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Modern generative models demonstrate impressive capabilities, likely stemming from an ability to identify and manipulate abstract concepts underlying their training data.
Competence and performance in child language: Are children really competent to judge?
Jill G De Villiers and Peter A De Villiers · 1974
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Knowledge acquisition: Enrichment or conceptual change
Susan Carey · 1991
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The origin and evolution of everyday concepts
Susan Carey · 1992
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Domain-specific knowledge and conceptual change
Susan Carey and Elizabeth Spelke · 1994
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Performance and competence in second language acquisition
Gillian Brown, Kirsten Malmkjær, and John Williams · 1996
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Learning by discovering concept hierarchies
Blaž Zupan, Marko Bohanec, Janez Demšar, and Ivan Bratko · 1999
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The origin of concepts
Susan Carey · 2000
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Bootstrapping & the origin of concepts
Susan Carey · 2004
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Linguistic competence without knowledge of language
John Collins · 2007
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Where our number concepts come from
Susan Carey · 2009
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Visual concept learning: Combining machine vision and bayesian generalization on concept hierarchies
Yangqing Jia, Joshua T Abbott, Joseph L Austerweil, Tom Griffiths, and Trevor Darrell · 2013
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Aspects of the Theory of Syntax
Noam Chomsky · 2014
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U-net: Convolutional networks for biomedical image segmentation, 2015
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Deep residual learning for image recognition, 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Unsupervised feature extraction by time-contrastive learning and nonlinear ica
Aapo Hyvarinen and Hiroshi Morioka · 2016
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Layer normalization, 2016
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
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Nonlinear ICA of temporally dependent stationary sources
Aapo Hyvarinen and Hiroshi Morioka · 2017
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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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Variational inference of disentangled latent concepts from unlabeled observations
Abhishek Kumar, Prasanna Sattigeri, and Avinash Balakrishnan · 2017
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Counterfactuals uncover the modular structure of deep generative models
Michel Besserve, Arash Mehrjou, Rémy Sun, and Bernhard Schölkopf · 2018
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Disentangling by factorising
Hyunjik Kim and Andriy Mnih · 2018
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A framework for the quantitative evaluation of disentangled representations
Cian Eastwood and Christopher KI Williams · 2018
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Isolating sources of disentanglement in variational autoencoders
Ricky TQ Chen, Xuechen Li, Roger B Grosse, and David K Duvenaud · 2018
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Challenging common assumptions in the unsupervised learning of disentangled representations
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem · 2019
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Are disentangled representations helpful for abstract visual reasoning?
Sjoerd Van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, and Olivier Bachem · 2019
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Nonlinear ICA using auxiliary variables and generalized contrastive learning
Aapo Hyvarinen, Hiroaki Sasaki, and Richard Turner · 2019
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Hype: A benchmark for human eye perceptual evaluation of generative models, 2019
Sharon Zhou, Mitchell L. Gordon, Ranjay Krishna, Austin Narcomey, Li Fei-Fei, and Michael S. Bernstein · 2019
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Decoupled weight decay regularization, 2019
Ilya Loshchilov and Frank Hutter · 2019
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Pytorch: An imperative style, high-performance deep learning library, 2019
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Can machines learn morality? the delphi experiment
Liwei Jiang, Jena D Hwang, Chandra Bhagavatula, Ronan Le Bras, Jenny Liang, Jesse Dodge, Keisuke Sakaguchi, Maxwell Forbes, Jon Borchardt, Saadia Gabriel, et al · 2021
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Towards causal representation learning
Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio · 2021
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Illiterate dall-e learns to compose
Gautam Singh, Fei Deng, and Sungjin Ahn · 2021
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Self-supervised learning with data augmentations provably isolates content from style
Julius Von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, and Francesco Locatello · 2021
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Independent mechanism analysis, a new concept?
