default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDJasper Snoek
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[j13]Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani:
Pre-trained Gaussian Processes for Bayesian Optimization. J. Mach. Learn. Res. 25: 212:1-212:83 (2024)- [j12]Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron T. Parisi, Abhishek Kumar, Alexander A. Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin Fathy Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel:
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. Trans. Mach. Learn. Res. 2024 (2024) - [c37]James Harrison, John Willes, Jasper Snoek:
Variational Bayesian Last Layers. ICLR 2024 - [i37]James Harrison, John Willes, Jasper Snoek:
Variational Bayesian Last Layers. CoRR abs/2404.11599 (2024) - [i36]Jiri Hron, Laura Culp, Gamaleldin F. Elsayed, Rosanne Liu, Ben Adlam, Maxwell L. Bileschi, Bernd Bohnet, JD Co-Reyes, Noah Fiedel, C. Daniel Freeman, Izzeddin Gur, Kathleen Kenealy, Jaehoon Lee, Peter J. Liu, Gaurav Mishra, Igor Mordatch, Azade Nova, Roman Novak, Aaron Parisi, Jeffrey Pennington, Alex Rizkowsky, Isabelle Simpson, Hanie Sedghi, Jascha Sohl-Dickstein, Kevin Swersky, Sharad Vikram, Tris Warkentin, Lechao Xiao, Kelvin Xu, Jasper Snoek, Simon Kornblith:
Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability. CoRR abs/2408.07852 (2024) - 2023
- [j11]Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zachary Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan:
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. J. Mach. Learn. Res. 24: 42:1-42:63 (2023) - [i35]Ben Adlam, Jaehoon Lee, Shreyas Padhy, Zachary Nado, Jasper Snoek:
Kernel Regression with Infinite-Width Neural Networks on Millions of Examples. CoRR abs/2303.05420 (2023) - [i34]Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron Parisi, Abhishek Kumar, Alex Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin F. Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel:
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. CoRR abs/2312.06585 (2023) - 2022
- [j10]James Urquhart Allingham, Florian Wenzel, Zelda E. Mariet, Basil Mustafa, Joan Puigcerver, Neil Houlsby, Ghassen Jerfel, Vincent Fortuin
, Balaji Lakshminarayanan, Jasper Snoek, Dustin Tran, Carlos Riquelme Ruiz, Rodolphe Jenatton:
Sparse MoEs meet Efficient Ensembles. Trans. Mach. Learn. Res. 2022 (2022) - [j9]Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljacic:
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure. Trans. Mach. Learn. Res. 2022 (2022) - [c36]Setareh Ariafar, Justin Gilmer, Zachary Nado, Jasper Snoek, Rodolphe Jenatton, George E. Dahl:
Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach. AISTATS 2022: 11056-11071 - [i33]Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zack Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan:
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. CoRR abs/2205.00403 (2022) - [i32]Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani:
Pre-training helps Bayesian optimization too. CoRR abs/2207.03084 (2022) - [i31]Dustin Tran, Jeremiah Z. Liu, Michael W. Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda Mariet, Huiyi Hu, Neil Band, Tim G. J. Rudner, Karan Singhal, Zachary Nado, Joost van Amersfoort, Andreas Kirsch, Rodolphe Jenatton, Nithum Thain, Honglin Yuan, Kelly Buchanan, Kevin Murphy, D. Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, Balaji Lakshminarayanan:
Plex: Towards Reliability using Pretrained Large Model Extensions. CoRR abs/2207.07411 (2022) - 2021
- [j8]Marton Havasi, Jasper Snoek, Dustin Tran, Jonathan Gordon, José Miguel Hernández-Lobato:
Sampling the Variational Posterior with Local Refinement. Entropy 23(11): 1475 (2021) - [j7]Benjamin Kompa, Jasper Snoek, Andrew L. Beam:
Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures. Entropy 23(12): 1608 (2021) - [j6]Benjamin Kompa, Jasper Snoek, Andrew L. Beam:
Second opinion needed: communicating uncertainty in medical machine learning. npj Digit. Medicine 4 (2021) - [c35]Setareh Ariafar, Zelda Mariet, Dana H. Brooks, Jennifer G. Dy, Jasper Snoek:
Faster & More Reliable Tuning of Neural Networks: Bayesian Optimization with Importance Sampling. AISTATS 2021: 3961-3969 - [c34]Ben Adlam, Jaehoon Lee, Lechao Xiao, Jeffrey Pennington, Jasper Snoek:
Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit. ICLR 2021 - [c33]Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew Mingbo Dai, Dustin Tran:
Training independent subnetworks for robust prediction. ICLR 2021 - [c32]Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W. Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran:
Combining Ensembles and Data Augmentation Can Harm Your Calibration. ICLR 2021 - [i30]Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljacic:
Scalable and Flexible Deep Bayesian Optimization with Auxiliary Information for Scientific Problems. CoRR abs/2104.11667 (2021) - [i29]Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Z. Liu, Zelda Mariet, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim G. J. Rudner, Yeming Wen, Florian Wenzel, Kevin Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, Dustin Tran:
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. CoRR abs/2106.04015 (2021) - [i28]Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani:
Automatic prior selection for meta Bayesian optimization with a case study on tuning deep neural network optimizers. CoRR abs/2109.08215 (2021) - [i27]James Urquhart Allingham, Florian Wenzel, Zelda E. Mariet, Basil Mustafa, Joan Puigcerver, Neil Houlsby, Ghassen Jerfel, Vincent Fortuin, Balaji Lakshminarayanan, Jasper Snoek, Dustin Tran, Carlos Riquelme Ruiz, Rodolphe Jenatton:
Sparse MoEs meet Efficient Ensembles. CoRR abs/2110.03360 (2021) - [i26]Setareh Ariafar, Justin Gilmer, Zachary Nado, Jasper Snoek, Rodolphe Jenatton, George E. Dahl:
Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach. CoRR abs/2112.08250 (2021) - 2020
- [c31]Michael Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine A. Heller, Balaji Lakshminarayanan, Dustin Tran:
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors. ICML 2020: 2782-2792 - [c30]Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling, Linh Tran, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin:
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks. ICML 2020: 9289-9299 - [c29]Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin:
How Good is the Bayes Posterior in Deep Neural Networks Really? ICML 2020: 10248-10259 - [c28]Alexey A. Gritsenko, Tim Salimans, Rianne van den Berg, Jasper Snoek, Nal Kalchbrenner:
A Spectral Energy Distance for Parallel Speech Synthesis. NeurIPS 2020 - [c27]Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton:
Hyperparameter Ensembles for Robustness and Uncertainty Quantification. NeurIPS 2020 - [i25]Linh Tran, Bastiaan S. Veeling, Kevin Roth, Jakub Swiatkowski, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Sebastian Nowozin, Rodolphe Jenatton:
Hydra: Preserving Ensemble Diversity for Model Distillation. CoRR abs/2001.04694 (2020) - [i24]Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin:
How Good is the Bayes Posterior in Deep Neural Networks Really? CoRR abs/2002.02405 (2020) - [i23]Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling, Linh Tran, Joshua V. Dillon, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin:
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks. CoRR abs/2002.02655 (2020) - [i22]Setareh Ariafar, Zelda Mariet, Ehsan Elhamifar, Dana H. Brooks, Jennifer G. Dy, Jasper Snoek:
Weighting Is Worth the Wait: Bayesian Optimization with Importance Sampling. CoRR abs/2002.09927 (2020) - [i21]Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-An Ma, Jasper Snoek, Katherine A. Heller, Balaji Lakshminarayanan, Dustin Tran:
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors. CoRR abs/2005.07186 (2020) - [i20]Zachary Nado, Shreyas Padhy, D. Sculley, Alexander D'Amour, Balaji Lakshminarayanan, Jasper Snoek:
Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift. CoRR abs/2006.10963 (2020) - [i19]Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton:
