Fetching the paper…
Reading the bibliography…
In many real-world scenarios, data to train machine learning models becomes available over time.
Random sampling with a reservoir
Jeffrey S Vitter · 1985
Earlier work this paper cites.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
Earlier work this paper cites.
Rcv1: A new benchmark collection for text categorization research
David D Lewis, Yiming Yang, Tony Russell-Rose, and Fan Li · 2004
Earlier work this paper cites.
Model compression
Cristian Bucilua, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Transforming auto-encoders
Geoffrey E Hinton, Alex Krizhevsky, and Sida D Wang · 2011
Earlier work this paper cites.
Enhancing navigation on wikipedia with social tags
Arkaitz Zubiaga · 2012
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
Earlier work this paper cites.
Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
Earlier work this paper cites.
Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
Earlier work this paper cites.
icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
Earlier work this paper cites.
Dynamic routing between capsules
Sara Sabour, Nicholas Frosst, and Geoffrey E Hinton · 2017
Earlier work this paper cites.
Incremental learning of object detectors without catastrophic forgetting
Konstantin Shmelkov, Cordelia Schmid, and Karteek Alahari · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Rahaf Aljundi, Marcus Rohrbach, and Tinne Tuytelaars · 2018
Earlier work this paper cites.
End-to-end incremental learning
Francisco M Castro, Manuel J Marín-Jiménez, Nicolás Guil, Cordelia Schmid, and Karteek Alahari · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Earlier work this paper cites.
Sentence encoders on stilts: Supplementary training on intermediate labeled-data tasks
Jason Phang, Thibault Févry, and Samuel R Bowman · 2018
Earlier work this paper cites.
Learning to learn without forgetting by maximizing transfer and minimizing interference
Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, and Gerald Tesauro · 2018
Earlier work this paper cites.
Experience replay for continual learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P Lillicrap, and Greg Wayne · 2018
Earlier work this paper cites.
Progress & compress: A scalable framework for continual learning
Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 2018
Earlier work this paper cites.
Sgm: sequence generation model for multi-label classification
Pengcheng Yang, Xu Sun, Wei Li, Shuming Ma, Wei Wu, and Houfeng Wang · 2018
Earlier work this paper cites.
Gradient based sample selection for online continual learning
Rahaf Aljundi, Min Lin, Baptiste Goujaud, and Yoshua Bengio · 2019
Cited alongside, same era.
An information-theoretic approach to transferability in task transfer learning
Yajie Bao, Yang Li, Shao-Lun Huang, Lin Zhang, Lizhong Zheng, Amir Zamir, and Leonidas Guibas · 2019
Cited alongside, same era.
Continual learning with tiny episodic memories
Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet K Dokania, Philip HS Torr, and M Ranzato · 2019
Cited alongside, same era.
Episodic memory in lifelong language learning
Cyprien de Masson d’Autume, Sebastian Ruder, Lingpeng Kong, and Dani Yogatama · 2019
Cited alongside, same era.
Adapterhub: A framework for adapting transformers
Jonas Pfeiffer, Andreas Rücklé, Clifton Poth, Aishwarya Kamath, Ivan Vulić, Sebastian Ruder, Kyunghyun Cho, and Iryna Gurevych · 2020
Later among the works it cites.
Mad-x: An adapter-based framework for multi-task cross-lingual transfer
Jonas Pfeiffer, Ivan Vulić, Iryna Gurevych, and Sebastian Ruder · 2020
Later among the works it cites.
Yada Pruksachatkun, Jason Phang, Haokun Liu, Phu Mon Htut, Xiaoyi Zhang, Richard Yuanzhe Pang, Clara Vania, Katharina Kann, and Samuel R Bowman · 2020
Later among the works it cites.
Scalable transfer learning with expert models
Joan Puigcerver, Carlos Riquelme, Basil Mustafa, Cedric Renggli, André Susano Pinto, Sylvain Gelly, Daniel Keysers, and Neil Houlsby · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Claudio Greco, Barbara Plank, Raquel Fernández, and Raffaella Bernardi · 2019
Cited alongside, same era.
Learning a unified classifier incrementally via rebalancing
Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and Dahua Lin · 2019
Cited alongside, same era.
Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
Cited alongside, same era.