Luigi Gresele, Julius Von Kügelgen, Vincent Stimper, Bernhard Schölkopf, and Michel Besserve · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Imagic: Text-based real image editing with diffusion models
Bahjat Kawar, Shiran Zada, Oran Lang, Omer Tov, Huiwen Chang, Tali Dekel, Inbar Mosseri, and Michal Irani · 2022
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Imagen video: High definition video generation with diffusion models
Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey Gritsenko, Diederik P Kingma, Ben Poole, Mohammad Norouzi, David J Fleet, et al · 2022
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Dreamfusion: Text-to-3d using 2d diffusion
Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall · 2022
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Modeling the data-generating process is necessary for out-of-distribution generalization
Discovering interpretable directions in the semantic latent space of diffusion models
René Haas, Inbar Huberman-Spiegelglas, Rotem Mulayoff, and Tomer Michaeli · 2023
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Ai capabilities can be significantly improved without expensive retraining
Tom Davidson, Jean-Stanislas Denain, Pablo Villalobos, and Guillem Bas · 2023
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Variational diffusion models, 2023
Diederik P. Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2023
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When are lemons purple? the concept association bias of vision-language models
Yingtian Tang, Yutaro Yamada, Yoyo Zhang, and Ilker Yildirim · 2023
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Linguistic binding in diffusion models: Enhancing attribute correspondence through attention map alignment
Royi Rassin, Eran Hirsch, Daniel Glickman, Shauli Ravfogel, Yoav Goldberg, and Gal Chechik · 2023
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Jivat Neet Kaur, Emre Kiciman, and Amit Sharma · 2022
Cited alongside, same era.
Causal machine learning: A survey and open problems
Jean Kaddour, Aengus Lynch, Qi Liu, Matt J Kusner, and Ricardo Silva · 2022
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Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
Cited alongside, same era.
Testing relational understanding in text-guided image generation
Colin Conwell and Tomer Ullman · 2022
Cited alongside, same era.
Dall-e 2 fails to reliably capture common syntactic processes
Evelina Leivada, Elliot Murphy, and Gary Marcus · 2022
Cited alongside, same era.
Benchmarking spatial relationships in text-to-image generation
Tejas Gokhale, Hamid Palangi, Besmira Nushi, Vibhav Vineet, Eric Horvitz, Ece Kamar, Chitta Baral, and Yezhou Yang · 2022
Cited alongside, same era.
Dalle-2 is seeing double: flaws in word-to-concept mapping in text2image models
Royi Rassin, Shauli Ravfogel, and Yoav Goldberg · 2022
Cited alongside, same era.
Scaling autoregressive models for content-rich text-to-image generation
Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, et al · 2022
Cited alongside, same era.
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Progress measures for grokking via mechanistic interpretability, 2023
Neel Nanda, Lawrence Chan, Tom Lieberum, Jess Smith, and Jacob Steinhardt · 2023
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Grokking as the transition from lazy to rich training dynamics
Tanishq Kumar, Blake Bordelon, Samuel J Gershman, and Cengiz Pehlevan · 2023
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Gaussian error linear units (gelus), 2023
Dan Hendrycks and Kevin Gimpel · 2023
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Attention is all you need, 2023
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2023
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Video generation models as world simulators
Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Ng, Ricky Wang, and Aditya Ramesh · 2024
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Cat3d: Create anything in 3d with multi-view diffusion models
Ruiqi Gao, Aleksander Holynski, Philipp Henzler, Arthur Brussee, Ricardo Martin-Brualla, Pratul Srinivasan, Jonathan T Barron, and Ben Poole · 2024
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Images that sound: Composing images and sounds on a single canvas
Ziyang Chen, Daniel Geng, and Andrew Owens · 2024
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Genie: Generative interactive environments
Jake Bruce, Michael Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, et al · 2024
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Imran Khan’s ‘Victory Speech’ From Jail Shows A.I.’s Peril and Promise , 2024
Yan Zhuang (New York Times) · 2024
