Mark Sandler - RankMe

61 papers A* 19Journal 33Unranked 8

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Contextually Guided Transformers via Low-Rank Adaptation. CoRR Andrey Zhmoginov, Jihwan Lee, Max Vladymyrov, Mark Sandler
2025	A*	`conf`	How new data permeates LLM knowledge and how to dilute it. ICLR Chen Sun, Renat Aksitov, Andrey Zhmoginov, Nolan Andrew Miller, Max Vladymyrov, Ulrich Rueckert, Been Kim, Mark Sandler
2025	J	`jnl`	How new data permeates LLM knowledge and how to dilute it. CoRR Chen Sun, Renat Aksitov, Andrey Zhmoginov, Nolan Andrew Miller, Max Vladymyrov, Ulrich Rueckert, Been Kim, Mark Sandler
2025	J	`jnl`	Long Context In-Context Compression by Getting to the Gist of Gisting. CoRR Aleksandar Petrov, Mark Sandler, Andrey Zhmoginov, Nolan Miller, Max Vladymyrov
2025	J	`jnl`	Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones. CoRR Andrey Zhmoginov, Jihwan Lee, Mark Sandler
2024	J	`jnl`	Continual HyperTransformer: A Meta-Learner for Continual Few-Shot Learning. Trans. Mach. Learn. Res. Max Vladymyrov, Andrey Zhmoginov, Mark Sandler
2024	J	`jnl`	Learning and Unlearning of Fabricated Knowledge in Language Models. CoRR Chen Sun, Nolan Andrew Miller, Andrey Zhmoginov, Max Vladymyrov, Mark Sandler
2024	A*	`conf`	Linear Transformers are Versatile In-Context Learners. NeurIPS Max Vladymyrov, Johannes von Oswald, Mark Sandler, Rong Ge
2024	J	`jnl`	Linear Transformers are Versatile In-Context Learners. CoRR Max Vladymyrov, Johannes von Oswald, Mark Sandler, Rong Ge
2024	J	`jnl`	Narrowing the Focus: Learned Optimizers for Pretrained Models. CoRR Gus Kristiansen, Mark Sandler, Andrey Zhmoginov, Nolan Miller, Anirudh Goyal, Jihwan Lee, Max Vladymyrov
2023	J	`jnl`	Continual Few-Shot Learning Using HyperTransformers. CoRR Max Vladymyrov, Andrey Zhmoginov, Mark Sandler
2023	A*	`conf`	Decentralized Learning with Multi-Headed Distillation. CVPR Andrey Zhmoginov, Mark Sandler, Nolan Miller, Gus Kristiansen, Max Vladymyrov
2023	J	`jnl`	Training trajectories, mini-batch losses and the curious role of the learning rate. CoRR Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Nolan Miller
2023	J	`jnl`	Uncovering mesa-optimization algorithms in Transformers. CoRR 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
2022	J	`jnl`	Decentralized Learning with Multi-Headed Distillation. CoRR Andrey Zhmoginov, Mark Sandler, Nolan Miller, Gus Kristiansen, Max Vladymyrov
2022	A*	`conf`	Fine-tuning Image Transformers using Learnable Memory. CVPR Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Andrew Jackson
2022	J	`jnl`	Fine-tuning Image Transformers using Learnable Memory. CoRR Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Andrew Jackson
2022	A*	`conf`	HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning. ICML Andrey Zhmoginov, Mark Sandler, Maksym Vladymyrov
2022	J	`jnl`	HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning. CoRR Andrey Zhmoginov, Mark Sandler, Max Vladymyrov
2021	J	`jnl`	Compositional Models: Multi-Task Learning and Knowledge Transfer with Modular Networks. CoRR Andrey Zhmoginov, Dina Bashkirova, Mark Sandler
2021	A*	`conf`	Meta-Learning Bidirectional Update Rules. ICML Mark Sandler, Max Vladymyrov, Andrey Zhmoginov, Nolan Miller, Tom Madams, Andrew Jackson, Blaise Agüera y Arcas
2021	J	`jnl`	Meta-Learning Bidirectional Update Rules. CoRR Mark Sandler, Max Vladymyrov, Andrey Zhmoginov, Nolan Miller, Andrew Jackson, Tom Madams, Blaise Agüera y Arcas
2021	J	`jnl`	SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection. CoRR Keren Ye, Adriana Kovashka, Mark Sandler, Menglong Zhu, Andrew Howard, Marco Fornoni
2020	J	`jnl`	Image segmentation via Cellular Automata. CoRR Mark Sandler, Andrey Zhmoginov, Liangcheng Luo, Alexander Mordvintsev, Ettore Randazzo, Blaise Agüera y Arcas
2020	—	`conf`	Information-Bottleneck Approach to Salient Region Discovery. ECML/PKDD (3) Andrey Zhmoginov, Ian Fischer, Mark Sandler
2020	J	`jnl`	Large-Scale Generative Data-Free Distillation. CoRR Liangchen Luo, Mark Sandler, Zi Lin, Andrey Zhmoginov, Andrew Howard
2020	—	`conf`	SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection. ACCV (6) Keren Ye, Adriana Kovashka, Mark Sandler, Menglong Zhu, Andrew G. Howard, Marco Fornoni
2020	—	`conf`	SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection. ECCV Workshops (1) Keren Ye, Adriana Kovashka, Mark Sandler, Menglong Zhu, Andrew G. Howard, Marco Fornoni
