Mark Sandler

Publication Activity (10 Years)

Years Active: 2003-2024
Publications (10 Years): 36

Top Topics

Transfer Learning

Delta Bar Delta

Convolutional Neural Networks

Top Venues

ECCV Workshops (1)

Publications

Max Vladymyrov, Andrey Zhmoginov, Mark Sandler
Continual HyperTransformer: A Meta-Learner for Continual Few-Shot Learning. Trans. Mach. Learn. Res. 2024 (2024)
Max Vladymyrov, Johannes von Oswald, Mark Sandler, Rong Ge
Linear Transformers are Versatile In-Context Learners. CoRR (2024)
Andrey Zhmoginov, Mark Sandler, Nolan Miller, Gus Kristiansen, Max Vladymyrov
Decentralized Learning with Multi-Headed Distillation. CVPR (2023)
Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Nolan Miller
Training trajectories, mini-batch losses and the curious role of the learning rate. CoRR (2023)
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 (2023)
Max Vladymyrov, Andrey Zhmoginov, Mark Sandler
Continual Few-Shot Learning Using HyperTransformers. CoRR (2023)
Andrey Zhmoginov, Mark Sandler, Nolan Miller, Gus Kristiansen, Max Vladymyrov
Decentralized Learning with Multi-Headed Distillation. CoRR (2022)
Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Andrew Jackson
Fine-tuning Image Transformers using Learnable Memory. CoRR (2022)
Andrey Zhmoginov, Mark Sandler, Maksym Vladymyrov
HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning. ICML (2022)
Andrey Zhmoginov, Mark Sandler, Max Vladymyrov
HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning. CoRR (2022)
Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Andrew Jackson
Fine-tuning Image Transformers using Learnable Memory. CVPR (2022)
Mark Sandler, Max Vladymyrov, Andrey Zhmoginov, Nolan Miller, Tom Madams, Andrew Jackson, Blaise Agüera y Arcas
Meta-Learning Bidirectional Update Rules. ICML (2021)
Keren Ye, Adriana Kovashka, Mark Sandler, Menglong Zhu, Andrew Howard, Marco Fornoni
SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection. CoRR (2021)
Mark Sandler, Max Vladymyrov, Andrey Zhmoginov, Nolan Miller, Andrew Jackson, Tom Madams, Blaise Agüera y Arcas
Meta-Learning Bidirectional Update Rules. CoRR (2021)
Andrey Zhmoginov, Dina Bashkirova, Mark Sandler
Compositional Models: Multi-Task Learning and Knowledge Transfer with Modular Networks. CoRR (2021)
Andrey Zhmoginov, Ian Fischer, Mark Sandler
Information-Bottleneck Approach to Salient Region Discovery. ECML/PKDD (3) (2020)
Keren Ye, Adriana Kovashka, Mark Sandler, Menglong Zhu, Andrew G. Howard, Marco Fornoni
SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection. ECCV Workshops (1) (2020)
Elad Eban, Yair Movshovitz-Attias, Hao Wu, Mark Sandler, Andrew Poon, Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán
Structured Multi-Hashing for Model Compression. CVPR (2020)
Mark Sandler, Andrey Zhmoginov, Liangcheng Luo, Alexander Mordvintsev, Ettore Randazzo, Blaise Agüera y Arcas
Image segmentation via Cellular Automata. CoRR (2020)
Keren Ye, Adriana Kovashka, Mark Sandler, Menglong Zhu, Andrew G. Howard, Marco Fornoni
SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection. ACCV (6) (2020)
Liangchen Luo, Mark Sandler, Zi Lin, Andrey Zhmoginov, Andrew Howard
Large-Scale Generative Data-Free Distillation. CoRR (2020)
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 (2019)
Mark Sandler, Jonathan Baccash, Andrey Zhmoginov, Andrew Howard
Non-discriminative data or weak model? On the relative importance of data and model resolution. CoRR (2019)
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)
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)
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)
Andrey Zhmoginov, Ian Fischer, Mark Sandler
Information-Bottleneck Approach to Salient Region Discovery. CoRR (2019)
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)
Elad Eban, Yair Movshovitz-Attias, Hao Wu, Mark Sandler, Andrew Poon, Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán
Structured Multi-Hashing for Model Compression. CoRR (2019)
Tien-Ju Yang, Andrew G. Howard, Bo Chen, Xiao Zhang, Alec Go, Mark Sandler, Vivienne Sze, Hartwig Adam
NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications. ECCV (10) (2018)
Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen
MobileNetV2: Inverted Residuals and Linear Bottlenecks. CVPR (2018)
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 (2018)
Pramod Kaushik Mudrakarta, Mark Sandler, Andrey Zhmoginov, Andrew G. Howard
K For The Price Of 1: Parameter Efficient Multi-task And Transfer Learning. CoRR (2018)
Casey Chu, Andrey Zhmoginov, Mark Sandler
CycleGAN, a Master of Steganography. CoRR (2017)
Soravit Changpinyo, Mark Sandler, Andrey Zhmoginov
The Power of Sparsity in Convolutional Neural Networks. CoRR (2017)
Andrey Zhmoginov, Mark Sandler
Inverting face embeddings with convolutional neural networks. CoRR (2016)
Qiang Ma, S. Muthukrishnan, Mark Sandler
Frugal Streaming for Estimating Quantiles: One (or two) memory suffices. CoRR (2014)
Darja Krushevskaja, Mark Sandler
Understanding latency variations of black box services. WWW (2013)
Qiang Ma, S. Muthukrishnan, Mark Sandler
Frugal Streaming for Estimating Quantiles. Space-Efficient Data Structures, Streams, and Algorithms (2013)
Gideon Mann, Mark Sandler, Darja Krushevskaja, Sudipto Guha, Eyal Even-Dar
Modeling the Parallel Execution of Black-Box Services. HotCloud (2011)
Eyal Even-Dar, Mark Sandler
Telling Two Distributions Apart: a Tight Characterization CoRR (2011)
Mark Sandler, S. Muthukrishnan
Monitoring algorithms for negative feedback systems. WWW (2010)
Jon M. Kleinberg, Mark Sandler
Using mixture models for collaborative filtering. J. Comput. Syst. Sci. 74 (1) (2008)
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) (2008)
Jon M. Kleinberg, Mark Sandler, Aleksandrs Slivkins
Network Failure Detection and Graph Connectivity. SIAM J. Comput. 38 (4) (2008)
Mark Sandler
Hierarchical mixture models: a probabilistic analysis. KDD (2007)
Nina Mishra, Mark Sandler
Privacy via pseudorandom sketches. PODS (2006)
Mark Sandler
On the use of linear programming for unsupervised text classification. KDD (2005)
Anirban Dasgupta, John E. Hopcroft, Jon M. Kleinberg, Mark Sandler
On Learning Mixtures of Heavy-Tailed Distributions. FOCS (2005)
Jon M. Kleinberg, Mark Sandler
Using mixture models for collaborative filtering. STOC (2004)
Jon M. Kleinberg, Mark Sandler, Aleksandrs Slivkins
Network failure detection and graph connectivity. SODA (2004)
Jon M. Kleinberg, Mark Sandler
Convergent algorithms for collaborative filtering. EC (2003)