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AI deepfakes get very real as 2024 election season begins , 2024
Mark Sullivan (FastCompany) · 2024
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Gemini Team · 2024
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Introducing the next generation of Claude
Claude team · 2024
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Generating audio for video , 2024
Generative Media Team (Google Deepmind) · 2024
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Compositional abilities emerge multiplicatively: Exploring diffusion models on a synthetic task, 2024
Maya Okawa, Ekdeep Singh Lubana, Robert P. Dick, and Hidenori Tanaka · 2024
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Uncovering the text embedding in text-to-image diffusion models
Hu Yu, Hao Luo, Fan Wang, and Feng Zhao · 2024
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Learning discrete concepts in latent hierarchical models
Lingjing Kong, Guangyi Chen, Biwei Huang, Eric P Xing, Yuejie Chi, and Kun Zhang · 2024
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Concept algebra for (score-based) text-controlled generative models
Zihao Wang, Lin Gui, Jeffrey Negrea, and Victor Veitch · 2024
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Vishaal Udandarao, Ameya Prabhu, Adhiraj Ghosh, Yash Sharma, Philip HS Torr, Adel Bibi, Samuel Albanie, and Matthias Bethge · 2024
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On the scalability of diffusion-based text-to-image generation
Hao Li, Yang Zou, Ying Wang, Orchid Majumder, Yusheng Xie, R Manmatha, Ashwin Swaminathan, Zhuowen Tu, Stefano Ermon, and Stefano Soatto · 2024
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A philosophical introduction to language models – part i: Continuity with classic debates, 2024
Raphaël Millière and Cameron Buckner · 2024
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A philosophical introduction to language models-part ii: The way forward
Raphaël Millière and Cameron Buckner · 2024
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Sleeper agents: Training deceptive llms that persist through safety training
Evan Hubinger, Carson Denison, Jesse Mu, Mike Lambert, Meg Tong, Monte MacDiarmid, Tamera Lanham, Daniel M Ziegler, Tim Maxwell, Newton Cheng, et al · 2024
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Sparse feature circuits: Discovering and editing interpretable causal graphs in language models
Samuel Marks, Can Rager, Eric J Michaud, Yonatan Belinkov, David Bau, and Aaron Mueller · 2024
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Refusal in language models is mediated by a single direction
Andy Arditi, Oscar Obeso, Aaquib Syed, Daniel Paleka, Nina Rimsky, Wes Gurnee, and Neel Nanda · 2024
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The linear representation hypothesis and the geometry of large language models, 2024
Kiho Park, Yo Joong Choe, and Victor Veitch · 2024
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On mechanistic knowledge localization in text-to-image generative models
Samyadeep Basu, Keivan Rezaei, Ryan Rossi, Cherry Zhao, Vlad Morariu, Varun Manjunatha, and Soheil Feizi · 2024
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Probing the 3d awareness of visual foundation models
Mohamed El Banani, Amit Raj, Kevis-Kokitsi Maninis, Abhishek Kar, Yuanzhen Li, Michael Rubinstein, Deqing Sun, Leonidas Guibas, Justin Johnson, and Varun Jampani · 2024
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Guidelines for Capabilities Elicitation , 2024
METR · 2024
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Stress-testing capability elicitation with password-locked models
Ryan Greenblatt, Fabien Roger, Dmitrii Krasheninnikov, and David Krueger · 2024
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Black-box access is insufficient for rigorous ai audits
Stephen Casper, Carson Ezell, Charlotte Siegmann, Noam Kolt, Taylor Lynn Curtis, Benjamin Bucknall, Andreas Haupt, Kevin Wei, Jérémy Scheurer, Marius Hobbhahn, et al · 2024
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Unfamiliar finetuning examples control how language models hallucinate
Katie Kang, Eric Wallace, Claire Tomlin, Aviral Kumar, and Sergey Levine · 2024
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Relational composition in neural networks: A survey and call to action
Martin Wattenberg and Fernanda B Viégas · 2024
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Unsupervised compositional concepts discovery with text-to-image generative models
Nan Liu, Yilun Du, Shuang Li, Joshua B Tenenbaum, and Antonio Torralba · 2095
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