Sandler, Mark

24 publications

ICLR 2026 Robust Training of Neural Networks at Arbitrary Precision and Sparsity Chengxi Ye, Grace Chu, Yanfeng Liu, Yichi Zhang, Lukasz Lew, Li Zhang, Mark Sandler, Andrew G. Howard

ICLR 2025 How New Data Permeates LLM Knowledge and How to Dilute It Chen Sun, Renat Aksitov, Andrey Zhmoginov, Nolan Andrew Miller, Max Vladymyrov, Ulrich Rueckert, Been Kim, Mark Sandler

TMLR 2024 Continual HyperTransformer: A Meta-Learner for Continual Few-Shot Learning Max Vladymyrov, Andrey Zhmoginov, Mark Sandler

ICMLW 2024 Efficient Linear System Solver with Transformers Max Vladymyrov, Johannes von Oswald, Nolan Andrew Miller, Mark Sandler

NeurIPSW 2024 How New Data Pollutes LLM Knowledge and How to Dilute It Chen Sun, Renat Aksitov, Andrey Zhmoginov, Nolan Andrew Miller, Max Vladymyrov, Ulrich Rueckert, Been Kim, Mark Sandler

NeurIPSW 2024 Knowledge Distillation: The Functional Perspective Israel Mason-Williams, Gabryel Mason-Williams, Mark Sandler

ICMLW 2024 Learning Fast and Slow: Representations for In-Context Weight Modulation Andrey Zhmoginov, Jihwan Lee, Max Vladymyrov, Mark Sandler

ICMLW 2024 Learning and Unlearning of Fabricated Knowledge in Language Models Chen Sun, Nolan Andrew Miller, Andrey Zhmoginov, Max Vladymyrov, Mark Sandler

NeurIPS 2024 Linear Transformers Are Versatile In-Context Learners Max Vladymyrov, Johannes von Oswald, Mark Sandler, Rong Ge

ICMLW 2024 Linear Transformers Are Versatile In-Context Learners Max Vladymyrov, Johannes von Oswald, Mark Sandler, Rong Ge

ICMLW 2024 Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Andrey Zhmoginov, Jihwan Lee, Mark Sandler

CVPR 2023 Decentralized Learning with Multi-Headed Distillation Andrey Zhmoginov, Mark Sandler, Nolan Miller, Gus Kristiansen, Max Vladymyrov

CVPR 2022 Fine-Tuning Image Transformers Using Learnable Memory Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Andrew Jackson

ICML 2022 HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning Andrey Zhmoginov, Mark Sandler, Maksym Vladymyrov

ICML 2021 Meta-Learning Bidirectional Update Rules Mark Sandler, Max Vladymyrov, Andrey Zhmoginov, Nolan Miller, Tom Madams, Andrew Jackson, Blaise Agüera Y Arcas

ECML-PKDD 2020 Information-Bottleneck Approach to Salient Region Discovery Andrey Zhmoginov, Ian Fischer, Mark Sandler

ECCVW 2020 SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection Keren Ye, Adriana Kovashka, Mark Sandler, Menglong Zhu, Andrew G. Howard, Marco Fornoni

CVPR 2020 Structured Multi-Hashing for Model Compression Elad Eban, Yair Movshovitz-Attias, Hao Wu, Mark Sandler, Andrew Poon, Yerlan Idelbayev, Miguel A. Carreira-Perpinan

ICLR 2019 K for the Price of 1: Parameter-Efficient Multi-Task and Transfer Learning Pramod Kaushik Mudrakarta, Mark Sandler, Andrey Zhmoginov, Andrew Howard

CVPR 2019 MnasNet: Platform-Aware Neural Architecture Search for Mobile Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, Quoc V. Le

ICCVW 2019 Non-Discriminative Data or Weak Model? on the Relative Importance of Data and Model Resolution Mark Sandler, Jonathan Baccash, Andrey Zhmoginov, Andrew Howard

ICCV 2019 Searching for MobileNetV3 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

CVPR 2018 MobileNetV2: Inverted Residuals and Linear Bottlenecks Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen

ECCV 2018 NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications Tien-Ju Yang, Andrew Howard, Bo Chen, Xiao Zhang, Alec Go, Mark Sandler, Vivienne Sze, Hartwig Adam