2020	A*	`conf`	Structured Multi-Hashing for Model Compression. CVPR Elad Eban, Yair Movshovitz-Attias, Hao Wu, Mark Sandler, Andrew Poon, Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán
2019	J	`jnl`	Information-Bottleneck Approach to Salient Region Discovery. CoRR Andrey Zhmoginov, Ian Fischer, Mark Sandler
2019	—	`conf`	K for the Price of 1: Parameter-efficient Multi-task and Transfer Learning. ICLR (Poster) Pramod Kaushik Mudrakarta, Mark Sandler, Andrey Zhmoginov, Andrew G. Howard
2019	A*	`conf`	MnasNet: Platform-Aware Neural Architecture Search for Mobile. CVPR Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, Quoc V. Le
2019	—	`conf`	Non-Discriminative Data or Weak Model? On the Relative Importance of Data and Model Resolution. ICCV Workshops Mark Sandler, Jonathan Baccash, Andrey Zhmoginov, Andrew Howard
2019	J	`jnl`	Non-discriminative data or weak model? On the relative importance of data and model resolution. CoRR Mark Sandler, Jonathan Baccash, Andrey Zhmoginov, Andrew Howard
2019	A*	`conf`	Searching for MobileNetV3. ICCV 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
2019	J	`jnl`	Searching for MobileNetV3. CoRR 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
2019	J	`jnl`	Structured Multi-Hashing for Model Compression. CoRR Elad Eban, Yair Movshovitz-Attias, Hao Wu, Mark Sandler, Andrew Poon, Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán
2018	J	`jnl`	Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation. CoRR Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen
2018	J	`jnl`	K For The Price Of 1: Parameter Efficient Multi-task And Transfer Learning. CoRR Pramod Kaushik Mudrakarta, Mark Sandler, Andrey Zhmoginov, Andrew G. Howard
2018	A*	`conf`	MobileNetV2: Inverted Residuals and Linear Bottlenecks. CVPR Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen
2018	—	`conf`	NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications. ECCV (10) Tien-Ju Yang, Andrew G. Howard, Bo Chen, Xiao Zhang, Alec Go, Mark Sandler, Vivienne Sze, Hartwig Adam
2017	J	`jnl`	CycleGAN, a Master of Steganography. CoRR Casey Chu, Andrey Zhmoginov, Mark Sandler
2017	J	`jnl`	The Power of Sparsity in Convolutional Neural Networks. CoRR Soravit Changpinyo, Mark Sandler, Andrey Zhmoginov
2016	J	`jnl`	Inverting face embeddings with convolutional neural networks. CoRR Andrey Zhmoginov, Mark Sandler
2014	J	`jnl`	Frugal Streaming for Estimating Quantiles: One (or two) memory suffices. CoRR Qiang Ma, S. Muthukrishnan, Mark Sandler
2013	—	`conf`	Frugal Streaming for Estimating Quantiles. Space-Efficient Data Structures, Streams, and Algorithms Qiang Ma, S. Muthukrishnan, Mark Sandler
2013	A*	`conf`	Understanding latency variations of black box services. WWW Darja Krushevskaja, Mark Sandler
2011	—	`conf`	Modeling the Parallel Execution of Black-Box Services. HotCloud Gideon Mann, Mark Sandler, Darja Krushevskaja, Sudipto Guha, Eyal Even-Dar
2011	J	`jnl`	Telling Two Distributions Apart: a Tight Characterization CoRR Eyal Even-Dar, Mark Sandler
2010	A*	`conf`	Monitoring algorithms for negative feedback systems. WWW Mark Sandler, S. Muthukrishnan
2008	J	`jnl`	Network Failure Detection and Graph Connectivity. SIAM J. Comput. Jon M. Kleinberg, Mark Sandler, Aleksandrs Slivkins
2008	J	`jnl`	Theory research at Google. SIGACT News 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
2008	J	`jnl`	Using mixture models for collaborative filtering. J. Comput. Syst. Sci. Jon M. Kleinberg, Mark Sandler
2007	A*	`conf`	Hierarchical mixture models: a probabilistic analysis. KDD Mark Sandler
2006	—	`—`	Algorithms for Mixture Models. Mark Sandler
2006	A*	`conf`	Privacy via pseudorandom sketches. PODS Nina Mishra, Mark Sandler
2005	A*	`conf`	On Learning Mixtures of Heavy-Tailed Distributions. FOCS Anirban Dasgupta, John E. Hopcroft, Jon M. Kleinberg, Mark Sandler
2005	A*	`conf`	On the use of linear programming for unsupervised text classification. KDD Mark Sandler
2004	A*	`conf`	Network failure detection and graph connectivity. SODA Jon M. Kleinberg, Mark Sandler, Aleksandrs Slivkins
2004	A*	`conf`	Using mixture models for collaborative filtering. STOC Jon M. Kleinberg, Mark Sandler
2003	A*	`conf`	Convergent algorithms for collaborative filtering. EC Jon M. Kleinberg, Mark Sandler