David D. Cox

Publication Activity (10 Years)

Years Active: 2003-2024
Publications (10 Years): 70

Top Topics

Predictive Coding

Confidence Measures

Convolutional Neural Networks

Top Venues

Publications

Runqian Wang, Soumya Ghosh, David D. Cox, Diego Antognini, Aude Oliva, Rogério Feris, Leonid Karlinsky
Trans-LoRA: towards data-free Transferable Parameter Efficient Finetuning. CoRR (2024)
Matt Stallone, Vaibhav Saxena, Leonid Karlinsky, Bridget McGinn, Tim Bula, Mayank Mishra, Adriana Meza Soria, Gaoyuan Zhang, Aditya Prasad, Yikang Shen, Saptha Surendran, Shanmukha C. Guttula, Hima Patel, Parameswaran Selvam, Xuan-Hong Dang, Yan Koyfman, Atin Sood, Rogério Feris, Nirmit Desai, David D. Cox, Ruchir Puri, Rameswar Panda
Scaling Granite Code Models to 128K Context. CoRR (2024)
Mayank Mishra, Matt Stallone, Gaoyuan Zhang, Yikang Shen, Aditya Prasad, Adriana Meza Soria, Michele Merler, Parameswaran Selvam, Saptha Surendran, Shivdeep Singh, Manish Sethi, Xuan-Hong Dang, Pengyuan Li, Kun-Lung Wu, Syed Zawad, Andrew Coleman, Matthew White, Mark Lewis, Raju Pavuluri, Yan Koyfman, Boris Lublinsky, Maximilien de Bayser, Ibrahim Abdelaziz, Kinjal Basu, Mayank Agarwal, Yi Zhou, Chris Johnson, Aanchal Goyal, Hima Patel, S. Yousaf Shah, Petros Zerfos, Heiko Ludwig, Asim Munawar, Maxwell Crouse, Pavan Kapanipathi, Shweta Salaria, Bob Calio, Sophia Wen, Seetharami Seelam, Brian Belgodere, Carlos A. Fonseca, Amith Singhee, Nirmit Desai, David D. Cox, Ruchir Puri, Rameswar Panda
Granite Code Models: A Family of Open Foundation Models for Code Intelligence. CoRR (2024)
Shivchander Sudalairaj, Abhishek Bhandwaldar, Aldo Pareja, Kai Xu, David D. Cox, Akash Srivastava
LAB: Large-Scale Alignment for ChatBots. CoRR (2024)
Junmo Kang, Leonid Karlinsky, Hongyin Luo, Zhen Wang, Jacob A. Hansen, Jim Glass, David D. Cox, Rameswar Panda, Rogério Feris, Alan Ritter
Self-MoE: Towards Compositional Large Language Models with Self-Specialized Experts. CoRR (2024)
Junmo Kang, Hongyin Luo, Yada Zhu, Jacob A. Hansen, James R. Glass, David D. Cox, Alan Ritter, Rogério Feris, Leonid Karlinsky
Self-Specialization: Uncovering Latent Expertise within Large Language Models. ACL (Findings) (2024)
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)
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 (2023)
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)
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 (2023)
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 (2023)
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)
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 (2023)
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)
Yi Li, Rameswar Panda, Yoon Kim, Chun-Fu Chen, Rogério Feris, David D. Cox, Nuno Vasconcelos
VALHALLA: Visual Hallucination for Machine Translation. CoRR (2022)
Yang Zhang, Wang Zhou, Gaoyuan Zhang, David D. Cox, Shiyu Chang
An Adversarial Framework for Generating Unseen Images by Activation Maximization. AAAI (2022)
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)
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)
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 (2022)
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 (2022)
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)
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) (2021)
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)
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 (2021)
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)
Kaizhi Qian, Yang Zhang, Shiyu Chang, Jinjun Xiong, Chuang Gan, David D. Cox, Mark Hasegawa-Johnson
Global Rhythm Style Transfer Without Text Transcriptions. CoRR (2021)
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)
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)
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 (2021)
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) (2020)
Kaizhi Qian, Yang Zhang, Shiyu Chang, David D. Cox, Mark Hasegawa-Johnson
Unsupervised Speech Decomposition via Triple Information Bottleneck. CoRR (2020)
Kaizhi Qian, Yang Zhang, Shiyu Chang, Mark Hasegawa-Johnson, David D. Cox
Unsupervised Speech Decomposition via Triple Information Bottleneck. ICML (2020)
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 (2020)
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) (2020)
Wang Zhou, Shiyu Chang, Norma E. Sosa, Hendrik F. Hamann, David D. Cox
Lifelong Object Detection. CoRR (2020)
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)
Jianwei Yang, Jiayuan Mao, Jiajun Wu, Devi Parikh, David D. Cox, Joshua B. Tenenbaum, Chuang Gan
Object-Centric Diagnosis of Visual Reasoning. CoRR (2020)
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 (2020)
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 (2020)
Philippe Tillet, Hsiang-Tsung Kung, David D. Cox
Triton: an intermediate language and compiler for tiled neural network computations. MAPL@PLDI (2019)
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)
Akash Srivastava, Jessie C. Rosenberg, Dan Gutfreund, David D. Cox
SimVAE: Simulator-Assisted Training forInterpretable Generative Models. CoRR (2019)
Chuang Gan, Hang Zhao, Peihao Chen, David D. Cox, Antonio Torralba
Self-supervised Moving Vehicle Tracking with Stereo Sound. CoRR (2019)
