David D. Cox

发表

How far can you get with a modern face recognition test set using only simple features?

Nicolas Pinto, James J. DiCarlo, David D. Cox, 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

SimVAE: Simulator-Assisted Training forInterpretable Generative Models pdf

David D. Cox, Jessie Rosenberg, Akash Srivastava, 2019, ArXiv.

A Self-Calibrating, Camera-Based Eye Tracker for the Recording of Rodent Eye Movements

Brett J. Graham, David D. Cox, Davide Zoccolan, 2010, Front. Neurosci..

Learning Person-Specific Representations From Faces in the Wild

Anderson Rocha, Giovani Chiachia, Alexandre X. Falcão, 2014, IEEE Transactions on Information Forensics and Security.

SkData: Data Sets and Algorithm Evaluation Protocols in Python

Nicolas Pinto, David D. Cox, James Bergstra, 2013 .

Saliency-based selection of sparse descriptors for action recognition

Michael Dorr, Eleonora Vig, David D. Cox, 2012, 2012 19th IEEE International Conference on Image Processing.

Vision-based Simultaneous Localization and Mapping in Changing Outdoor Environments

Michael Milford, Eleonora Vig, Walter J. Scheirer, 2014, J. Field Robotics.

On the Model Selection of Bernoulli Restricted Boltzmann Machines Through Harmony Search

João Paulo Papa, Kelton A. P. Costa, Gustavo H. Rosa, 2015, GECCO.

Why is Real-World Visual Object Recognition Hard?

Nicolas Pinto, James J. DiCarlo, David D. Cox, 2008, PLoS Comput. Biol..

A systematic examination of ground-based and space-based approaches to optical detection and tracking of artificial satellites.

John T. McGraw, David D. Cox, Mark R. Ackermann, 2015 .

Affordable Options for Ground-Based, Large-Aperture Optical Space Surveillance Systems

John T. McGraw, W. T. Vestrand, David D. Cox, 2013 .

Beyond simple features: A large-scale feature search approach to unconstrained face recognition

Nicolas Pinto, David D. Cox, N. Pinto, 2011, Face and Gesture 2011.

Editorial: What can simple brains teach us about how vision works

David D. Cox, Davide Zoccolan, Andrea Benucci, 2015, Front. Neural Circuits.

SkData: data sets and algorithm evaluation protocols in Python

Nicolas Pinto, David D. Cox, James Bergstra, 2015 .

Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations

Martin Schrimpf, David D. Cox, James J. DiCarlo, 2020, bioRxiv.

Fine-tuning Deep Belief Networks using Harmony Search

João Paulo Papa, Walter J. Scheirer, David D. Cox, 2016, Appl. Soft Comput..

Making a Science of Model Search pdf

David D. Cox, James Bergstra, Dan Yamins, 2012, ArXiv.

Improving Optimum-Path Forest Classification Using Confidence Measures

João Paulo Papa, Silas Evandro Nachif Fernandes, Walter J. Scheirer, 2015, CIARP.

not-so-BigGAN: Generating High-Fidelity Images on a Small Compute Budget pdf

David D. Cox, Prasanna Sattigeri, Seungwook Han, 2020, ArXiv.

A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation

David D. Cox, James J. DiCarlo, David Doukhan, 2009, PLoS Comput. Biol..

Space-Variant Descriptor Sampling for Action Recognition Based on Saliency and Eye Movements

Michael Dorr, Eleonora Vig, David D. Cox, 2012, ECCV.

Payload state of health monitoring design for next generation satellite constellations

David D. Cox, P. L. Geib, A. M. Tomasi, 2002, Proceedings, IEEE Aerospace Conference.

Hyperparameter Optimization and Boosting for Classifying Facial Expressions: How good can a "Null" Model be? pdf

David D. Cox, James Bergstra, 2013, ICML 2013.

Minnorm training: an algorithm for training over-parameterized deep neural networks pdf

David D. Cox, Andrew M. Saxe, Yamini Bansal, 2018, ArXiv.

