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Robert Fergus

Associate Professor of Computer Science

    20012019
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    Research Output 2001 2019

    Article

    80 million tiny images: A large data set for nonparametric object and scene recognition

    Torralba, A., Fergus, R. & Freeman, W. T., 2008, In : IEEE Transactions on Pattern Analysis and Machine Intelligence. 30, 11, p. 1958-1970 13 p.

    Research output: Contribution to journalArticle

    Large Data Sets
    Semantics
    Internet
    Image resolution
    WordNet

    A hybrid neural network-latent topic model

    Wan, L., Zhu, L. & Fergus, R., 2012, In : Journal of Machine Learning Research. 22, p. 1287-1294 8 p.

    Research output: Contribution to journalArticle

    Neural Networks
    Neural networks
    Hybrid Model
    Discriminative Training
    Model
    Filter
    Tigers
    Search Engine
    Camelus
    Equidae

    Dark flash photography

    Krishnan, D. & Fergus, R., Jul 27 2009, In : ACM Transactions on Graphics. 28, 3, 96.

    Research output: Contribution to journalArticle

    Photography
    Cameras
    Lighting
    Infrared radiation
    Wavelength

    Detection of third and sixth cranial nerve palsies with a novel method for eye tracking while watching a short film clip

    Samadani, U., Farooq, S., Ritlop, R., Warren, F., Reyes, M., Lamm, E., Alex, A., Nehrbass, E., Kolecki, R., Jureller, M., Schneider, J., Chen, A., Shi, C., Mendhiratta, N., Huang, J. H., Qian, M., Kwak, R., Mikheev, A., Rusinek, H., George, A. & 4 others, Fergus, R., Kondziolka, D., Huang, P. P. & Smith, R. T., Mar 1 2015, In : Journal of Neurosurgery. 122, 3, p. 707-720 14 p.

    Research output: Contribution to journalArticle

    Abducens Nerve Diseases
    Oculomotor Nerve
    Motion Pictures
    Surgical Instruments
    Cranial Nerve Diseases

    Image and depth from a conventional camera with a coded aperture

    Levin, A., Fergus, R., Durand, F. & Freeman, W. T., Jul 29 2007, In : ACM Transactions on Graphics. 26, 3, 1276464.

    Research output: Contribution to journalArticle

    Cameras
    Camera lenses
    Image resolution
    Hardware
    Recovery

    Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories

    Fei-Fei, L., Fergus, R. & Perona, P., Apr 2007, In : Computer Vision and Image Understanding. 106, 1, p. 59-70 12 p.

    Research output: Contribution to journalArticle

    Maximum likelihood
    Information use
    Statistical Models

    Learning multiagent communication with backpropagation

    Sukhbaatar, S., Szlam, A. & Fergus, R., 2016, In : Advances in Neural Information Processing Systems. p. 2252-2260 9 p.

    Research output: Contribution to journalArticle

    Backpropagation
    Communication
    Network protocols

    Learning object categories from internet image searches

    Fergus, R., Fei-Fei, L., Perona, P. & Zisserman, A., Aug 2010, In : Proceedings of the IEEE. 98, 8, p. 1453-1466 14 p., 5483225.

    Research output: Contribution to journalArticle

    Internet
    Search engines
    Semantics

    One-shot learning of object categories

    Fei-Fei, L., Fergus, R. & Perona, P., Apr 2006, In : IEEE Transactions on Pattern Analysis and Machine Intelligence. 28, 4, p. 594-611 18 p.

    Research output: Contribution to journalArticle

    Bayesian Approach
    Model Category
    Probability density function
    Maximum likelihood
    Model

    Project 1640 Observations of Brown Dwarf GJ 758 B: Near-infrared Spectrum and Atmospheric Modeling

    Nilsson, R., Veicht, A., Godfrey, P. A. G., Rice, E. L., Aguilar, J., Pueyo, L., Roberts, L. C., Oppenheimer, R., Brenner, D., Luszcz-Cook, S. H., Bacchus, E., Beichman, C., Burruss, R., Cady, E., Dekany, R., Fergus, R., Hillenbrand, L., Hinkley, S., King, D., Lockhart, T. & 6 others, Parry, I. R., Sivaramakrishnan, A., Soummer, R., Vasisht, G., Zhai, C. & Zimmerman, N. T., Mar 20 2017, In : Astrophysical Journal. 838, 1, 64.