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)
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 (2019)
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 (2019)
Chuang Gan, Hang Zhao, Peihao Chen, David D. Cox, Antonio Torralba
Self-Supervised Moving Vehicle Tracking With Stereo Sound. ICCV (2019)
Eric Wu, Kevin Wu, David D. Cox, William Lotter
Conditional Infilling GANs for Data Augmentation in Mammogram Classification. RAMBO+BIA+TIA@MICCAI (2018)
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)
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 (2018)
Yamini Bansal, Madhu Advani, David D. Cox, Andrew M. Saxe
Minnorm training: an algorithm for training over-parameterized deep neural networks. CoRR (2018)
Philippe Tillet, David D. Cox
Input-Aware Auto-Tuning of Compute-Bound HPC Kernels. CoRR (2018)
Eric Wu, Kevin Wu, David D. Cox, William Lotter
Conditional Infilling GANs for Data Augmentation in Mammogram Classification. CoRR (2018)
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)
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 (2017)
William Lotter, Greg Sorensen, David D. Cox
A Multi-Scale CNN and Curriculum Learning Strategy for Mammogram Classification. CoRR (2017)
Ruth Fong, Walter J. Scheirer, David D. Cox
Using Human Brain Activity to Guide Machine Learning. CoRR (2017)
Philippe Tillet, David D. Cox
Input-aware auto-tuning of compute-bound HPC kernels. SC (2017)
Philippe Tillet, H. T. Kung, David D. Cox
Infomax-ICA using Hessian-free optimization. ICASSP (2017)
William Lotter, Gabriel Kreiman, David D. Cox
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. ICLR (Poster) (2017)
William Lotter, Greg Sorensen, David D. Cox
A Multi-scale CNN and Curriculum Learning Strategy for Mammogram Classification. DLMIA/ML-CDS@MICCAI (2017)
Chuan-Yung Tsai, Andrew M. Saxe, David D. Cox
Tensor Switching Networks. NIPS (2016)
Chuan-Yung Tsai, Andrew M. Saxe, David D. Cox
Tensor Switching Networks. CoRR (2016)
William Lotter, Gabriel Kreiman, David D. Cox
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. CoRR (2016)
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) (2016)
William Lotter, Gabriel Kreiman, David D. Cox
Unsupervised Learning of Visual Structure using Predictive Generative Networks. CoRR (2015)
Silas Evandro Nachif Fernandes, Walter J. Scheirer, David D. Cox, João Paulo Papa
Improving Optimum-Path Forest Classification Using Confidence Measures. CIARP (2015)
Chuan-Yung Tsai, David D. Cox
Measuring and Understanding Sensory Representations within Deep Networks Using a Numerical Optimization Framework. CoRR (2015)
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)
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 (2015)
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) (2014)
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) (2014)
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) (2014)
Eleonora Vig, Michael Dorr, David D. Cox
Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images. CVPR (2014)
Michael Milford, Walter J. Scheirer, Eleonora Vig, Arren J. Glover, Oliver Baumann, Jason B. Mattingley, David D. Cox
Condition-invariant, top-down visual place recognition. ICRA (2014)
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)
James Bergstra, Dan Yamins, David D. Cox
Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms. SciPy (2013)
James Bergstra, David D. Cox
Hyperparameter Optimization and Boosting for Classifying Facial Expressions: How good can a "Null" Model be? CoRR (2013)
James Bergstra, Nicolas Pinto, David D. Cox
SkData: Data Sets and Algorithm Evaluation Protocols in Python. SciPy (2013)
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 O. 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)
Eleonora Vig, Michael Dorr, David D. Cox
Saliency-based selection of sparse descriptors for action recognition. ICIP (2012)
Eleonora Vig, Michael Dorr, David D. Cox
Space-Variant Descriptor Sampling for Action Recognition Based on Saliency and Eye Movements. ECCV (7) (2012)
James Bergstra, Dan Yamins, David D. Cox
Making a Science of Model Search CoRR (2012)
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)
Nicolas Pinto, David D. Cox
High-throughput-derived biologically-inspired features for unconstrained face recognition. Image Vis. Comput. 30 (3) (2012)
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)
David D. Cox, Nicolas Pinto
Beyond simple features: A large-scale feature search approach to unconstrained face recognition. FG (2011)
Nicolas Pinto, Youssef Barhomi, David D. Cox, James J. DiCarlo
Comparing state-of-the-art visual features on invariant object recognition tasks. WACV (2011)
Nicolas Pinto, David D. Cox
An Evaluation of the Invariance Properties of a Biologically-Inspired System for Unconstrained Face Recognition. BIONETICS (2010)
Vinay Sriram, David D. Cox, Kuen Hung Tsoi, Wayne Luk
Towards an embedded biologically-inspired machine vision processor. FPT (2010)
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)
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)
Nicolas Pinto, David D. Cox, James J. DiCarlo
Why is Real-World Visual Object Recognition Hard? PLoS Comput. Biol. 4 (1) (2008)
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) (2003)