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Mark Sandler 0002
Person information
- affiliation: Google
- affiliation: Cornell University, Ithaca, NY, USA
Other persons with the same name
- Mark B. Sandler
(aka: Mark Sandler 0001) — Queen Mary University of London, UK
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2020 – today
- 2024
- [j4]Max Vladymyrov, Andrey Zhmoginov, Mark Sandler:
Continual HyperTransformer: A Meta-Learner for Continual Few-Shot Learning. Trans. Mach. Learn. Res. 2024 (2024) - [i24]Max Vladymyrov, Johannes von Oswald, Mark Sandler, Rong Ge:
Linear Transformers are Versatile In-Context Learners. CoRR abs/2402.14180 (2024) - [i23]Gus Kristiansen, Mark Sandler, Andrey Zhmoginov, Nolan Miller, Anirudh Goyal, Jihwan Lee, Max Vladymyrov:
Narrowing the Focus: Learned Optimizers for Pretrained Models. CoRR abs/2408.09310 (2024) - 2023
- [c25]Andrey Zhmoginov, Mark Sandler, Nolan Miller, Gus Kristiansen, Max Vladymyrov:
Decentralized Learning with Multi-Headed Distillation. CVPR 2023: 8053-8063 - [i22]Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Nolan Miller:
Training trajectories, mini-batch losses and the curious role of the learning rate. CoRR abs/2301.02312 (2023) - [i21]Max Vladymyrov, Andrey Zhmoginov, Mark Sandler:
Continual Few-Shot Learning Using HyperTransformers. CoRR abs/2301.04584 (2023) - [i20]Johannes von Oswald, Eyvind Niklasson, Maximilian Schlegel, Seijin Kobayashi, Nicolas Zucchet, Nino Scherrer, Nolan Miller, Mark Sandler, Blaise Agüera y Arcas, Max Vladymyrov, Razvan Pascanu, João Sacramento:
Uncovering mesa-optimization algorithms in Transformers. CoRR abs/2309.05858 (2023) - 2022
- [c24]Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Andrew Jackson:
Fine-tuning Image Transformers using Learnable Memory. CVPR 2022: 12145-12154 - [c23]Andrey Zhmoginov, Mark Sandler, Maksym Vladymyrov:
HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning. ICML 2022: 27075-27098 - [i19]Andrey Zhmoginov, Mark Sandler, Max Vladymyrov:
HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning. CoRR abs/2201.04182 (2022) - [i18]Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Andrew Jackson:
Fine-tuning Image Transformers using Learnable Memory. CoRR abs/2203.15243 (2022) - [i17]Andrey Zhmoginov, Mark Sandler, Nolan Miller, Gus Kristiansen, Max Vladymyrov:
Decentralized Learning with Multi-Headed Distillation. CoRR abs/2211.15774 (2022) - 2021
- [c22]Mark Sandler, Max Vladymyrov, Andrey Zhmoginov, Nolan Miller, Tom Madams, Andrew Jackson, Blaise Agüera y Arcas:
Meta-Learning Bidirectional Update Rules. ICML 2021: 9288-9300 - [i16]Keren Ye, Adriana Kovashka, Mark Sandler, Menglong Zhu, Andrew Howard, Marco Fornoni:
SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection. CoRR abs/2101.01260 (2021) - [i15]Mark Sandler, Max Vladymyrov, Andrey Zhmoginov, Nolan Miller, Andrew Jackson, Tom Madams, Blaise Agüera y Arcas:
Meta-Learning Bidirectional Update Rules. CoRR abs/2104.04657 (2021) - [i14]Andrey Zhmoginov, Dina Bashkirova, Mark Sandler:
Compositional Models: Multi-Task Learning and Knowledge Transfer with Modular Networks. CoRR abs/2107.10963 (2021) - 2020
- [c21]Keren Ye
SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection. ACCV (6) 2020: 239-256 - [c20]Elad Eban, Yair Movshovitz-Attias, Hao Wu, Mark Sandler, Andrew Poon, Yerlan Idelbayev
Structured Multi-Hashing for Model Compression. CVPR 2020: 11900-11909 - [c19]Keren Ye
SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection. ECCV Workshops (1) 2020: 636-640 - [c18]Andrey Zhmoginov, Ian Fischer, Mark Sandler:
Information-Bottleneck Approach to Salient Region Discovery. ECML/PKDD (3) 2020: 531-546 - [i13]Mark Sandler, Andrey Zhmoginov, Liangcheng Luo, Alexander Mordvintsev, Ettore Randazzo, Blaise Agüera y Arcas:
Image segmentation via Cellular Automata. CoRR abs/2008.04965 (2020) - [i12]Liangchen Luo, Mark Sandler, Zi Lin, Andrey Zhmoginov, Andrew Howard:
Large-Scale Generative Data-Free Distillation. CoRR abs/2012.05578 (2020)
2010 – 2019
- 2019
- [c17]Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, Quoc V. Le:
MnasNet: Platform-Aware Neural Architecture Search for Mobile. CVPR 2019: 2820-2828 - [c16]Andrew Howard, Ruoming Pang, Hartwig Adam, Quoc V. Le, Mark Sandler, Bo Chen, Weijun Wang, Liang-Chieh Chen, Mingxing Tan, Grace Chu, Vijay Vasudevan, Yukun Zhu:
Searching for MobileNetV3. ICCV 2019: 1314-1324 - [c15]Mark Sandler, Jonathan Baccash, Andrey Zhmoginov, Andrew Howard:
Non-Discriminative Data or Weak Model? On the Relative Importance of Data and Model Resolution. ICCV Workshops 2019: 1036-1044 - [c14]Pramod Kaushik Mudrakarta, Mark Sandler, Andrey Zhmoginov, Andrew G. Howard:
K for the Price of 1: Parameter-efficient Multi-task and Transfer Learning. ICLR (Poster) 2019 - [i11]Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, Hartwig Adam:
Searching for MobileNetV3. CoRR abs/1905.02244 (2019) - [i10]Andrey Zhmoginov, Ian Fischer, Mark Sandler:
Information-Bottleneck Approach to Salient Region Discovery. CoRR abs/1907.09578 (2019) - [i9]Mark Sandler, Jonathan Baccash, Andrey Zhmoginov, Andrew Howard:
Non-discriminative data or weak model? On the relative importance of data and model resolution. CoRR abs/1909.03205 (2019) - [i8]Elad Eban, Yair Movshovitz-Attias, Hao Wu, Mark Sandler, Andrew Poon, Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán:
Structured Multi-Hashing for Model Compression. CoRR abs/1911.11177 (2019) - 2018
- [c13]Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen:
MobileNetV2: Inverted Residuals and Linear Bottlenecks. CVPR 2018: 4510-4520 - [c12]Tien-Ju Yang
NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications. ECCV (10) 2018: 289-304 - [i7]Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen:
Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation. CoRR abs/1801.04381 (2018) - [i6]Pramod Kaushik Mudrakarta, Mark Sandler, Andrey Zhmoginov, Andrew G. Howard:
K For The Price Of 1: Parameter Efficient Multi-task And Transfer Learning. CoRR abs/1810.10703 (2018) - 2017
- [i5]Soravit Changpinyo, Mark Sandler, Andrey Zhmoginov:
The Power of Sparsity in Convolutional Neural Networks. CoRR abs/1702.06257 (2017) - [i4]Casey Chu, Andrey Zhmoginov, Mark Sandler:
CycleGAN, a Master of Steganography. CoRR abs/1712.02950 (2017) - 2016
- [i3]Andrey Zhmoginov, Mark Sandler:
Inverting face embeddings with convolutional neural networks. CoRR abs/1606.04189 (2016) - 2014
- [i2]Qiang Ma, S. Muthukrishnan, Mark Sandler:
Frugal Streaming for Estimating Quantiles: One (or two) memory suffices. CoRR abs/1407.1121 (2014) - 2013
- [c11]Qiang Ma, S. Muthukrishnan, Mark Sandler:
Frugal Streaming for Estimating Quantiles. Space-Efficient Data Structures, Streams, and Algorithms 2013: 77-96 - [c10]Darja Krushevskaja, Mark Sandler:
Understanding latency variations of black box services. WWW 2013: 703-714 - 2011
- [c9]Gideon Mann, Mark Sandler, Darja Krushevskaja, Sudipto Guha, Eyal Even-Dar:
Modeling the Parallel Execution of Black-Box Services. HotCloud 2011 - [i1]Eyal Even-Dar, Mark Sandler:
Telling Two Distributions Apart: a Tight Characterization. CoRR abs/1110.3100 (2011) - 2010
- [c8]Mark Sandler, S. Muthukrishnan:
Monitoring algorithms for negative feedback systems. WWW 2010: 871-880
2000 – 2009
- 2008
- [j3]Jon M. Kleinberg, Mark Sandler:
Using mixture models for collaborative filtering. J. Comput. Syst. Sci. 74(1): 49-69 (2008) - [j2]Jon M. Kleinberg, Mark Sandler, Aleksandrs Slivkins:
Network Failure Detection and Graph Connectivity. SIAM J. Comput. 38(4): 1330-1346 (2008) - [j1]Gagan Aggarwal, Nir Ailon, Florin Constantin, Eyal Even-Dar, Jon Feldman, Gereon Frahling, Monika Rauch Henzinger, S. Muthukrishnan, Noam Nisan, Martin Pál, Mark Sandler, Anastasios Sidiropoulos:
Theory research at Google. SIGACT News 39(2): 10-28 (2008) - 2007
- [c7]Mark Sandler:
Hierarchical mixture models: a probabilistic analysis. KDD 2007: 580-589 - 2006
- [b1]Mark Sandler:
Algorithms for Mixture Models. Cornell University, USA, 2006 - [c6]Nina Mishra, Mark Sandler:
Privacy via pseudorandom sketches. PODS 2006: 143-152 - 2005
- [c5]Anirban Dasgupta
On Learning Mixtures of Heavy-Tailed Distributions. FOCS 2005: 491-500 - [c4]Mark Sandler:
On the use of linear programming for unsupervised text classification. KDD 2005: 256-264 - 2004
- [c3]Jon M. Kleinberg, Mark Sandler, Aleksandrs Slivkins:
Network failure detection and graph connectivity. SODA 2004: 76-85 - [c2]Jon M. Kleinberg, Mark Sandler:
Using mixture models for collaborative filtering. STOC 2004: 569-578 - 2003
- [c1]Jon M. Kleinberg, Mark Sandler:
Convergent algorithms for collaborative filtering. EC 2003: 1-10
Coauthor Index
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last updated on 2024-10-21 20:32 CEST by the dblp team
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