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David Daniel Cox
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- affiliation: MIT-IBM Watson AI Lab, Cambridge, MA, USA
- affiliation (former): Harvard University, Center for Brain Science, Cambridge, MA, USA
- affiliation (PhD): Massachusetts Institute of Technology (MIT), Department of Brain and Cognitive Sciences, Cambridge, MA, USA
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2020 – today
- 2024
[i39]Shivchander Sudalairaj, Abhishek Bhandwaldar, Aldo Pareja, Kai Xu, David D. Cox, Akash Srivastava:
LAB: Large-Scale Alignment for ChatBots. CoRR abs/2403.01081 (2024)- 2023
- [c49]Cheng-I Jeff Lai, Freda Shi, Puyuan Peng, Yoon Kim, Kevin Gimpel, Shiyu Chang, Yung-Sung Chuang, Saurabhchand Bhati, David D. Cox, David Harwath, Yang Zhang, Karen Livescu, James R. Glass:
Audio-Visual Neural Syntax Acquisition. ASRU 2023: 1-8 - [c48]James Seale Smith, Paola Cascante-Bonilla, Assaf Arbelle, Donghyun Kim, Rameswar Panda, David D. Cox, Diyi Yang, Zsolt Kira, Rogério Feris, Leonid Karlinsky:
ConStruct-VL: Data-Free Continual Structured VL Concepts Learning. CVPR 2023: 14994-15004 - [c47]Joel Dapello, Kohitij Kar, Martin Schrimpf, Robert Baldwin Geary, Michael Ferguson, David Daniel Cox, James J. DiCarlo:
Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness. ICLR 2023 - [c46]Peihao Wang, Rameswar Panda, Lucas Torroba Hennigen, Philip Greengard, Leonid Karlinsky, Rogério Feris, David Daniel Cox, Zhangyang Wang, Yoon Kim:
Learning to Grow Pretrained Models for Efficient Transformer Training. ICLR 2023 - [c45]Zhiqing Sun, Yikang Shen, Qinhong Zhou, Hongxin Zhang, Zhenfang Chen, David D. Cox, Yiming Yang, Chuang Gan:
Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision. NeurIPS 2023 - [i38]Peihao Wang, Rameswar Panda, Lucas Torroba Hennigen, Philip Greengard, Leonid Karlinsky, Rogério Feris, David Daniel Cox, Zhangyang Wang, Yoon Kim:
Learning to Grow Pretrained Models for Efficient Transformer Training. CoRR abs/2303.00980 (2023) - [i37]Mingyu Ding, Yan Xu, Zhenfang Chen, David Daniel Cox, Ping Luo, Joshua B. Tenenbaum, Chuang Gan:
Embodied Concept Learner: Self-supervised Learning of Concepts and Mapping through Instruction Following. CoRR abs/2304.03767 (2023) - [i36]Zhiqing Sun, Yikang Shen, Qinhong Zhou, Hongxin Zhang, Zhenfang Chen, David D. Cox, Yiming Yang, Chuang Gan:
Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision. CoRR abs/2305.03047 (2023) - [i35]Junmo Kang, Hongyin Luo, Yada Zhu, James R. Glass, David D. Cox, Alan Ritter, Rogério Feris, Leonid Karlinsky:
Self-Specialization: Uncovering Latent Expertise within Large Language Models. CoRR abs/2310.00160 (2023) - [i34]Zhiqing Sun, Yikang Shen, Hongxin Zhang, Qinhong Zhou, Zhenfang Chen, David D. Cox, Yiming Yang, Chuang Gan:
SALMON: Self-Alignment with Principle-Following Reward Models. CoRR abs/2310.05910 (2023) - [i33]Cheng-I Jeff Lai, Freda Shi, Puyuan Peng, Yoon Kim, Kevin Gimpel, Shiyu Chang, Yung-Sung Chuang, Saurabhchand Bhati, David D. Cox, David Harwath, Yang Zhang, Karen Livescu, James R. Glass:
Audio-Visual Neural Syntax Acquisition. CoRR abs/2310.07654 (2023) - 2022
- [c44]Yang Zhang, Wang Zhou, Gaoyuan Zhang, David D. Cox, Shiyu Chang:
An Adversarial Framework for Generating Unseen Images by Activation Maximization. AAAI 2022: 3371-3379 - [c43]Mingyu Ding, Yan Xu, Zhenfang Chen, David Daniel Cox, Ping Luo, Joshua B. Tenenbaum, Chuang Gan:
Embodied Concept Learner: Self-supervised Learning of Concepts and Mapping through Instruction Following. CoRL 2022: 1743-1754 - [c42]Yi Li, Rameswar Panda, Yoon Kim, Chun-Fu Richard Chen, Rogério Feris, David D. Cox, Nuno Vasconcelos:
VALHALLA: Visual Hallucination for Machine Translation. CVPR 2022: 5206-5216 - [c41]Cheng-I Jeff Lai, Erica Cooper, Yang Zhang, Shiyu Chang, Kaizhi Qian, Yi-Lun Liao, Yung-Sung Chuang, Alexander H. Liu, Junichi Yamagishi, David D. Cox, James R. Glass:
On the Interplay between Sparsity, Naturalness, Intelligibility, and Prosody in Speech Synthesis. ICASSP 2022: 8447-8451 - [c40]Kaizhi Qian, Yang Zhang, Heting Gao, Junrui Ni, Cheng-I Lai, David D. Cox, Mark Hasegawa-Johnson, Shiyu Chang:
ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers. ICML 2022: 18003-18017 - [i32]Kaizhi Qian, Yang Zhang, Heting Gao, Junrui Ni, Cheng-I Lai, David D. Cox, Mark Hasegawa-Johnson, Shiyu Chang:
Improving Self-Supervised Speech Representations by Disentangling Speakers. CoRR abs/2204.09224 (2022) - [i31]Yi Li, Rameswar Panda, Yoon Kim, Chun-Fu Chen, Rogério Feris, David D. Cox, Nuno Vasconcelos:
VALHALLA: Visual Hallucination for Machine Translation. CoRR abs/2206.00100 (2022) - [i30]James Seale Smith, Paola Cascante-Bonilla, Assaf Arbelle, Donghyun Kim, Rameswar Panda, David D. Cox, Diyi Yang, Zsolt Kira, Rogério Feris, Leonid Karlinsky:
ConStruct-VL: Data-Free Continual Structured VL Concepts Learning. CoRR abs/2211.09790 (2022) - 2021
- [j13]German Abrevaya
, Guillaume Dumas, Aleksandr Y. Aravkin, Peng Zheng, Jean-Christophe Gagnon-Audet, James R. Kozloski, Pablo Polosecki, Guillaume Lajoie, David D. Cox, Silvina Ponce Dawson, Guillermo A. Cecchi, Irina Rish:
Learning Brain Dynamics With Coupled Low-Dimensional Nonlinear Oscillators and Deep Recurrent Networks. Neural Comput. 33(8): 2087-2127 (2021) - [c39]Yonggan Fu, Yongan Zhang, Yang Zhang, David D. Cox, Yingyan Lin:
Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators. ICML 2021: 3505-3517 - [c38]Kaizhi Qian, Yang Zhang, Shiyu Chang, Jinjun Xiong, Chuang Gan, David D. Cox, Mark Hasegawa-Johnson:
Global Prosody Style Transfer Without Text Transcriptions. ICML 2021: 8650-8660 - [c37]Yonggan Fu, Qixuan Yu, Yang Zhang, Shang Wu, Xu Ouyang, David D. Cox, Yingyan Lin:
Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks. NeurIPS 2021: 13059-13072 - [c36]Joel Dapello, Jenelle Feather, Hang Le, Tiago Marques, David D. Cox, Josh H. McDermott, James J. DiCarlo, SueYeon Chung:
Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception. NeurIPS 2021: 15595-15607 - [c35]Chuang Gan, Jeremy Schwartz, Seth Alter, Damian Mrowca, Martin Schrimpf, James Traer, Julian De Freitas, Jonas Kubilius, Abhishek Bhandwaldar, Nick Haber, Megumi Sano, Kuno Kim, Elias Wang, Michael Lingelbach, Aidan Curtis, Kevin T. Feigelis, Daniel Bear, Dan Gutfreund, David D. Cox, Antonio Torralba, James J. DiCarlo, Josh Tenenbaum, Josh H. McDermott, Dan Yamins:
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation. NeurIPS Datasets and Benchmarks 2021 - [c34]Cheng-I Jeff Lai, Yang Zhang, Alexander H. Liu, Shiyu Chang, Yi-Lun Liao, Yung-Sung Chuang, Kaizhi Qian, Sameer Khurana, David D. Cox, Jim Glass:
PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition. NeurIPS 2021: 21256-21272 - [c33]Rosaura G. VidalMata, Walter J. Scheirer, Anna Kukleva, David D. Cox, Hilde Kuehne:
Joint Visual-Temporal Embedding for Unsupervised Learning of Actions in Untrimmed Sequences. WACV 2021: 1237-1246 - [i29]Cheng-I Jeff Lai, Yang Zhang, Alexander H. Liu, Shiyu Chang, Yi-Lun Liao, Yung-Sung Chuang, Kaizhi Qian, Sameer Khurana, David D. Cox, James R. Glass:
PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition. CoRR abs/2106.05933 (2021) - [i28]Yonggan Fu, Yongan Zhang, Yang Zhang, David D. Cox, Yingyan Lin:
Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators. CoRR abs/2106.06575 (2021) - [i27]Kaizhi Qian, Yang Zhang, Shiyu Chang, Jinjun Xiong, Chuang Gan, David D. Cox, Mark Hasegawa-Johnson:
Global Rhythm Style Transfer Without Text Transcriptions. CoRR abs/2106.08519 (2021) - [i26]Cheng-I Jeff Lai, Erica Cooper, Yang Zhang, Shiyu Chang, Kaizhi Qian, Yi-Lun Liao, Yung-Sung Chuang, Alexander H. Liu, Junichi Yamagishi, David D. Cox, James R. Glass:
On the Interplay Between Sparsity, Naturalness, Intelligibility, and Prosody in Speech Synthesis. CoRR abs/2110.01147 (2021) - [i25]Yonggan Fu, Qixuan Yu, Yang Zhang, Shang Wu, Xu Ouyang, David D. Cox, Yingyan Lin:
Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks. CoRR abs/2110.14068 (2021) - [i24]Joel Dapello, Jenelle Feather, Hang Le, Tiago Marques, David D. Cox, Josh H. McDermott, James J. DiCarlo, SueYeon Chung:
Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception. CoRR abs/2111.06979 (2021) - 2020
- [j12]Lisa Amini, Ching-Hua Chen, David D. Cox, Aude Oliva, Antonio Torralba:
Experiences and Insights for Collaborative Industry-Academic Research in Artificial Intelligence. AI Mag. 41(1): 70-81 (2020) - [j11]William Lotter, Gabriel Kreiman, David D. Cox:
A neural network trained for prediction mimics diverse features of biological neurons and perception. Nat. Mach. Intell. 2(4): 210-219 (2020) - [c32]Kaizhi Qian, Yang Zhang, Shiyu Chang, Mark Hasegawa-Johnson, David D. Cox:
Unsupervised Speech Decomposition via Triple Information Bottleneck. ICML 2020: 7836-7846 - [c31]Joel Dapello, Tiago Marques, Martin Schrimpf, Franziska Geiger, David D. Cox, James J. DiCarlo:
Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations. NeurIPS 2020 - [i23]Kaizhi Qian, Yang Zhang, Shiyu Chang, David D. Cox, Mark Hasegawa-Johnson:
Unsupervised Speech Decomposition via Triple Information Bottleneck. CoRR abs/2004.11284 (2020) - [i22]Chuang Gan, Jeremy Schwartz, Seth Alter, Martin Schrimpf, James Traer, Julian De Freitas, Jonas Kubilius, Abhishek Bhandwaldar, Nick Haber, Megumi Sano, Kuno Kim, Elias Wang, Damian Mrowca, Michael Lingelbach, Aidan Curtis, Kevin T. Feigelis, Daniel M. Bear, Dan Gutfreund, David D. Cox, James J. DiCarlo, Josh H. McDermott, Joshua B. Tenenbaum, Daniel L. K. Yamins:
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation. CoRR abs/2007.04954 (2020) - [i21]Wang Zhou, Shiyu Chang, Norma E. Sosa, Hendrik F. Hamann, David D. Cox:
Lifelong Object Detection. CoRR abs/2009.01129 (2020) - [i20]Seungwook Han, Akash Srivastava, Cole L. Hurwitz, Prasanna Sattigeri, David D. Cox:
not-so-BigGAN: Generating High-Fidelity Images on a Small Compute Budget. CoRR abs/2009.04433 (2020) - [i19]Akash Srivastava, Yamini Bansal, Yukun Ding, Cole L. Hurwitz, Kai Xu, Bernhard Egger, Prasanna Sattigeri, Josh Tenenbaum, David D. Cox, Dan Gutfreund:
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling. CoRR abs/2010.13187 (2020) - [i18]Jianwei Yang, Jiayuan Mao, Jiajun Wu, Devi Parikh, David D. Cox, Joshua B. Tenenbaum, Chuang Gan:
Object-Centric Diagnosis of Visual Reasoning. CoRR abs/2012.11587 (2020)
2010 – 2019
- 2019
- [c30]Chuang Gan, Hang Zhao, Peihao Chen, David D. Cox, Antonio Torralba:
Self-Supervised Moving Vehicle Tracking With Stereo Sound. ICCV 2019: 7052-7061 - [c29]Quanfu Fan, Chun-Fu (Richard) Chen, Hilde Kuehne, Marco Pistoia, David D. Cox:
More Is Less: Learning Efficient Video Representations by Big-Little Network and Depthwise Temporal Aggregation. NeurIPS 2019: 2261-2270 - [c28]Xiangyi Chen, Sijia Liu, Kaidi Xu, Xingguo Li, Xue Lin, Mingyi Hong, David D. Cox:
ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization. NeurIPS 2019: 7202-7213 - [c27]Philippe Tillet, Hsiang-Tsung Kung, David D. Cox:
Triton: an intermediate language and compiler for tiled neural network computations. MAPL@PLDI 2019: 10-19 - [i17]Xiangyi Chen, Sijia Liu, Kaidi Xu, Xingguo Li, Xue Lin, Mingyi Hong, David D. Cox:
ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization. CoRR abs/1910.06513 (2019) - [i16]Chuang Gan, Hang Zhao, Peihao Chen, David D. Cox, Antonio Torralba:
Self-supervised Moving Vehicle Tracking with Stereo Sound. CoRR abs/1910.11760 (2019) - [i15]Akash Srivastava, Jessie C. Rosenberg, Dan Gutfreund, David D. Cox:
SimVAE: Simulator-Assisted Training forInterpretable Generative Models. CoRR abs/1911.08051 (2019) - [i14]Quanfu Fan, Chun-Fu Chen, Hilde Kuehne, Marco Pistoia, David D. Cox:
More Is Less: Learning Efficient Video Representations by Big-Little Network and Depthwise Temporal Aggregation. CoRR abs/1912.00869 (2019) - 2018
- [c26]Isha Puri, David D. Cox:
A System for Accurate Tracking and Video Recordings of Rodent Eye Movements using Convolutional Neural Networks for Biomedical Image Segmentation. EMBC 2018: 3590-3593 - [c25]Andrew M. Saxe, Yamini Bansal, Joel Dapello, Madhu Advani, Artemy Kolchinsky, Brendan D. Tracey, David D. Cox:
On the Information Bottleneck Theory of Deep Learning. ICLR (Poster) 2018 - [c24]Eric Wu, Kevin Wu, David D. Cox, William Lotter:
Conditional Infilling GANs for Data Augmentation in Mammogram Classification. RAMBO+BIA+TIA@MICCAI 2018: 98-106 - [i13]Philippe Tillet, David D. Cox:
Input-Aware Auto-Tuning of Compute-Bound HPC Kernels. CoRR abs/1802.05371 (2018) - [i12]William Lotter, Gabriel Kreiman, David D. Cox:
A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception. CoRR abs/1805.10734 (2018) - [i11]Yamini Bansal, Madhu Advani, David D. Cox, Andrew M. Saxe:
Minnorm training: an algorithm for training over-parameterized deep neural networks. CoRR abs/1806.00730 (2018) - [i10]Eric Wu, Kevin Wu, David D. Cox, William Lotter:
Conditional Infilling GANs for Data Augmentation in Mammogram Classification. CoRR abs/1807.08093 (2018) - 2017
- [c23]Philippe Tillet, H. T. Kung, David D. Cox:
Infomax-ICA using Hessian-free optimization. ICASSP 2017: 2537-2541 - [c22]William Lotter, Gabriel Kreiman, David D. Cox:
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. ICLR (Poster) 2017 - [c21]William Lotter, Greg Sorensen, David D. Cox:
A Multi-scale CNN and Curriculum Learning Strategy for Mammogram Classification. DLMIA/ML-CDS@MICCAI 2017: 169-177 - [c20]Philippe Tillet, David D. Cox:
Input-aware auto-tuning of compute-bound HPC kernels. SC 2017: 43 - [i9]Ruth Fong, Walter J. Scheirer, David D. Cox:
Using Human Brain Activity to Guide Machine Learning. CoRR abs/1703.05463 (2017) - [i8]Hanlin Tang, Bill Lotter, Martin Schrimpf, Ana Paredes, Josue Ortega Caro, Walter Hardesty, David D. Cox, Gabriel Kreiman:
Recurrent computations for visual pattern completion. CoRR abs/1706.02240 (2017) - [i7]William Lotter, Greg Sorensen, David D. Cox:
A Multi-Scale CNN and Curriculum Learning Strategy for Mammogram Classification. CoRR abs/1707.06978 (2017) - 2016
- [j10]João Paulo Papa, Walter J. Scheirer, David Daniel Cox:
Fine-tuning Deep Belief Networks using Harmony Search. Appl. Soft Comput. 46: 875-885 (2016) - [j9]Stephanie M. Lowry, Niko Sünderhauf, Paul Newman, John J. Leonard, David D. Cox, Peter I. Corke, Michael J. Milford:
Visual Place Recognition: A Survey. IEEE Trans. Robotics 32(1): 1-19 (2016) - [c19]Chuan-Yung Tsai, Andrew M. Saxe, David D. Cox:
Tensor Switching Networks. NIPS 2016: 2038-2046 - [i6]William Lotter, Gabriel Kreiman, David D. Cox:
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. CoRR abs/1605.08104 (2016) - [i5]Chuan-Yung Tsai, Andrew M. Saxe, David D. Cox:
Tensor Switching Networks. CoRR abs/1610.10087 (2016) - 2015
- [j8]João Paulo Papa, Gustavo H. Rosa, Aparecido Nilceu Marana, Walter J. Scheirer, David D. Cox:
Model selection for Discriminative Restricted Boltzmann Machines through meta-heuristic techniques. J. Comput. Sci. 9: 14-18 (2015) - [c18]Silas Evandro Nachif Fernandes, Walter J. Scheirer, David D. Cox, João Paulo Papa:
Improving Optimum-Path Forest Classification Using Confidence Measures. CIARP 2015: 619-625 - [c17]Gustavo H. Rosa, João Paulo Papa, Aparecido Nilceu Marana, Walter J. Scheirer, David D. Cox:
Fine-Tuning Convolutional Neural Networks Using Harmony Search. CIARP 2015: 683-690 - [c16]João P. Papa, Gustavo Henrique Rosa, Kelton A. P. Costa, Nilceu A. Marana, Walter J. Scheirer, David Daniel Cox:
On the Model Selection of Bernoulli Restricted Boltzmann Machines Through Harmony Search. GECCO (Companion) 2015: 1449-1450 - [i4]Chuan-Yung Tsai, David D. Cox:
Measuring and Understanding Sensory Representations within Deep Networks Using a Numerical Optimization Framework. CoRR abs/1502.04972 (2015) - [i3]William Lotter, Gabriel Kreiman, David D. Cox:
Unsupervised Learning of Visual Structure using Predictive Generative Networks. CoRR abs/1511.06380 (2015) - 2014
- [j7]Michael Milford, Eleonora Vig, Walter J. Scheirer, David D. Cox:
Vision-based Simultaneous Localization and Mapping in Changing Outdoor Environments. J. Field Robotics 31(5): 780-802 (2014) - [j6]Walter J. Scheirer, Samuel E. Anthony, Ken Nakayama, David D. Cox:
Perceptual Annotation: Measuring Human Vision to Improve Computer Vision. IEEE Trans. Pattern Anal. Mach. Intell. 36(8): 1679-1686 (2014) - [j5]Giovani Chiachia, Alexandre X. Falcão, Nicolas Pinto, Anderson Rocha, David D. Cox:
Learning Person-Specific Representations From Faces in the Wild. IEEE Trans. Inf. Forensics Secur. 9(12): 2089-2099 (2014) - [c15]Eleonora Vig, Michael Dorr, David D. Cox:
Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images. CVPR 2014: 2798-2805 - [c14]Michael Milford, Walter J. Scheirer, Eleonora Vig, Arren Glover, Oliver Baumann, Jason B. Mattingley, David D. Cox:
Condition-invariant, top-down visual place recognition. ICRA 2014: 5571-5577 - 2013
- [c13]Manuel Günther, Artur Costa-Pazo, Changxing Ding, Elhocine Boutellaa, Giovani Chiachia, Honglei Zhang, Marcus A. Angeloni, Vitomir Struc, Elie Khoury, Esteban Vázquez-Fernández, Dacheng Tao, Messaoud Bengherabi, David D. Cox, Serkan Kiranyaz, Tiago de Freitas Pereira, Jerneja Zganec-Gros, Enrique Argones-Rúa, Nicolas Pinto, Moncef Gabbouj, Flávio Olmos Simões, Simon Dobrisek, Daniel González-Jiménez, Anderson Rocha, M. U. Neto, Nikola Pavesic, Alexandre Xavier Falcão, Ricardo P. V. Violato, Sébastien Marcel:
The 2013 face recognition evaluation in mobile environment. ICB 2013: 1-7 - [c12]James Bergstra, Daniel Yamins, David D. Cox:
Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures. ICML (1) 2013: 115-123 - [c11]James Bergstra, Dan Yamins, David D. Cox:
Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms. SciPy 2013: 13-19 - [c10]James Bergstra, Nicolas Pinto, David D. Cox:
SkData: Data Sets and Algorithm Evaluation Protocols in Python. SciPy 2013: 20-26 - [i2]James Bergstra, David D. Cox:
Hyperparameter Optimization and Boosting for Classifying Facial Expressions: How good can a "Null" Model be? CoRR abs/1306.3476 (2013) - 2012
- [j4]Nicolas Pinto, David D. Cox:
High-throughput-derived biologically-inspired features for unconstrained face recognition. Image Vis. Comput. 30(3): 159-168 (2012) - [c9]Giovani Chiachia, Nicolas Pinto, William Robson Schwartz, Anderson Rocha, Alexandre X. Falcão, David D. Cox:
Person-Specific Subspace Analysis for Unconstrained Familiar Face Identification. BMVC 2012: 1-12 - [c8]Eleonora Vig, Michael Dorr, David D. Cox:
Space-Variant Descriptor Sampling for Action Recognition Based on Saliency and Eye Movements. ECCV (7) 2012: 84-97 - [c7]Eleonora Vig, Michael Dorr, David D. Cox:
Saliency-based selection of sparse descriptors for action recognition. ICIP 2012: 1405-1408 - [i1]James Bergstra, Dan Yamins, David D. Cox:
Making a Science of Model Search. CoRR abs/1209.5111 (2012) - 2011
- [c6]Nicolas Pinto, Zak Stone, Todd E. Zickler, David D. Cox:
Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook. CVPR Workshops 2011: 35-42 - [c5]David D. Cox, Nicolas Pinto:
Beyond simple features: A large-scale feature search approach to unconstrained face recognition. FG 2011: 8-15 - [c4]Nicolas Pinto, Youssef Barhomi, David D. Cox, James J. DiCarlo:
Comparing state-of-the-art visual features on invariant object recognition tasks. WACV 2011: 463-470 - 2010
- [c3]Nicolas Pinto, David D. Cox:
An Evaluation of the Invariance Properties of a Biologically-Inspired System for Unconstrained Face Recognition. BIONETICS 2010: 505-518 - [c2]Vinay Sriram, David D. Cox, Kuen Hung Tsoi, Wayne Luk:
Towards an embedded biologically-inspired machine vision processor. FPT 2010: 273-278
2000 – 2009
- 2009
- [j3]Nicolas Pinto, David Doukhan, James J. DiCarlo, David D. Cox:
A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation. PLoS Comput. Biol. 5(11) (2009) - [c1]Nicolas Pinto, James J. DiCarlo, David D. Cox:
How far can you get with a modern face recognition test set using only simple features?. CVPR 2009: 2591-2598 - 2008
- [j2]Nicolas Pinto, David D. Cox, James J. DiCarlo:
Why is Real-World Visual Object Recognition Hard? PLoS Comput. Biol. 4(1) (2008) - 2003
- [j1]David D. Cox, Robert L. Savoy:
Functional magnetic resonance imaging (fMRI) "brain reading": detecting and classifying distributed patterns of fMRI activity in human visual cortex. NeuroImage 19(2): 261-270 (2003)
Coauthor Index
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