Model selection for Discriminative Restricted Boltzmann Machines through meta-heuristic techniques

João Paulo Papa, Gustavo H. Rosa, Aparecido Nilceu Marana, 2015, J. Comput. Sci..

UDOT RESEARCH & DEVELOPMENT REPORT ABSTRACT

Mitsuru Saito, David D. Cox, Stan Burns, 2004 .

Tensor Switching Networks

David D. Cox, Andrew M. Saxe, Chuan-Yung Tsai, 2016, NIPS.

CZ-GEM: A FRAMEWORK FOR DISENTANGLED REPRESENTATION LEARNING

Bernhard Egger, David D. Cox, Prasanna Sattigeri, 2019 .

Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images

Michael Dorr, Eleonora Vig, David D. Cox, 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures

David D. Cox, James Bergstra, Dan Yamins, 2013, ICML.

Rats strategically manage learning during perceptual decision making

David D. Cox, Andrew M. Saxe, Javier Masís, 2020, bioRxiv.

Object-Centric Diagnosis of Visual Reasoning pdf

Chuang Gan, Jiajun Wu, Joshua B. Tenenbaum, 2020, ArXiv.

Condition-invariant, top-down visual place recognition

Michael Milford, Jason B. Mattingley, Oliver Baumann, 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

An Evaluation of the Invariance Properties of a Biologically-Inspired System for Unconstrained Face Recognition

Nicolas Pinto, David D. Cox, 2010, BIONETICS.

Alternatives for Ground-Based Large-Aperture Optical Space Surveillance Systems.

John T. McGraw, Mark Weeks, David D. Cox, 2013 .

Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling pdf

Kai Xu, Bernhard Egger, David D. Cox, 2020, ArXiv.

Comparing state-of-the-art visual features on invariant object recognition tasks

Nicolas Pinto, James J. DiCarlo, David D. Cox, 2011, 2011 IEEE Workshop on Applications of Computer Vision (WACV).

On the information bottleneck theory of deep learning

David D. Cox, Artemy Kolchinsky, Andrew M. Saxe, 2018, ICLR.

Person-Specific Subspace Analysis for Unconstrained Familiar Face Identification

Anderson Rocha, William Robson Schwartz, Giovani Chiachia, 2012, BMVC.

Perceptual Annotation: Measuring Human Vision to Improve Computer Vision

Walter J. Scheirer, Ken Nakayama, David D. Cox, 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Using human brain activity to guide machine learning

Walter J. Scheirer, David D. Cox, Ruth Fong, 2017, Scientific Reports.

High-throughput-derived biologically-inspired features for unconstrained face recognition

Nicolas Pinto, David D. Cox, 2012, Image Vis. Comput..

Measuring and Understanding Sensory Representations within Deep Networks Using a Numerical Optimization Framework pdf

David D. Cox, Chuan-Yung Tsai, D. Cox, 2015, ArXiv.

The 2013 face recognition evaluation in mobile environment

Moncef Gabbouj, Sébastien Marcel, Ricardo P. V. Violato, 2013, 2013 International Conference on Biometrics (ICB).

Fine-Tuning Convolutional Neural Networks Using Harmony Search

João Paulo Papa, Gustavo H. Rosa, Aparecido Nilceu Marana, 2015, CIARP.

A System for Accurate Tracking and Video Recordings of Rodent Eye Movements using Convolutional Neural Networks for Biomedical Image Segmentation

David D. Cox, Isha Puri, Isha Puri, 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

not-so-BigGAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution

David D. Cox, Prasanna Sattigeri, Seungwook Han, 2020 .

Visual Place Recognition: A Survey

Peter I. Corke, Michael Milford, John J. Leonard, 2016, IEEE Transactions on Robotics.

Unsupervised Learning of Visual Structure using Predictive Generative Networks pdf

Gabriel Kreiman, David D. Cox, William Lotter, 2015, ArXiv.

Evaluation of Recent Traffic and Safety Initiatives, Volume II: Developing a Procedure for Evaluating the Need for Raised Medians

Mitsuru Saito, David D. Cox, Thomas G. Jin, 2005 .

Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception pdf

James J. DiCarlo, David D. Cox, Josh H. McDermott, 2021, NeurIPS.