    Research output: Contribution to journalArticle

    atmospheric modeling
    near infrared
    infrared spectra
    stars
    sun

    Reconnaissance of the hr 8799 exosolar system. I. near-infrared spectroscopy

    Roberts, L. C., Oppenheimer, B. R., Baranec, C., Beichman, C., Brenner, D., Burruss, R., Cady, E., Crepp, J. R., Dekany, R., Fergus, R., Hale, D., Hillenbrand, L., Hinkley, S., Hogg, D. W., King, D., Ligon, E. R., Lockhart, T., Nilsson, R., Parry, I. R., Pueyo, L. & 13 others, Rice, E., Roberts, J. E., Roberts, L. C., Shao, M., Sivaramakrishnan, A., Soummer, R., Truong, T., Vasisht, G., Veicht, A., Vescelus, F., Wallace, J. K., Zhai, C. & Zimmerman, N., May 1 2013, In : Astrophysical Journal. 768, 1, 24.

    Research output: Contribution to journalArticle

    reconnaissance
    infrared spectroscopy
    planets
    near infrared
    planet

    S4: A spatial-spectral model for speckle suppression

    Fergus, R., Hogg, D. W., Oppenheimer, R., Brenner, D. & Pueyo, L., Oct 20 2014, In : Astrophysical Journal. 794, 2, 161.

    Research output: Contribution to journalArticle

    speckle
    retarding
    brightness
    stars
    error signals

    Weakly supervised scale-invariant learning of models for visual recognition

    Fergus, R., Perona, P. & Zisserman, A., Mar 2007, In : International Journal of Computer Vision. 71, 3, p. 273-303 31 p.

    Research output: Contribution to journalArticle

    Animals
    Railroad cars
    Statistical Models
    Chapter

    Learning binary hash codes for large-scale image search

    Grauman, K. & Fergus, R., 2013, Machine Learning for Computer Vision. Vol. 411. p. 49-87 39 p. (Studies in Computational Intelligence; vol. 411).

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Binary codes
    Content based retrieval
    Object recognition
    Spectrum analysis
    Data structures
    Chapter (peer-reviewed)

    A sparse object category model for efficient learning and complete recognition

    Fergus, R., Perona, P. & Zisserman, A., 2006, Toward category-level object recognition. Ponce, J., Herbert, M., Schmid, C. & Zisserman, A. (eds.). Springer, (1470).

    Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

    Learning binary projections for large scale image search

    Grauman, K. & Fergus, R., 2011, Computer vision. Cipolla, R., Battiato, S. & Farinella, G. (eds.). Springer

    Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

    Object class recognition by unsupervised scale-invariant learning

    Fergus, R., Perona, P. & Zisserman, A., 2006, Cognitive vision systems. Nagel, H. H. (ed.). Springer LNCS, p. 3948

    Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

    Commissioned report

    Efficient methods for object recognition using the constellation model

    Fergus, R., Weber, M. & Perona, P., 2001, Caltech Technical Report.

    Research output: Book/ReportCommissioned report

    Conference contribution

    A Bayesian approach to unsupervised one-shot learning of object categories

    Fei-Fei, L., Fergus, R. & Perona, P., 2003, Proceedings of the IEEE International Conference on Computer Vision. Vol. 2. p. 1134-1141 8 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Motorcycles
    Probability density function
    Aircraft
    Statistical Models

    Adaptive deconvolutional networks for mid and high level feature learning

    Zeiler, M. D., Taylor, G. W. & Fergus, R., 2011, 2011 International Conference on Computer Vision, ICCV 2011. p. 2018-2025 8 p. 6126474

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Classifiers
    Decomposition

    A hybrid neural-network latent topic model

    Wan, L., Zhu, L. & Fergus, R., 2012, Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    A sparse object category model for efficient learning and exhaustive recognition

    Fergus, R., Perona, P. & Zisserman, A., 2005, Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005. Schmid, C., Soatto, S. & Tomasi, C. (eds.). Vol. 1. p. 380-387 8 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Detectors
    Object recognition
    Model structures
    Stars
    Topology

    Beyond frontal faces: Improving Person Recognition using multiple cues

    Zhang, N., Paluri, M., Taigman, Y., Fergus, R. & Bourdev, L., Oct 14 2015, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015. IEEE Computer Society, Vol. 07-12-June-2015. p. 4804-4813 10 p. 7299113

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Image resolution
    Lighting
    Cameras
    Experiments

    Blind deconvolution using a normalized sparsity measure

    Krishnan, D., Tay, T. & Fergus, R., 2011, 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011. p. 233-240 8 p. 5995521

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Deconvolution
    Cost functions
    Costs

    Case for automated detection of diabetic retinopathy

    Silberman, N., Ahlrich, K., Fergus, R. & Subramanian, L., 2010, Artificial Intelligence for Development - Papers from the AAAI Spring Symposium, Technical Report. Vol. SS-10-01. p. 85-90 6 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Computer vision
    Medical problems
    Explosions
    Color

    Composable planning with attributes

    Zhang, A., Lerer, A., Sukhbaatar, S., Fergus, R. & Szlam, A., Jan 1 2018, 35th International Conference on Machine Learning, ICML 2018. Krause, A. & Dy, J. (eds.). International Machine Learning Society (IMLS), Vol. 13. p. 9292-9307 16 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Planning

    Convolutional learning of spatio-temporal features

    Taylor, G. W., Fergus, R., LeCun, Y. & Bregler, C., 2010, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 6 ed. Vol. 6316 LNCS. p. 140-153 14 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 6316 LNCS, no. PART 6).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Action Recognition
    Image Sequence
    Parametrization
    Model
    Flow Field

    Deconvolutional networks

    Zeiler, M. D., Krishnan, D., Taylor, G. W. & Fergus, R., 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010. p. 2528-2535 8 p. 5539957

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Decomposition
    Detectors

    Deep End2End Voxel2Voxel Prediction

    Tran, D., Bourdev, L., Fergus, R., Torresani, L. & Paluri, M., Dec 16 2016, Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016. IEEE Computer Society, p. 402-409 8 p. 7789547

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Optical flows
    Coloring
    Processing
    Labels
    Semantics

    Deep generative image models using a laplacian pyramid of adversarial networks

    Denton, E., Chintala, S., Szlam, A. & Fergus, R., 2015, Advances in Neural Information Processing Systems. Neural information processing systems foundation, Vol. 2015-January. p. 1486-1494 9 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Image resolution

    Depth map prediction from a single image using a multi-scale deep network

    Eigen, D., Puhrsch, C. & Fergus, R., 2014, Advances in Neural Information Processing Systems. January ed. Neural information processing systems foundation, Vol. 3. p. 2366-2374 9 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Geometry
    Uncertainty

    End-to-end integration of a Convolutional Network, Deformable Parts Model and non-maximum suppression

    Wan, L., Eigen, D. & Fergus, R., Oct 14 2015, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015. IEEE Computer Society, Vol. 07-12-June-2015. p. 851-859 9 p. 7298686

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Volatile organic compounds
    Pixels
    Processing
    Chemical analysis
    Object detection

    End-to-end memory networks

    Sukhbaatar, S., Szlam, A., Weston, J. & Fergus, R., 2015, Advances in Neural Information Processing Systems. Neural information processing systems foundation, Vol. 2015-January. p. 2440-2448 9 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Data storage equipment
    Neural networks

    Exploiting linear structure within convolutional networks for efficient evaluation

    Denton, E., Zaremba, W., Bruna, J., LeCun, Y. & Fergus, R., 2014, Advances in Neural Information Processing Systems. January ed. Neural information processing systems foundation, Vol. 2. p. 1269-1277 9 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Smartphones
    Object recognition
    Convolution
    Program processors
    Redundancy

    Facial expression transfer with input-output Temporal Restricted Boltzmann Machines

    Zeiler, M. D., Taylor, G. W., Sigal, L., Matthews, I. & Fergus, R., 2011, Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Probability distributions

    Fast image deconvolution using hyper-laplacian priors

    Krishnan, D. & Fergus, R., 2009, Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. p. 1033-1041 9 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Deconvolution
    Pixels
    Table lookup
    Image processing
    Polynomials

    Image and depth from a conventional camera with a coded aperture

    Levin, A., Fergus, R., Durand, F. & Freeman, W. T., 2007, Proceedings of ACM SIGGRAPH 2007. 70

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Cameras
    Camera lenses
    Image resolution
    Hardware
    Recovery

    Improving image classification with location context

    Tang, K., Paluri, M., Fei-Fei, L., Fergus, R. & Bourdev, L., Feb 17 2016, Proceedings - 2015 IEEE International Conference on Computer Vision, ICCV 2015. Institute of Electrical and Electronics Engineers Inc., Vol. 11-18-December-2015. p. 1008-1016 9 p. 7410478

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Image classification
    Global positioning system
    Availability
    Neural networks
    Image recognition

    Indoor scene segmentation using a structured light sensor

    Silberman, N. & Fergus, R., 2011, 2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011. p. 601-608 8 p. 6130298

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Sensors
    Labels

    Indoor segmentation and support inference from RGBD images

    Silberman, N., Hoiem, D., Kohli, P. & Fergus, R., 2012, Computer Vision, ECCV 2012 - 12th European Conference on Computer Vision, Proceedings. PART 5 ed. Vol. 7576 LNCS. p. 746-760 15 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 7576 LNCS, no. PART 5).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Segmentation
    Integer programming
    Integer Programming
    Annotation
    Verify

    Instance segmentation of indoor scenes using a coverage loss

    Silberman, N., Sontag, D. & Fergus, R., 2014, Computer Vision, ECCV 2014 - 13th European Conference, Proceedings. PART 1 ed. Springer Verlag, Vol. 8689 LNCS. p. 616-631 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 8689 LNCS, no. PART 1).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Coverage
    Segmentation
    Semantics
    Pixel
    Pixels

    Learning by Asking Questions

    Misra, I., Girshick, R., Fergus, R., Hebert, M., Gupta, A. & Van Der Maaten, L., Dec 14 2018, Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018. IEEE Computer Society, p. 11-20 10 p. 8578107. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Curricula
    Learning systems
    Testing

    Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories

    Fei-Fei, L., Fergus, R. & Perona, P., 2004, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. January ed. IEEE Computer Society, Vol. 2004-January. 1384978

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Maximum likelihood
    Information use
    Statistical Models

    Learning invariance through imitation

    Taylor, G. W., Spiro, I., Bregler, C. & Fergus, R., 2011, 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011. p. 2729-2736 8 p. 5995538

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Invariance
    Labels
    Lighting

    Learning invariant features through topographic filter maps

    Kavukcuoglu, K., Ranzato, MA., Fergus, R. & LeCun, Y., 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. p. 1605-1612 8 p. 5206545

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Image recognition
    Object recognition
    Feature extraction
    Classifiers
    Detectors

    Learning object categories from Google's image search

    Fergus, R., Fei-Fei, L., Perona, P. & Zisserman, A., 2005, Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005. Vol. II. p. 1816-1823 8 p. 1544937

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Search engines
    Object recognition
    Internet

    Learning physical intuition of block towers by example

    Lerer, A., Gross, S. & Fergus, R., 2016, 33rd International Conference on Machine Learning, ICML 2016. International Machine Learning Society (IMLS), Vol. 1. p. 648-656 9 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Towers
    Physics
    Trajectories
    Engines

    Learning simple algorithms from examples

    Zaremba, W., Mikolov, T., Joulin, A. & Fergus, R., 2016, 33rd International Conference on Machine Learning, ICML 2016. International Machine Learning Society (IMLS), Vol. 1. p. 639-647 9 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Controllers
    Copying
    Tapes
    Neural networks

    Learning spatiotemporal features with 3D convolutional networks

    Tran, D., Bourdev, L., Fergus, R., Torresani, L. & Paluri, M., Feb 17 2016, Proceedings - 2015 IEEE International Conference on Computer Vision, ICCV 2015. Institute of Electrical and Electronics Engineers Inc., Vol. 11-18-December-2015. p. 4489-4497 9 p. 7410867

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Convolution
    Classifiers
    Deep learning