Hyperparameter Ensembles for Robustness and Uncertainty Quantification. CoRR abs/2006.13570 (2020) - [i18]Shreyas Padhy, Zachary Nado, Jie Ren, Jeremiah Z. Liu, Jasper Snoek, Balaji Lakshminarayanan:
Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks. CoRR abs/2007.05134 (2020) - [i17]Ben Adlam, Jasper Snoek, Samuel L. Smith:
Cold Posteriors and Aleatoric Uncertainty. CoRR abs/2008.00029 (2020) - [i16]Alexey A. Gritsenko, Tim Salimans, Rianne van den Berg, Jasper Snoek, Nal Kalchbrenner:
A Spectral Energy Distance for Parallel Speech Synthesis. CoRR abs/2008.01160 (2020) - [i15]Benjamin Kompa, Jasper Snoek, Andrew Beam:
Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures. CoRR abs/2010.03039 (2020) - [i14]Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew M. Dai, Dustin Tran:
Training independent subnetworks for robust prediction. CoRR abs/2010.06610 (2020) - [i13]Ben Adlam, Jaehoon Lee, Lechao Xiao, Jeffrey Pennington, Jasper Snoek:
Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit. CoRR abs/2010.07355 (2020) - [i12]Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W. Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran:
Combining Ensembles and Data Augmentation can Harm your Calibration. CoRR abs/2010.09875 (2020)
2010 – 2019
- 2019
- [c26]Alexey A. Gritsenko, Jasper Snoek, Tim Salimans:
On the relationship between Normalising Flows and Variational- and Denoising Autoencoders. DGS@ICLR 2019 - [c25]Zelda E. Mariet, Yaniv Ovadia, Jasper Snoek:
DppNet: Approximating Determinantal Point Processes with Deep Networks. NeurIPS 2019: 3218-3229 - [c24]Jasper Snoek, Yaniv Ovadia, Emily Fertig, Balaji Lakshminarayanan, Sebastian Nowozin, D. Sculley, Joshua V. Dillon, Jie Ren, Zachary Nado:
Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift. NeurIPS 2019: 13969-13980 - [c23]Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan:
Likelihood Ratios for Out-of-Distribution Detection. NeurIPS 2019: 14680-14691 - [i11]Zelda Mariet, Yaniv Ovadia, Jasper Snoek:
DPPNet: Approximating Determinantal Point Processes with Deep Networks. CoRR abs/1901.02051 (2019) - [i10]D. Sculley, Jasper Snoek, Alexander B. Wiltschko:
Avoiding a Tragedy of the Commons in the Peer Review Process. CoRR abs/1901.06246 (2019) - [i9]Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua V. Dillon, Balaji Lakshminarayanan, Jasper Snoek:
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift. CoRR abs/1906.02530 (2019) - [i8]Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan:
Likelihood Ratios for Out-of-Distribution Detection. CoRR abs/1906.02845 (2019) - 2018
- [c22]Gonzalo E. Mena, David Belanger, Scott W. Linderman, Jasper Snoek:
Learning Latent Permutations with Gumbel-Sinkhorn Networks. ICLR (Poster) 2018 - [c21]Zachary Nado, Jasper Snoek, Roger B. Grosse, David Duvenaud, Bowen Xu, James Martens:
Stochastic Gradient Langevin dynamics that Exploit Neural Network Structure. ICLR (Workshop) 2018 - [c20]Carlos Riquelme, George Tucker, Jasper Snoek:
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling. ICLR (Poster) 2018 - [c19]D. Sculley, Jasper Snoek, Alexander B. Wiltschko, Ali Rahimi:
Winner's Curse? On Pace, Progress, and Empirical Rigor. ICLR (Workshop) 2018 - [i7]Gonzalo E. Mena, David Belanger, Scott W. Linderman, Jasper Snoek:
Learning Latent Permutations with Gumbel-Sinkhorn Networks. CoRR abs/1802.08665 (2018) - [i6]Carlos Riquelme, George Tucker, Jasper Snoek:
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling. CoRR abs/1802.09127 (2018) - 2017
- [j5]Ahmad Akl, Jasper Snoek, Alex Mihailidis:
Unobtrusive Detection of Mild Cognitive Impairment in Older Adults Through Home Monitoring. IEEE J. Biomed. Health Informatics 21(2): 339-348 (2017) - 2015
- [c18]Li-Wei H. Lehman, Mohammad M. Ghassemi, Jasper Snoek, Shamim Nemati:
Patient Prognosis from Vital Sign Time Series: Combining Convolutional Neural Networks with a Dynamical Systems Approach. CinC 2015: 1069-1072 - [c17]Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Md. Mostofa Ali Patwary, Prabhat, Ryan P. Adams:
Scalable Bayesian Optimization Using Deep Neural Networks. ICML 2015: 2171-2180 - [c16]Oren Rippel, Jasper Snoek, Ryan P. Adams:
Spectral Representations for Convolutional Neural Networks. NIPS 2015: 2449-2457 - [i5]Oren Rippel, Jasper Snoek, Ryan P. Adams:
Spectral Representations for Convolutional Neural Networks. CoRR abs/1506.03767 (2015) - 2014
- [b1]Jasper Snoek:
Bayesian Optimization and Semiparametric Models with Applications to Assistive Technology. University of Toronto, Canada, 2014 - [j4]Babak Taati, Jasper Snoek, Dionne M. Aleman, Ardeshir Ghavamzadeh:
Data Mining in Bone Marrow Transplant Records to Identify Patients With High Odds of Survival. IEEE J. Biomed. Health Informatics 18(1): 21-27 (2014) - [c15]Mohammad M. Ghassemi, Li-Wei H. Lehman, Jasper Snoek, Shamim Nemati:
Global Optimization Approaches for Parameter Tuning in Biomedical Signal Processing: A Focus of Multi-scale Entropy. CinC 2014: 993-996 - [c14]Ahmad Akl, Jasper Snoek, Alex Mihailidis:
Generalized Linear Models of home activity for automatic detection of mild cognitive impairment in older adults. EMBC 2014: 680-683 - [c13]Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams:
Input Warping for Bayesian Optimization of Non-Stationary Functions. ICML 2014: 1674-1682 - [c12]Michael A. Gelbart, Jasper Snoek, Ryan P. Adams:
Bayesian Optimization with Unknown Constraints. UAI 2014: 250-259 - [i4]Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams:
Input Warping for Bayesian Optimization of Non-stationary Functions. CoRR abs/1402.0929 (2014) - [i3]Michael A. Gelbart, Jasper Snoek, Ryan P. Adams:
Bayesian Optimization with Unknown Constraints. CoRR abs/1403.5607 (2014) - [i2]Kevin Swersky, Jasper Snoek, Ryan Prescott Adams:
Freeze-Thaw Bayesian Optimization. CoRR abs/1406.3896 (2014) - 2013
- [j3]Babak Taati, Jasper Snoek, Alex Mihailidis:
Video analysis for identifying human operation difficulties and faucet usability assessment. Neurocomputing 100: 163-169 (2013) - [c11]Jasper Snoek, Richard S. Zemel, Ryan Prescott Adams:
A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data. NIPS 2013: 1932-1940 - [c10]Kevin Swersky, Jasper Snoek, Ryan Prescott Adams:
Multi-Task Bayesian Optimization. NIPS 2013: 2004-2012 - 2012
- [j2]Jasper Snoek, Ryan P. Adams, Hugo Larochelle:
Nonparametric guidance of autoencoder representations using label information. J. Mach. Learn. Res. 13: 2567-2588 (2012) - [c9]Jasper Snoek, Babak Taati, Alex Mihailidis:
An Automated Machine Learning Approach Applied to Robotic Stroke Rehabilitation. AAAI Fall Symposium: Artificial Intelligence for Gerontechnology 2012 - [c8]Jasper Snoek, Hugo Larochelle, Ryan P. Adams:
Practical Bayesian Optimization of Machine Learning Algorithms. NIPS 2012: 2960-2968 - [c7]Jasper Snoek, Ryan Prescott Adams, Hugo Larochelle:
On Nonparametric Guidance for Learning Autoencoder Representations. AISTATS 2012: 1073-1080 - [i1]Jasper Snoek, Hugo Larochelle, Ryan Prescott Adams:
Practical Bayesian Optimization of Machine Learning Algorithms. CoRR abs/1206.2944 (2012) - 2011
- [c6]Michael Belshaw, Babak Taati, Jasper Snoek, Alex Mihailidis:
Towards a single sensor passive solution for automated fall detection. EMBC 2011: 1773-1776 - [c5]Babak Taati, Jasper Snoek, Alex Mihailidis:
Towards Aging-in-Place: Automatic Assessment of Product Usability for Older Adults with Dementia. HISB 2011: 205-212 - [c4]Jasper Snoek, Luciano Sbaiz, Hrishikesh B. Aradhye:
From Videos to Places: Geolocating the World's Videos. ICDM Workshops 2011: 823-832 - 2010
- [c3]Babak Taati, Jasper Snoek, David Giesbrecht, Alex Mihailidis:
Water Flow Detection in a Handwashing Task. CRV 2010: 175-182 - [c2]Jasper Snoek, Babak Taati, Yulia Eskin, Alex Mihailidis:
Automatic segmentation of video to aid the study of faucet usability for older adults. CVPR Workshops 2010: 63-70
2000 – 2009
- 2009
- [j1]Jasper Snoek, Jesse Hoey, Liam Stewart, Richard S. Zemel, Alex Mihailidis:
Automated detection of unusual events on stairs. Image Vis. Comput. 27(1-2): 153-166 (2009) - 2006
- [c1]Jasper Snoek, Jesse Hoey, Liam Stewart, Richard S. Zemel:
Automated Detection of Unusual Events on Stairs. CRV 2006: 5
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 2024-09-25 00:43 CEST 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:
