default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDRob Fergus
Robert Fergus
Person information
- affiliation: New York University, Courant Institute
Other persons with a similar name
- R. Ian Ferguson
![[0000-0001-9866-6182]](https://dblp.org/img/orcid-mark.12x12.png "0000-0001-9866-6182")
(aka: Robert Ian Ferguson) — University of Abertay Dundee, School of Computing and Engineering Systems, UK (and 2 more) - Robert Ferguson
- Robert Douglas Ferguson
- Robert I. Ferguson
- Robert W. Ferguson
- Stuart Ferguson (aka: Robin Stuart Ferguson) — Queen's University Belfast, Belfast, UK
- Janet Ferguson-Roberts
- Robert J. Fergusson
- Fergus Robertson
SPARQL queries 
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[c81]Nishant Yadav, Nicholas Monath, Manzil Zaheer, Rob Fergus, Andrew McCallum:
Adaptive Retrieval and Scalable Indexing for k-NN Search with Cross-Encoders. ICLR 2024- [c80]Seungyeon Kim, Ankit Singh Rawat, Manzil Zaheer, Wittawat Jitkrittum, Veeranjaneyulu Sadhanala, Sadeep Jayasumana, Aditya Krishna Menon, Rob Fergus, Sanjiv Kumar:
USTAD: Unified Single-model Training Achieving Diverse Scores for Information Retrieval. ICML 2024 - [c79]Nicholas Monath, Will Sussman Grathwohl, Michael Boratko, Rob Fergus, Andrew McCallum, Manzil Zaheer:
A Fresh Take on Stale Embeddings: Improving Dense Retriever Training with Corrector Networks. ICML 2024 - [c78]Ulyana Piterbarg, Lerrel Pinto, Rob Fergus:
diff History for Neural Language Agents. ICML 2024 - [i56]Nishant Yadav, Nicholas Monath, Manzil Zaheer, Rob Fergus, Andrew McCallum:
Adaptive Retrieval and Scalable Indexing for k-NN Search with Cross-Encoders. CoRR abs/2405.03651 (2024) - [i55]Shengbang Tong, Ellis Brown, Penghao Wu, Sanghyun Woo, Manoj Middepogu, Sai Charitha Akula, Jihan Yang, Shusheng Yang, Adithya Iyer, Xichen Pan, Austin Wang, Rob Fergus, Yann LeCun, Saining Xie:
Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs. CoRR abs/2406.16860 (2024) - [i54]Anthony GX-Chen, Kenneth Marino, Rob Fergus:
Efficient Exploration and Discriminative World Model Learning with an Object-Centric Abstraction. CoRR abs/2408.11816 (2024) - [i53]Nicholas Monath, Will Grathwohl, Michael Boratko, Rob Fergus, Andrew McCallum, Manzil Zaheer:
A Fresh Take on Stale Embeddings: Improving Dense Retriever Training with Corrector Networks. CoRR abs/2409.01890 (2024) - [i52]Ulyana Piterbarg, Lerrel Pinto, Rob Fergus:
Training Language Models on Synthetic Edit Sequences Improves Code Synthesis. CoRR abs/2410.02749 (2024) - 2023
- [c77]Jake Bruce, Ankit Anand, Bogdan Mazoure, Rob Fergus:
Learning About Progress From Experts. ICLR 2023 - [c76]Manzil Zaheer, Ankit Singh Rawat, Seungyeon Kim, Chong You, Himanshu Jain, Andreas Veit, Rob Fergus, Sanjiv Kumar:
Teacher Guided Training: An Efficient Framework for Knowledge Transfer. ICLR 2023 - [c75]Yilun Du, Conor Durkan, Robin Strudel, Joshua B. Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, Will Sussman Grathwohl:
Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC. ICML 2023: 8489-8510 - [c74]Theodore R. Sumers, Kenneth Marino, Arun Ahuja, Rob Fergus, Ishita Dasgupta:
Distilling Internet-Scale Vision-Language Models into Embodied Agents. ICML 2023: 32797-32818 - [c73]Ulyana Piterbarg, Lerrel Pinto, Rob Fergus:
NetHack is Hard to Hack. NeurIPS 2023 - [i51]Seungyeon Kim, Ankit Singh Rawat, Manzil Zaheer, Sadeep Jayasumana, Veeranjaneyulu Sadhanala, Wittawat Jitkrittum, Aditya Krishna Menon, Rob Fergus, Sanjiv Kumar:
EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval. CoRR abs/2301.12005 (2023) - [i50]Theodore R. Sumers, Kenneth Marino, Arun Ahuja, Rob Fergus, Ishita Dasgupta:
Distilling Internet-Scale Vision-Language Models into Embodied Agents. CoRR abs/2301.12507 (2023) - [i49]Ishita Dasgupta, Christine Kaeser-Chen, Kenneth Marino, Arun Ahuja, Sheila Babayan, Felix Hill, Rob Fergus:
Collaborating with language models for embodied reasoning. CoRR abs/2302.00763 (2023) - [i48]Yilun Du, Conor Durkan, Robin Strudel, Joshua B. Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, Will Grathwohl:
Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC. CoRR abs/2302.11552 (2023) - [i47]Bogdan Mazoure, Jake Bruce, Doina Precup, Rob Fergus, Ankit Anand:
Accelerating exploration and representation learning with offline pre-training. CoRR abs/2304.00046 (2023) - [i46]Ulyana Piterbarg, Lerrel Pinto, Rob Fergus:
NetHack is Hard to Hack. CoRR abs/2305.19240 (2023) - [i45]Arun Ahuja, Kavya Kopparapu, Rob Fergus, Ishita Dasgupta:
Hierarchical reinforcement learning with natural language subgoals. CoRR abs/2309.11564 (2023) - [i44]Ulyana Piterbarg, Lerrel Pinto, Rob Fergus:
diff History for Long-Context Language Agents. CoRR abs/2312.07540 (2023) - 2022
- [j10]Ronan Riochet, Mario Ynocente Castro, Mathieu Bernard, Adam Lerer, Rob Fergus, Véronique Izard, Emmanuel Dupoux:
IntPhys 2019: A Benchmark for Visual Intuitive Physics Understanding. IEEE Trans. Pattern Anal. Mach. Intell. 44(9): 5016-5025 (2022) - [c72]Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto:
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning. ICLR 2022 - [c71]Manzil Zaheer, Kenneth Marino, Will Grathwohl, John Schultz, Wendy Shang, Sheila Babayan, Arun Ahuja, Ishita Dasgupta, Christine Kaeser-Chen, Rob Fergus:
Learning to Navigate Wikipedia by Taking Random Walks. NeurIPS 2022 - [i43]Manzil Zaheer, Ankit Singh Rawat, Seungyeon Kim, Chong You, Himanshu Jain, Andreas Veit, Rob Fergus, Sanjiv Kumar:
Teacher Guided Training: An Efficient Framework for Knowledge Transfer. CoRR abs/2208.06825 (2022) - [i42]Manzil Zaheer, Kenneth Marino, Will Grathwohl, John Schultz, Wendy Shang, Sheila Babayan, Arun Ahuja, Ishita Dasgupta, Christine Kaeser-Chen, Rob Fergus:
Learning to Navigate Wikipedia by Taking Random Walks. CoRR abs/2211.00177 (2022) - 2021
- [j9]Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, Rob Fergus:
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc. Natl. Acad. Sci. USA 118(15): e2016239118 (2021) - [c70]Denis Yarats, Amy Zhang, Ilya Kostrikov, Brandon Amos, Joelle Pineau, Rob Fergus:
Improving Sample Efficiency in Model-Free Reinforcement Learning from Images. AAAI 2021: 10674-10681 - [c69]Denis Yarats, Ilya Kostrikov, Rob Fergus:
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels. ICLR 2021 - [c68]Andrew Jaegle, Yury Sulsky, Arun Ahuja, Jake Bruce, Rob Fergus, Greg Wayne:
Imitation by Predicting Observations. ICML 2021: 4665-4676 - [c67]Ilya Kostrikov, Rob Fergus, Jonathan Tompson, Ofir Nachum:
Offline Reinforcement Learning with Fisher Divergence Critic Regularization. ICML 2021: 5774-5783 - [c66]Roberta Raileanu, Rob Fergus:
Decoupling Value and Policy for Generalization in Reinforcement Learning. ICML 2021: 8787-8798 - [c65]Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto:
Reinforcement Learning with Prototypical Representations. ICML 2021: 11920-11931 - [c64]Roberta Raileanu, Maxwell Goldstein, Denis Yarats, Ilya Kostrikov, Rob Fergus:
Automatic Data Augmentation for Generalization in Reinforcement Learning. NeurIPS 2021: 5402-5415 - [i41]Roberta Raileanu, Rob Fergus:
Decoupling Value and Policy for Generalization in Reinforcement Learning. CoRR abs/2102.10330 (2021) - [i40]Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto:
Reinforcement Learning with Prototypical Representations. CoRR abs/2102.11271 (2021) - [i39]Ilya Kostrikov, Jonathan Tompson, Rob Fergus, Ofir Nachum:
Offline Reinforcement Learning with Fisher Divergence Critic Regularization. CoRR abs/2103.08050 (2021) - [i38]Andrew Jaegle, Yury Sulsky, Arun Ahuja, Jake Bruce, Rob Fergus, Greg Wayne:
Imitation by Predicting Observations. CoRR abs/2107.03851 (2021) - [i37]Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto:
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning. CoRR abs/2107.09645 (2021) - 2020
- [c63]Yilun Du, Joshua Meier, Jerry Ma, Rob Fergus, Alexander Rives:
Energy-based models for atomic-resolution protein conformations. ICLR 2020 - [c62]Roberta Raileanu, Maxwell Goldstein, Arthur Szlam, Rob Fergus:
Fast Adaptation to New Environments via Policy-Dynamics Value Functions. ICML 2020: 7920-7931 - [i36]Yilun Du, Joshua Meier, Jerry Ma, Rob Fergus, Alexander Rives:
Energy-based models for atomic-resolution protein conformations. CoRR abs/2004.13167 (2020) - [i35]Ilya Kostrikov, Denis Yarats, Rob Fergus:
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels. CoRR abs/2004.13649 (2020) - [i34]Roberta Raileanu, Maxwell Goldstein, Denis Yarats, Ilya Kostrikov, Rob Fergus:
Automatic Data Augmentation for Generalization in Deep Reinforcement Learning. CoRR abs/2006.12862 (2020) - [i33]Kenneth Marino, Rob Fergus, Arthur Szlam, Abhinav Gupta:
Empirically Verifying Hypotheses Using Reinforcement Learning. CoRR abs/2006.15762 (2020) - [i32]Roberta Raileanu, Maxwell Goldstein, Arthur Szlam, Rob Fergus:
Fast Adaptation via Policy-Dynamics Value Functions. CoRR abs/2007.02879 (2020)
2010 – 2019
- 2019
- [c61]Ethan Perez, Siddharth Karamcheti, Rob Fergus, Jason Weston, Douwe Kiela, Kyunghyun Cho:
Finding Generalizable Evidence by Learning to Convince Q&A Models. EMNLP/IJCNLP (1) 2019: 2402-2411 - [c60]Kenneth Marino, Abhinav Gupta, Rob Fergus, Arthur Szlam:
Hierarchical RL Using an Ensemble of Proprioceptive Periodic Policies. ICLR (Poster) 2019 - [i31]William F. Whitney, Rob Fergus:
Disentangling Video with Independent Prediction. CoRR abs/1901.05590 (2019) - [i30]Ethan Perez, Siddharth Karamcheti, Rob Fergus, Jason Weston, Douwe Kiela, Kyunghyun Cho:
Finding Generalizable Evidence by Learning to Convince Q&A Models. CoRR abs/1909.05863 (2019) - [i29]Denis Yarats, Amy Zhang, Ilya Kostrikov, Brandon Amos, Joelle Pineau, Rob Fergus:
Improving Sample Efficiency in Model-Free Reinforcement Learning from Images. CoRR abs/1910.01741 (2019) - 2018
- [c59]Ishan Misra, Ross B. Girshick, Rob Fergus, Martial Hebert, Abhinav Gupta, Laurens van der Maaten:
Learning by Asking Questions. CVPR 2018: 11-20 - [c58]Sainbayar Sukhbaatar, Zeming Lin, Ilya Kostrikov, Gabriel Synnaeve, Arthur Szlam, Rob Fergus:
Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play. ICLR (Poster) 2018 - [c57]Emily Denton, Rob Fergus:
Stochastic Video Generation with a Learned Prior. ICML 2018: 1182-1191 - [c56]Roberta Raileanu, Emily Denton, Arthur Szlam, Rob Fergus:
Modeling Others using Oneself in Multi-Agent Reinforcement Learning. ICML 2018: 4254-4263 - [c55]Amy Zhang, Sainbayar Sukhbaatar, Adam Lerer, Arthur Szlam, Rob Fergus:
Composable Planning with Attributes. ICML 2018: 5837-5846 - [i28]Emily Denton, Rob Fergus:
Stochastic Video Generation with a Learned Prior. CoRR abs/1802.07687 (2018) - [i27]Roberta Raileanu, Emily Denton, Arthur Szlam, Rob Fergus:
Modeling Others using Oneself in Multi-Agent Reinforcement Learning. CoRR abs/1802.09640 (2018) - [i26]Amy Zhang, Adam Lerer, Sainbayar Sukhbaatar, Rob Fergus, Arthur Szlam:
Composable Planning with Attributes. CoRR abs/1803.00512 (2018) - [i25]Ronan Riochet, Mario Ynocente Castro, Mathieu Bernard, Adam Lerer, Rob Fergus, Véronique Izard, Emmanuel Dupoux:
IntPhys: A Framework and Benchmark for Visual Intuitive Physics Reasoning. CoRR abs/1803.07616 (2018) - [i24]Sainbayar Sukhbaatar, Emily Denton, Arthur Szlam, Rob Fergus:
Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement Learning. CoRR abs/1811.09083 (2018) - 2017
- [e1]Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, Roman Garnett:
Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA. 2017 [contents] - [i23]Sainbayar Sukhbaatar, Ilya Kostrikov, Arthur Szlam, Rob Fergus:
Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play. CoRR abs/1703.05407 (2017) - [i22]Ishan Misra, Ross B. Girshick, Rob Fergus, Martial Hebert, Abhinav Gupta, Laurens van der Maaten:
Learning by Asking Questions. CoRR abs/1712.01238 (2017) - 2016
- [c54]Du Tran, Lubomir D. Bourdev, Rob Fergus, Lorenzo Torresani, Manohar Paluri:
Deep End2End Voxel2Voxel Prediction. CVPR Workshops 2016: 402-409 - [c53]Wojciech Zaremba, Tomás Mikolov, Armand Joulin, Rob Fergus:
Learning Simple Algorithms from Examples. ICML 2016: 421-429 - [c52]Adam Lerer, Sam Gross, Rob Fergus:
Learning Physical Intuition of Block Towers by Example. ICML 2016: 430-438 - [c51]Sainbayar Sukhbaatar, Arthur Szlam, Rob Fergus:
Learning Multiagent Communication with Backpropagation. NIPS 2016: 2244-2252 - [i21]Adam Lerer, Sam Gross, Rob Fergus:
Learning Physical Intuition of Block Towers by Example. CoRR abs/1603.01312 (2016) - [i20]Sainbayar Sukhbaatar, Arthur Szlam, Rob Fergus:
Learning Multiagent Communication with Backpropagation. CoRR abs/1605.07736 (2016) - [i19]Emily L. Denton, Sam Gross, Rob Fergus:
Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks. CoRR abs/1611.06430 (2016) - 2015
- [c50]Brenden M. Lake, Wojciech Zaremba, Rob Fergus, Todd M. Gureckis:
Deep Neural Networks Predict Category Typicality Ratings for Images. CogSci 2015 - [c49]Yunchao Gong, Marcin Pawlowski, Fei Yang, Louis Brandy, Lubomir D. Bourdev, Rob Fergus:
Web scale photo hash clustering on a single machine. CVPR 2015: 19-27 - [c48]Li Wan, David Eigen, Rob Fergus:
End-to-end integration of a Convolutional Network, Deformable Parts Model and non-maximum suppression. CVPR 2015: 851-859 - [c47]Ning Zhang, Manohar Paluri, Yaniv Taigman, Rob Fergus, Lubomir D. Bourdev:
Beyond frontal faces: Improving Person Recognition using multiple cues. CVPR 2015: 4804-4813 - [c46]Kevin D. Tang, Manohar Paluri, Li Fei-Fei, Robert Fergus, Lubomir D. Bourdev:
Improving Image Classification with Location Context. ICCV 2015: 1008-1016 - [c45]David Eigen, Rob Fergus:
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture. ICCV 2015: 2650-2658 - [c44]Du Tran, Lubomir D. Bourdev, Rob Fergus, Lorenzo Torresani, Manohar Paluri:
Learning Spatiotemporal Features with 3D Convolutional Networks. ICCV 2015: 4489-4497 - [c43]Emily Denton, Jason Weston, Manohar Paluri, Lubomir D. Bourdev, Rob Fergus:
User Conditional Hashtag Prediction for Images. KDD 2015: 1731-1740 - [c42]Emily L. Denton, Soumith Chintala, Arthur Szlam, Rob Fergus:
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks. NIPS 2015: 1486-1494 - [c41]Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, Rob Fergus:
End-To-End Memory Networks. NIPS 2015: 2440-2448 - [c40]Sainbayar Sukhbaatar, Rob Fergus:
Learning from Noisy Labels with Deep Neural Networks. ICLR (Workshop) 2015 - [i18]Ning Zhang, Manohar Paluri, Yaniv Taigman, Rob Fergus, Lubomir D. Bourdev:
Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues. CoRR abs/1501.05703 (2015) - [i17]Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, Rob Fergus:
Weakly Supervised Memory Networks. CoRR abs/1503.08895 (2015) - [i16]Kevin D. Tang, Manohar Paluri, Li Fei-Fei, Rob Fergus, Lubomir D. Bourdev:
Improving Image Classification with Location Context. CoRR abs/1505.03873 (2015) - [i15]Emily L. Denton, Soumith Chintala, Arthur Szlam, Robert Fergus:
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks. CoRR abs/1506.05751 (2015) - [i14]Du Tran, Lubomir D. Bourdev, Rob Fergus, Lorenzo Torresani, Manohar Paluri:
Deep End2End Voxel2Voxel Prediction. CoRR abs/1511.06681 (2015) - [i13]Wojciech Zaremba, Tomás Mikolov, Armand Joulin, Rob Fergus:
Learning Simple Algorithms from Examples. CoRR abs/1511.07275 (2015) - [i12]Sainbayar Sukhbaatar, Arthur Szlam, Gabriel Synnaeve, Soumith Chintala, Rob Fergus:
MazeBase: A Sandbox for Learning from Games. CoRR abs/1511.07401 (2015) - [i11]Bolei Zhou, Yuandong Tian, Sainbayar Sukhbaatar, Arthur Szlam, Rob Fergus:
Simple Baseline for Visual Question Answering. CoRR abs/1512.02167 (2015) - 2014
- [c39]Nathan Silberman, David A. Sontag, Rob Fergus:
Instance Segmentation of Indoor Scenes Using a Coverage Loss. ECCV (1) 2014: 616-631 - [c38]Matthew D. Zeiler, Rob Fergus:
Visualizing and Understanding Convolutional Networks. ECCV (1) 2014: 818-833 - [c37]Emily L. Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, Rob Fergus:
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation. NIPS 2014: 1269-1277 - [c36]Wojciech Zaremba, Karol Kurach, Rob Fergus:
Learning to Discover Efficient Mathematical Identities. NIPS 2014: 1278-1286 - [c35]David Eigen, Christian Puhrsch, Rob Fergus:
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network. NIPS 2014: 2366-2374 - [c34]David Eigen, Jason Tyler Rolfe, Rob Fergus, Yann LeCun:
Understanding Deep Architectures using a Recursive Convolutional Network. ICLR (Workshop Poster) 2014 - [c33]Pierre Sermanet, David Eigen, Xiang Zhang, Michaël Mathieu, Rob Fergus, Yann LeCun:
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks. ICLR 2014 - [c32]Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, Rob Fergus:
Intriguing properties of neural networks. ICLR (Poster) 2014 - [i10]Emily Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, Rob Fergus:
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation. CoRR abs/1404.0736 (2014) - [i9]Wojciech Zaremba, Karol Kurach, Rob Fergus:
Learning to Discover Efficient Mathematical Identities. CoRR abs/1406.1584 (2014) - [i8]David Eigen, Christian Puhrsch, Rob Fergus:
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network. CoRR abs/1406.2283 (2014) - [i7]Lubomir D. Bourdev, Fei Yang, Rob Fergus:
Deep Poselets for Human Detection. CoRR abs/1407.0717 (2014) - [i6]David Eigen, Rob Fergus:
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture. CoRR abs/1411.4734 (2014) - [i5]Li Wan, David Eigen, Rob Fergus:
End-to-End Integration of a Convolutional Network, Deformable Parts Model and Non-Maximum Suppression. CoRR abs/1411.5309 (2014) - [i4]Du Tran, Lubomir D. Bourdev, Rob Fergus, Lorenzo Torresani, Manohar Paluri:
C3D: Generic Features for Video Analysis. CoRR abs/1412.0767 (2014) - 2013
- [c31]David Eigen, Dilip Krishnan, Rob Fergus:
Restoring an Image Taken through a Window Covered with Dirt or Rain. ICCV 2013: 633-640 - [c30]Li Wan, Matthew D. Zeiler, Sixin Zhang, Yann LeCun, Rob Fergus:
Regularization of Neural Networks using DropConnect. ICML (3) 2013: 1058-1066 - [c29]Matthew D. Zeiler, Rob Fergus:
Stochastic Pooling for Regularization of Deep Convolutional Neural Networks. ICLR 2013 - [p1]Kristen Grauman, Rob Fergus:
Learning Binary Hash Codes for Large-Scale Image Search. Machine Learning for Computer Vision 2013: 49-87 - [i3]Matthew D. Zeiler, Rob Fergus:
Visualizing and Understanding Convolutional Networks. CoRR abs/1311.2901 (2013) - [i2]Dilip Krishnan, Joan Bruna, Rob Fergus:
Blind Deconvolution with Re-weighted Sparsity Promotion. CoRR abs/1311.4029 (2013) - 2012
- [c28]David Eigen, Rob Fergus:
Nonparametric image parsing using adaptive neighbor sets. CVPR 2012: 2799-2806 - [c27]Yair Weiss, Rob Fergus, Antonio Torralba:
Multidimensional Spectral Hashing. ECCV (5) 2012: 340-353 - [c26]Nathan Silberman, Derek Hoiem, Pushmeet Kohli, Rob Fergus:
Indoor Segmentation and Support Inference from RGBD Images. ECCV (5) 2012: 746-760 - [c25]Li Wan, Leo Zhu, Rob Fergus:
A Hybrid Neural Network-Latent Topic Model. AISTATS 2012: 1287-1294 - [i1]Matthew D. Zeiler, Rob Fergus:
Differentiable Pooling for Hierarchical Feature Learning. CoRR abs/1207.0151 (2012) - 2011
- [c24]Dilip Krishnan, Terence Tay, Rob Fergus:
Blind deconvolution using a normalized sparsity measure. CVPR 2011: 233-240 - [c23]Graham W. Taylor, Ian Spiro, Christoph Bregler, Rob Fergus:
Learning invariance through imitation. CVPR 2011: 2729-2736 - [c22]Matthew D. Zeiler, Graham W. Taylor, Rob Fergus:
Adaptive deconvolutional networks for mid and high level feature learning. ICCV 2011: 2018-2025 - [c21]Nathan Silberman, Rob Fergus:
Indoor scene segmentation using a structured light sensor. ICCV Workshops 2011: 601-608 - [c20]Matthew D. Zeiler, Graham W. Taylor, Leonid Sigal, Iain A. Matthews, Rob Fergus:
Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines. NIPS 2011: 1629-1637 - 2010
- [j8]Robert Fergus, Li Fei-Fei, Pietro Perona, Andrew Zisserman:
Learning Object Categories From Internet Image Searches. Proc. IEEE 98(8): 1453-1466 (2010) - [c19]Nathan Silberman, Kristy Ahrlich, Rob Fergus, Lakshminarayanan Subramanian:
Case for Automated Detection of Diabetic Retinopathy. AAAI Spring Symposium: Artificial Intelligence for Development 2010 - [c18]Matthew D. Zeiler, Dilip Krishnan, Graham W. Taylor, Robert Fergus:
Deconvolutional networks. CVPR 2010: 2528-2535 - [c17]Graham W. Taylor, Rob Fergus, Yann LeCun, Christoph Bregler:
Convolutional Learning of Spatio-temporal Features. ECCV (6) 2010: 140-153 - [c16]Robert Fergus, Hector Bernal, Yair Weiss, Antonio Torralba:
Semantic Label Sharing for Learning with Many Categories. ECCV (1) 2010: 762-775 - [c15]Graham W. Taylor, Rob Fergus, George Williams, Ian Spiro, Christoph Bregler:
Pose-Sensitive Embedding by Nonlinear NCA Regression. NIPS 2010: 2280-2288
2000 – 2009
- 2009
- [j7]Dilip Krishnan, Rob Fergus:
Dark flash photography. ACM Trans. Graph. 28(3): 96 (2009) - [c14]Koray Kavukcuoglu, Marc'Aurelio Ranzato, Rob Fergus, Yann LeCun:
Learning invariant features through topographic filter maps. CVPR 2009: 1605-1612 - [c13]Rob Fergus, Yair Weiss, Antonio Torralba:
Semi-Supervised Learning in Gigantic Image Collections. NIPS 2009: 522-530 - [c12]Dilip Krishnan, Rob Fergus:
Fast Image Deconvolution using Hyper-Laplacian Priors. NIPS 2009: 1033-1041 - 2008
- [j6]Antonio Torralba, Robert Fergus, William T. Freeman:
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition. IEEE Trans. Pattern Anal. Mach. Intell. 30(11): 1958-1970 (2008) - [c11]Antonio Torralba, Robert Fergus, Yair Weiss:
Small codes and large image databases for recognition. CVPR 2008 - [c10]Yair Weiss, Antonio Torralba, Robert Fergus:
Spectral Hashing. NIPS 2008: 1753-1760 - 2007
- [j5]Li Fei-Fei, Robert Fergus, Pietro Perona:
Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories. Comput. Vis. Image Underst. 106(1): 59-70 (2007) - [j4]Robert Fergus, Pietro Perona, Andrew Zisserman:
Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition. Int. J. Comput. Vis. 71(3): 273-303 (2007) - [j3]Anat Levin, Robert Fergus, Frédo Durand, William T. Freeman:
Image and depth from a conventional camera with a coded aperture. ACM Trans. Graph. 26(3): 70 (2007) - [c9]Bryan C. Russell, Antonio Torralba, Ce Liu, Robert Fergus, William T. Freeman:
Object Recognition by Scene Alignment. NIPS 2007: 1241-1248 - 2006
- [j2]Li Fei-Fei, Robert Fergus, Pietro Perona:
One-Shot Learning of Object Categories. IEEE Trans. Pattern Anal. Mach. Intell. 28(4): 594-611 (2006) - [j1]Robert Fergus, Barun Singh, Aaron Hertzmann, Sam T. Roweis, William T. Freeman:
Removing camera shake from a single photograph. ACM Trans. Graph. 25(3): 787-794 (2006) - [c8]Robert Fergus, Pietro Perona, Andrew Zisserman:
A Sparse Object Category Model for Efficient Learning and Complete Recognition. Toward Category-Level Object Recognition 2006: 443-461 - 2005
- [b1]Robert Fergus:
Visual object category recognition. University of Oxford, UK, 2005 - [c7]Robert Fergus, Pietro Perona, Andrew Zisserman:
A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition. CVPR (1) 2005: 380-387 - [c6]Robert Fergus, Li Fei-Fei, Pietro Perona, Andrew Zisserman:
Learning Object Categories from Google's Image Search. ICCV 2005: 1816-1823 - 2004
- [c5]Li Fei-Fei, Rob Fergus, Pietro Perona:
Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories. CVPR Workshops 2004: 178 - [c4]Robert Fergus, Pietro Perona, Andrew Zisserman:
A Visual Category Filter for Google Images. ECCV (1) 2004: 242-256 - [c3]Robert Fergus, Andrew Zisserman, Pietro Perona:
Sampling Methods for Unsupervised Learning. NIPS 2004: 433-440 - 2003
- [c2]Robert Fergus, Pietro Perona, Andrew Zisserman:
Object Class Recognition by Unsupervised Scale-Invariant Learning. CVPR (2) 2003: 264-271 - [c1]Li Fei-Fei, Robert Fergus, Pietro Perona:
A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories. ICCV 2003: 1134-1141
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-08 20:33 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
