Mark Sandler

According to our database, Mark Sandler authored at least 39 papers between 2003 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Linear Transformers are Versatile In-Context Learners.

Max Vladymyrov, Johannes von Oswald, Mark Sandler, Rong Ge

CoRR, 2024

2023

Uncovering mesa-optimization algorithms in Transformers.

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

CoRR, 2023

Continual Few-Shot Learning Using HyperTransformers.

Max Vladymyrov, Andrey Zhmoginov, Mark Sandler

CoRR, 2023

Training trajectories, mini-batch losses and the curious role of the learning rate.

Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Nolan Miller

CoRR, 2023

Decentralized Learning with Multi-Headed Distillation.

Andrey Zhmoginov, Mark Sandler, Nolan Miller, Gus Kristiansen, Max Vladymyrov

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning.

Andrey Zhmoginov, Mark Sandler, Maksym Vladymyrov

Proceedings of the International Conference on Machine Learning, 2022

Fine-tuning Image Transformers using Learnable Memory.

Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Andrew Jackson

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

Compositional Models: Multi-Task Learning and Knowledge Transfer with Modular Networks.

Andrey Zhmoginov, Dina Bashkirova, Mark Sandler

CoRR, 2021

Meta-Learning Bidirectional Update Rules.

Mark Sandler, Max Vladymyrov, Andrey Zhmoginov, Nolan Miller, Tom Madams, Andrew Jackson, Blaise Agüera y Arcas

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Large-Scale Generative Data-Free Distillation.

Liangchen Luo, Mark Sandler, Zi Lin, Andrey Zhmoginov, Andrew Howard

CoRR, 2020

Image segmentation via Cellular Automata.

Mark Sandler, Andrey Zhmoginov, Liangcheng Luo, Alexander Mordvintsev, Ettore Randazzo, Blaise Agüera y Arcas

CoRR, 2020

Information-Bottleneck Approach to Salient Region Discovery.

Andrey Zhmoginov, Ian Fischer, Mark Sandler

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

Structured Multi-Hashing for Model Compression.

Elad Eban, Yair Movshovitz-Attias, Hao Wu, Mark Sandler, Andrew Poon, Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection.

Keren Ye, Adriana Kovashka, Mark Sandler, Menglong Zhu, Andrew G. Howard, Marco Fornoni

Proceedings of the Computer Vision - ACCV 2020 - 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30, 2020

2019

K for the Price of 1: Parameter-efficient Multi-task and Transfer Learning.

Pramod Kaushik Mudrakarta, Mark Sandler, Andrey Zhmoginov, Andrew G. Howard

Proceedings of the 7th International Conference on Learning Representations, 2019

Non-Discriminative Data or Weak Model? On the Relative Importance of Data and Model Resolution.

Mark Sandler, Jonathan Baccash, Andrey Zhmoginov, Andrew Howard

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision Workshops, 2019

Searching for MobileNetV3.

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

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

MnasNet: Platform-Aware Neural Architecture Search for Mobile.

Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, Quoc V. Le

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation.

Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen

CoRR, 2018

NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications.

Tien-Ju Yang, Andrew G. Howard, Bo Chen, Xiao Zhang, Alec Go, Mark Sandler, Vivienne Sze, Hartwig Adam

Proceedings of the Computer Vision - ECCV 2018, 2018

MobileNetV2: Inverted Residuals and Linear Bottlenecks.

Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

2017

CycleGAN, a Master of Steganography.

Casey Chu, Andrey Zhmoginov, Mark Sandler

CoRR, 2017

The Power of Sparsity in Convolutional Neural Networks.

Soravit Changpinyo, Mark Sandler, Andrey Zhmoginov

CoRR, 2017

2016

Inverting face embeddings with convolutional neural networks.

Andrey Zhmoginov, Mark Sandler

CoRR, 2016

2014

Frugal Streaming for Estimating Quantiles: One (or two) memory suffices.

Qiang Ma, S. Muthukrishnan, Mark Sandler

CoRR, 2014

2013

Understanding latency variations of black box services.

Darja Krushevskaja, Mark Sandler

Proceedings of the 22nd International World Wide Web Conference, 2013

Frugal Streaming for Estimating Quantiles.

Qiang Ma, S. Muthukrishnan, Mark Sandler

Proceedings of the Space-Efficient Data Structures, 2013

2011

Telling Two Distributions Apart: a Tight Characterization

Eyal Even-Dar, Mark Sandler

CoRR, 2011

Modeling the Parallel Execution of Black-Box Services.

Gideon Mann, Mark Sandler, Darja Krushevskaja, Sudipto Guha, Eyal Even-Dar

Proceedings of the 3rd USENIX Workshop on Hot Topics in Cloud Computing, 2011

2010

Monitoring algorithms for negative feedback systems.

Mark Sandler, S. Muthukrishnan

Proceedings of the 19th International Conference on World Wide Web, 2010

2008

Theory research at Google.

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

SIGACT News, 2008

Network Failure Detection and Graph Connectivity.

Jon M. Kleinberg, Mark Sandler, Aleksandrs Slivkins

SIAM J. Comput., 2008

Using mixture models for collaborative filtering.

Jon M. Kleinberg, Mark Sandler

J. Comput. Syst. Sci., 2008

2007

Hierarchical mixture models: a probabilistic analysis.

Mark Sandler

Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007

2006

Algorithms for Mixture Models.

Mark Sandler

PhD thesis, 2006

Privacy via pseudorandom sketches.

Nina Mishra, Mark Sandler

Proceedings of the Twenty-Fifth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, 2006

2005

On the use of linear programming for unsupervised text classification.

Mark Sandler

Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2005

On Learning Mixtures of Heavy-Tailed Distributions.

Anirban Dasgupta, John E. Hopcroft, Jon M. Kleinberg, Mark Sandler

Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2005), 2005

2003

Convergent algorithms for collaborative filtering.

Jon M. Kleinberg, Mark Sandler

Proceedings of the Proceedings 4th ACM Conference on Electronic Commerce (EC-2003), 2003