204 citations · 505 across the 17 of their papers we have counts for
19 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…
An ensemble diversity approach to supervised binary hashing
Miguel Á. Carreira-Perpiñán, Ramin Raziperchikolaei
Binary hashing is a well-known approach for fast approximate nearest-neighbor search in information retrieval. Much work has focused on affinity-based objective functions involving…
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…
Hashing with binary autoencoders
Miguel Á. Carreira-Perpiñán, Ramin Raziperchikolaei
An attractive approach for fast search in image databases is binary hashing, where each high-dimensional, real-valued image is mapped onto a low-dimensional, binary vector and the…
Optimizing affinity-based binary hashing using auxiliary coordinates
Ramin Raziperchikolaei, Miguel Á. Carreira-Perpiñán
In supervised binary hashing, one wants to learn a function that maps a high-dimensional feature vector to a vector of binary codes, for application to fast image retrieval. This t…
An ADMM algorithm for solving a proximal bound-constrained quadratic program
Miguel Á. Carreira-Perpiñán
We consider a proximal operator given by a quadratic function subject to bound constraints and give an optimization algorithm using the alternating direction method of multipliers…
The Laplacian K-modes algorithm for clustering
Weiran Wang, Miguel Á. Carreira-Perpiñán
In addition to finding meaningful clusters, centroid-based clustering algorithms such as K-means or mean-shift should ideally find centroids that are valid patterns in the input sp…
The role of dimensionality reduction in linear classification
Weiran Wang, Miguel Á. Carreira-Perpiñán
Dimensionality reduction (DR) is often used as a preprocessing step in classification, but usually one first fixes the DR mapping, possibly using label information, and then learns…
LASS: a simple assignment model with Laplacian smoothing
Miguel Á. Carreira-Perpiñán, Weiran Wang
We consider the problem of learning soft assignments of items to categories given two sources of information: an item-category similarity matrix, which encourages items to…
Projection onto the probability simplex: An efficient algorithm with a simple proof, and an application
Weiran Wang, Miguel Á. Carreira-Perpiñán
We provide an elementary proof of a simple, efficient algorithm for computing the Euclidean projection of a point onto the probability simplex. We also show an application in Lapla…
The K-modes algorithm for clustering
Miguel Á. Carreira-Perpiñán, Weiran Wang
Many clustering algorithms exist that estimate a cluster centroid, such as K-means, K-medoids or mean-shift, but no algorithm seems to exist that clusters data by returning exactly…
Distributed optimization of deeply nested systems
Miguel Á. Carreira-Perpiñán, Weiran Wang
In science and engineering, intelligent processing of complex signals such as images, sound or language is often performed by a parameterized hierarchy of nonlinear processing laye…
Partial-Hessian Strategies for Fast Learning of Nonlinear Embeddings
Max Vladymyrov, Miguel Carreira-Perpinan
Stochastic neighbor embedding (SNE) and related nonlinear manifold learning algorithms achieve high-quality low-dimensional representations of similarity data, but are notoriously…
Reconstruction of sequential data with density models
Miguel Á. Carreira-Perpiñán
We introduce the problem of reconstructing a sequence of multidimensional real vectors where some of the data are missing. This problem contains regression and mapping inversion as…
Generalised elastic nets
Miguel Á. Carreira-Perpiñán, Geoffrey J. Goodhill
The elastic net was introduced as a heuristic algorithm for combinatorial optimisation and has been applied, among other problems, to biological modelling. It has an energy functio…