguel Á. Carreira-Perpiñán
With the recent success of deep neural networks in computer vision, it is important to understand the internal working of these networks. What does a given neuron represent? The co…
Style Transfer by Rigid Alignment in Neural Net Feature Space
Suryabhan Singh Hada, Miguel Á. Carreira-Perpiñán
Arbitrary style transfer is an important problem in computer vision that aims to transfer style patterns from an arbitrary style image to a given content image. However, current me…
Model compression as constrained optimization, with application to neural nets. Part II: quantization
Miguel Á. Carreira-Perpiñán, Yerlan Idelbayev
We consider the problem of deep neural net compression by quantization: given a large, reference net, we want to quantize its real-valued weights using a codebook with entries…
ParMAC: distributed optimisation of nested functions, with application to learning binary autoencoders
Miguel Á. Carreira-Perpiñán, Mehdi Alizadeh
Many powerful machine learning models are based on the composition of multiple processing layers, such as deep nets, which gives rise to nonconvex objective functions. A general, r…
A review of mean-shift algorithms for clustering
Miguel Á. Carreira-Perpiñán
A natural way to characterize the cluster structure of a dataset is by finding regions containing a high density of data. This can be done in a nonparametric way with a kernel dens…