Meta-learning representations for continual learning
Khurram Javed and Martha White · 2019
Cited alongside, same era.
Linguistic knowledge and transferability of contextual representations
Nelson F Liu, Matt Gardner, Yonatan Belinkov, Matthew E Peters, and Noah A Smith · 2019
Cited alongside, same era.
Meta-learning improves lifelong relation extraction
Abiola Obamuyide, Andreas Vlachos, et al · 2019
Cited alongside, same era.
Lamol: Language modeling for lifelong language learning
Fan-Keng Sun, Cheng-Hao Ho, and Hung-Yi Lee · 2019
Cited alongside, same era.
Transferability and hardness of supervised classification tasks
Anh T Tran, Cuong V Nguyen, and Tal Hassner · 2019
Cited alongside, same era.
Tu Vu, Tong Wang, Tsendsuren Munkhdalai, Alessandro Sordoni, Adam Trischler, Andrew Mattarella-Micke, Subhransu Maji, and Mohit Iyyer · 2020
Later among the works it cites.
Efficient meta lifelong-learning with limited memory
Zirui Wang, Sanket Vaibhav Mehta, Barnabás Póczos, and Jaime Carbonell · 2020
Later among the works it cites.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Julien Chaumond, Lysandre Debut, Victor Sanh, Clement Delangue, Anthony Moi, Pierric Cistac, Morgan Funtowicz, Joe Davison, Sam Shleifer, et al · 2020
Later among the works it cites.
Mitchell Wortsman, Vivek Ramanujan, Rosanne Liu, Aniruddha Kembhavi, Mohammad Rastegari, Jason Yosinski, and Ali Farhadi · 2020
Later among the works it cites.
Class-incremental learning via deep model consolidation
Junting Zhang, Jie Zhang, Shalini Ghosh, Dawei Li, Serafettin Tasci, Larry Heck, Heming Zhang, and C-C Jay Kuo · 2020
Later among the works it cites.
Data-free knowledge distillation for object detection
Akshay Chawla, Hongxu Yin, Pavlo Molchanov, and Jose Alvarez · 2021
Later among the works it cites.
Dytox: Transformers for continual learning with dynamic token expansion
Arthur Douillard, Alexandre Ramé, Guillaume Couairon, and Matthieu Cord · 2021
Later among the works it cites.
Continual learning by using information of each class holistically
Wenpeng Hu, Qi Qin, Mengyu Wang, Jinwen Ma, and Bing Liu · 2021
Later among the works it cites.
Frustratingly easy transferability estimation
Long-Kai Huang, Ying Wei, Yu Rong, Qiang Yang, and Junzhou Huang · 2021
Later among the works it cites.
Continual learning for text classification with information disentanglement based regularization
Yufan Huang, Yanzhe Zhang, Jiaao Chen, Xuezhi Wang, and Diyi Yang · 2021
Later among the works it cites.
Adapting bert for continual learning of a sequence of aspect sentiment classification tasks
Zixuan Ke, Hu Xu, and Bing Liu · 2021
Later among the works it cites.
Continual few-shot learning for text classification
Ramakanth Pasunuru, Veselin Stoyanov, and Mohit Bansal · 2021
Later among the works it cites.
AdapterFusion: Non-destructive task composition for transfer learning
Jonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé, Kyunghyun Cho, and Iryna Gurevych · 2021
Later among the works it cites.
What to pre-train on? efficient intermediate task selection
Clifton Poth, Jonas Pfeiffer, Andreas Rücklé, and Iryna Gurevych · 2021
Later among the works it cites.
Otce: A transferability metric for cross-domain cross-task representations
Yang Tan, Yang Li, and Shao-Lun Huang · 2021
Later among the works it cites.
Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
Later among the works it cites.
Congying Xia, Wenpeng Yin, Yihao Feng, and Philip Yu · 2021
Later among the works it cites.
Der: Dynamically expandable representation for class incremental learning
Shipeng Yan, Jiangwei Xie, and Xuming He · 2021
Later among the works it cites.
Technical report for iccv 2021 challenge SSLAD-Track3B: Transformers are better continual learners
Duo Li, Guimei Cao, Yunlu Xu, Zhanzhan Cheng, and Yi Niu · 2022
Closest in time.