Neural Networks and Neuroscience-Inspired Computer Vision

Thomas L. Dean, David D. Cox, D. Cox, 2014, Current Biology.

Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex

David D. Cox, David D Cox, Robert L Savoy, 2003, NeuroImage.

Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms

David D. Cox, David Cox, Daniel Yamins, 2013, SciPy.

Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook

David Cox, Nicolas Pinto, David D. Cox, 2011, CVPR 2011 WORKSHOPS.

Recurrent computations for visual pattern completion

David Cox, Gabriel Kreiman, Hanlin Tang, 2017, Proceedings of the National Academy of Sciences.

Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning pdf

Gabriel Kreiman, David D. Cox, William Lotter, 2016, ICLR.

Hyperopt: a Python library for model selection and hyperparameter optimization

Chris Eliasmith, David D. Cox, David Cox, 2015 .

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

Berkman Sahiner, Jaime S. Cardoso, Thomas Yu, 2020, JAMA network open.

Opinion TRENDS in Cognitive Sciences Vol.11 No.8 Untangling invariant object recognition

David D. Cox, J. DiCarlo, D. Cox, 2022 .

Multiple Object Response Normalization in Monkey Inferotemporal Cortex

James J DiCarlo, David D. Cox, Davide Zoccolan, 2005, The Journal of Neuroscience.

A Multi-scale CNN and Curriculum Learning Strategy for Mammogram Classification

David Cox, David D. Cox, William Lotter, 2017, DLMIA/ML-CDS@MICCAI.

Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness

David D. Cox, J. DiCarlo, D. Cox, 2022, bioRxiv.

Conditional Infilling GANs for Data Augmentation in Mammogram Classification pdf

David Cox, Kevin Wu, Eric Wu, 2018, RAMBO+BIA+TIA@MICCAI.

High-resolution three-dimensional microelectrode brain mapping using stereo microfocal X-ray imaging.

David D. Cox, Benjamin B. Andken, J. DiCarlo, 2008, Journal of neurophysiology.

Spatial Reconstruction of Trajectories of an Array of Recording Microelectrodes

David D. Cox, Benjamin B. Andken, D. Cox, 2013 .

A neural network trained for prediction mimics diverse features of biological neurons and perception

Gabriel Kreiman, David D. Cox, David Cox, 2020, Nat. Mach. Intell..

Towards an embedded biologically-inspired machine vision processor

Wayne Luk, David Cox, Kuen Hung Tsoi, 2010, 2010 International Conference on Field-Programmable Technology.

Triton: an intermediate language and compiler for tiled neural network computations

David Cox, David D. Cox, Philippe Tillet, 2019, MAPL@PLDI.

A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception pdf

David Cox, Gabriel Kreiman, David D. Cox, 2018, ArXiv.

Do we understand high-level vision?

David D. Cox, David Daniel Cox, D. Cox, 2014, Current Opinion in Neurobiology.

A rodent model for the study of invariant visual object recognition

James J DiCarlo, David D. Cox, Davide Zoccolan, 2009, Proceedings of the National Academy of Sciences.

What response properties do individual neurons need to underlie position and clutter "invariant" object recognition?

David D. Cox, J. DiCarlo, D. Cox, 2009, Journal of neurophysiology.

Object Recognition? Need to Underlie Position and Clutter ''Invariant'' What Response Properties Do Individual Neurons

David D. Cox, N. P. Bichot, Ethan M. Meyers, 2011 .

Machine learning for predictive auto-tuning with boosted regression trees

David D. Cox, J. Bergstra, N. Pinto, 2012, 2012 Innovative Parallel Computing (InPar).

Input-Aware Auto-Tuning of Compute-Bound HPC Kernels

David Cox, David D. Cox, Philippe Tillet, 2017, SC17: International Conference for High Performance Computing, Networking, Storage and Analysis.

Does Learned Shape Selectivity in Inferior Temporal Cortex Automatically Generalize Across Retinal Position?

David D. Cox, J. DiCarlo, D. Cox, 2008, The Journal of Neuroscience.