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Training a neural network is a monolithic endeavor, akin to carving knowledge into stone: once the process is completed, editing the knowledge in a network is hard, since all information is distributed across the network's weights.
Simpleshot: Revisiting nearest-neighbor classification for few-shot learning
Yan Wang, Wei-Lun Chao, Kilian Q Weinberger, and Laurens Van Der Maaten · 1911
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Context theory of classification learning
Douglas L Medin and Marguerite M Schaffer · 1978
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Attention, similarity, and the identification–categorization relationship
Robert M Nosofsky · 1986
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Instance-based learning algorithms
David W Aha, Dennis Kibler, and Marc K Albert · 1991
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Face recognition using eigenfaces
Matthew A Turk and Alex P Pentland · 1991
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Combining instance-based and model-based learning
J Ross Quinlan · 1993
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Comparing exemplar and prototype models of categorization
Stephen Dopkins and Theresa Gleason · 1997
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Video Google: a text retrieval approach to object matching in videos
Sivic and Zisserman · 2003
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The problem of concept drift: definitions and related work
Alexey Tsymbal · 2004
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Generalization and similarity in exemplar models of categorization: Insights from machine learning
Frank Jäkel, Bernhard Schölkopf, and Felix A Wichmann · 2008
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A novel weighted voting for k-nearest neighbor rule
Jianping Gou, Taisong Xiong, Yin Kuang, et al · 2011
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The generalized context model: An exemplar model of classification
Robert M Nosofsky · 2011
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Food-101–mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
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Jason Weston, Sumit Chopra, and Antoine Bordes · 2014
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Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang · 2015
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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
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Deep learning scaling is predictable, empirically
Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md Mostofa Ali Patwary, Yang Yang, and Yanqi Zhou · 2017
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Semi-supervised deep learning with memory
Yanbei Chen, Xiatian Zhu, and Shaogang Gong · 2018
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Learning under concept drift: A review
Jie Lu, Anjin Liu, Fan Dong, Feng Gu, Joao Gama, and Guangquan Zhang · 2018
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Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning
Nicolas Papernot and Patrick McDaniel · 2018
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Neural nearest neighbors networks
Tobias Plötz and Stefan Roth · 2018
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Interpreting neural networks with nearest neighbors
Eric Wallace, Shi Feng, and Jordan Boyd-Graber · 2018
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Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin · 2018
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Approximating CNNs with bag-of-local-features models works surprisingly well on ImageNet
Wieland Brendel and Matthias Bethge · 2019
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ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2019
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Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2019
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Generalization through memorization: Nearest neighbor language models
Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis · 2019
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Continual lifelong learning with neural networks: A review
German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter · 2019
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On the robustness of deep k-nearest neighbors
Chawin Sitawarin and David Wagner · 2019
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Deep nearest neighbor anomaly detection
Liron Bergman, Niv Cohen, and Yedid Hoshen · 2020
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Lucas Beyer, Olivier J Hénaff, Alexander Kolesnikov, Xiaohua Zhai, and Aäron van den Oord · 2020
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What neural networks memorize and why: Discovering the long tail via influence estimation
Vitaly Feldman and Chiyuan Zhang · 2020
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Re-labeling ImageNet: from single to multi-labels, from global to localized labels
Sangdoo Yun, Seong Joon Oh, Byeongho Heo, Dongyoon Han, Junsuk Choe, and Sanghyuk Chun · 2021
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From concept drift to model degradation: An overview on performance-aware drift detectors
Firas Bayram, Bestoun S Ahmed, and Andreas Kassler · 2022
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You can’t pick your neighbors, or can you? when and how to rely on retrieval in the k k nn-lm
Andrew Drozdov, Shufan Wang, Razieh Rahimi, Andrew McCallum, Hamed Zamani, and Mohit Iyyer · 2022
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Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al · 2022
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Accelerating large-scale inference with anisotropic vector quantization
Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, and Sanjiv Kumar · 2020
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The many faces of robustness: A critical analysis of out-of-distribution generalization
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, and Justin Gilmer · 2020
Cited alongside, same era.
Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
Cited alongside, same era.
ALCOVE: an exemplar-based connectionist model of category learning
John K Kruschke · 2020
Cited alongside, same era.
Improving trust in deep neural networks with nearest neighbors
Ritchie Lee, Justin Clarke, Adrian Agogino, and Dimitra Giannakopoulou · 2020
Cited alongside, same era.
Explaining and improving model behavior with k nearest neighbor representations
Nazneen Fatema Rajani, Ben Krause, Wengpeng Yin, Tong Niu, Richard Socher, and Caiming Xiong · 2020
Cited alongside, same era.
Evaluating machine accuracy on ImageNet
Vaishaal Shankar, Rebecca Roelofs, Horia Mania, Alex Fang, Benjamin Recht, and Ludwig Schmidt · 2020
Cited alongside, same era.
Ahmet Iscen, Thomas Bird, Mathilde Caron, Alireza Fathi, and Cordelia Schmid · 2022
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Revisiting a knn-based image classification system with high-capacity storage
Kengo Nakata, Youyang Ng, Daisuke Miyashita, Asuka Maki, Yu-Chieh Lin, and Jun Deguchi · 2022
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A survey of machine unlearning
Thanh Tam Nguyen, Thanh Trung Huynh, Phi Le Nguyen, Alan Wee-Chung Liew, Hongzhi Yin, and Quoc Viet Hung Nguyen · 2022
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Beyond neural scaling laws: beating power law scaling via data pruning
Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, and Ari Morcos · 2022
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Out-of-distribution detection with deep nearest neighbors
Yiyou Sun, Yifei Ming, Xiaojin Zhu, and Yixuan Li · 2022
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Scaling vision transformers
Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer · 2022
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SemDeDup: Data-efficient learning at web-scale through semantic deduplication
Amro Kamal Mohamed Abbas, Kushal Tirumala, Daniel Simig, Surya Ganguli, and Ari S Morcos · 2023
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In or out? Fixing ImageNet out-of-distribution detection evaluation
Julian Bitterwolf, Maximilian Müller, and Matthias Hein · 2023
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Scaling vision transformers to 22 billion parameters
Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin F. Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Patrick Collier, Alexey Gritsenko, Vighnesh Birodkar, Cristina Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetić, Dustin Tran, Thomas Kipf, Mario Lučić, Xiaohua Zhai, Daniel Keysers, Jeremiah Harmsen, and Neil Houlsby · 2023
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Retrieval-enhanced contrastive vision-text models
Ahmet Iscen, Mathilde Caron, Alireza Fathi, and Cordelia Schmid · 2023
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DINOv2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel HAZIZA, Francisco Massa, Alaaeldin El-Nouby, et al · 2023
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Online continual learning without the storage constraint
Ameya Prabhu, Zhipeng Cai, Puneet Dokania, Philip Torr, Vladlen Koltun, and Ozan Sener · 2023
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A review on machine unlearning
Haibo Zhang, Toru Nakamura, Takamasa Isohara, and Kouichi Sakurai · 2023
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kNN-CLIP: Retrieval enables training-free segmentation on continually expanding large vocabularies
Zhongrui Gui, Shuyang Sun, Runjia Li, Jianhao Yuan, Zhaochong An, Karsten Roth, Ameya Prabhu, and Philip Torr · 2024
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BANG: billion-scale approximate nearest neighbor search using a single gpu
Saim Khan, Somesh Singh, Harsha Vardhan Simhadri, Jyothi Vedurada, et al · 2024
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Towards unbounded machine unlearning
Meghdad Kurmanji, Peter Triantafillou, Jamie Hayes, and Eleni Triantafillou · 2024
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Machine unlearning in 2024, Apr 2024
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
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Data selection for transfer unlearning
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Scaling retrieval-based language models with a trillion-token datastore
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Learning from memory: Non-parametric memory augmented self-supervised learning of visual features
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Are we making progress in unlearning? findings from the first neurips unlearning competition
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