Welcome to my site

This is my personal site which plays host to my publications, videos, and some information about myself.

I work on Machine Learning and Computer Vision and recently defended my Computer Science PhD at New York University in those fields. I have since launched a company called Clarifai to bring large scale deep learning into every day use. Through Clarifai, we won the Imagenet 2013 Classification Challenge (results) and have since been making huge strides in improving accuracy, speed, and memory usage of these models.

News & Events

Recent Publications

Visualizing and Understanding Convolutional Networks
M.D. Zeiler, R. Fergus
ECCV 2014 (Honourable Mention for Best Paper Award), Arxiv 1311.2901 (Nov 28, 2013)

Hierarchical Convolutional Deep Learning in Computer Vision
M.D. Zeiler. Advisor: R. Fergus
Unpublished PhD Thesis (Nov 8, 2013)

Regularization of Neural Networks using DropConnect
L. Wan, M.D. Zeiler, S. Zhang, Y. LeCun, R. Fergus
ICML 2013 (June 16, 2013)

On Recified Linear Units for Speech Processing
M.D. Zeiler, M. Ranzato, R. Monga, M. Mao, K. Yang, Q.V. Le, P. Nguyen, A. Senior, V. Vanhoucke, J. Dean, and G.E. Hinton
ICASSP 2013 (May 26, 2013)

Stochastic Pooling for Regularization of Deep Convolutional Neural Networks
Matthew D. Zeiler and Rob Fergus
ICLR 2013 (May 2, 2013)
Supplemental Material:  Images  Videos

ADADELTA: An Adaptive Learning Rate Method
Matthew D. Zeiler
arXiv:1212.5701 (Dec 27, 2012)

Differentiable Pooling for Hierarchical Feature Learning
Matthew D. Zeiler and Rob Fergus
arXiv:1207.0151v1 (July 3, 2012)

Recent Software Added

Adaptive Deconvolutional Network Toolbox

This toolbox includes code that implements an Adaptive Deconvolutional Network as described in the paper Adaptive Deconvolutional Networks for Mid and High Level Feature Learning. It may also be used to implement a Deconvolutional Network as described in the paper Deconvolutional Networks though this is not longer the recommended method. This has a function to train a Deconvolutional Network, to visualize the learned filters, and to recsontruct a new image from a trained model. Also, there are files that can be used to make descriptors that can be used with Svetlana Lazebnik's Spatial Pyramid Matching code with a few minor modifications. The Deconvolutional Network Toolbox also works with (and now includes) the IPP Convolutions Toolbox which drastically improves performance (just ensure the IPP Convolutions Toolbox files are in your MATLAB path in order to use it with this toolbox and that they are compiled with your IPP libraries.).
Download (.zip)  Documentation (html)
Suggested Software
Related Publications

Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines

This toolbox provides MATLAB implementations of ioTRBMs and FIOTRBM models for use in facial retargeting expeirments.
Download (.zip)  Documentation (html)
Related Publications