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Modern generative models exhibit unprecedented capabilities to generate extremely realistic data.
Reverse Engineering the Brain , 2008
IEEE Spectrum. David Adler · 2008
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Compositionality in rational analysis: Grammar-based induction for concept learning
Noah D Goodman, Joshua B Tenenbaum, Thomas L Griffiths, and Jacob Feldman · 2008
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Is weak emergence just in the mind?
Mark A Bedau · 2008
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Categorial compositionality: A category theory explanation for the systematicity of human cognition
Steven Phillips and William H Wilson · 2010
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Compositionality of rule representations in human prefrontal cortex
Carlo Reverberi, Kai Görgen, and John-Dylan Haynes · 2012
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
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Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens Van Der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick · 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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Improving language understanding by generative pre-training, 2018
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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Compositional clustering in task structure learning
Nicholas T Franklin and Michael J Frank · 2018
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Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
Brenden Lake and Marco Baroni · 2018
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Bias and generalization in deep generative models: An empirical study
Shengjia Zhao, Hongyu Ren, Arianna Yuan, Jiaming Song, Noah Goodman, and Stefano Ermon · 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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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction
Hidenori Tanaka, Aran Nayebi, Niru Maheswaranathan, Lane McIntosh, Stephen Baccus, and Surya Ganguli · 2019
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Measuring compositionality in representation learning
Jacob Andreas · 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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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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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
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Doctor GPT-3: hype or reality? , 2020
Kevin Riera, Anne-Laure Rousseau, and Clement Baudelaire · 2020
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Concepts and compositionality: in search of the brain’s language of thought
Steven M Frankland and Joshua D Greene · 2020
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Compositionality decomposed: How do neural networks generalise?
Dieuwke Hupkes, Verna Dankers, Mathijs Mul, and Elia Bruni · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 2021
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Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 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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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le · 2021
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A general language assistant as a laboratory for alignment
Amanda Askell, Yuntao Bai, Anna Chen, Dawn Drain, Deep Ganguli, Tom Henighan, Andy Jones, Nicholas Joseph, Ben Mann, Nova DasSarma, et al · 2021
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Skill induction and planning with latent language
Pratyusha Sharma, Antonio Torralba, and Jacob Andreas · 2021
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Can subnetwork structure be the key to out-of-distribution generalization?
Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, and Aaron Courville · 2021
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Illiterate dall-e learns to compose
Gautam Singh, Fei Deng, and Sungjin Ahn · 2021
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Why Scientists Have Spent Years Mapping This Creature’s Brain , 2021
NYTimes. Emily Anthes · 2021
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On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
Cited alongside, same era.
Unsupervised learning of compositional energy concepts
Yilun Du, Shuang Li, Yash Sharma, Josh Tenenbaum, and Igor Mordatch · 2021
Cited alongside, same era.
The role of syntactic planning in compositional image captioning
Emanuele Bugliarello and Desmond Elliott · 2021
Cited alongside, same era.
Visual representation learning does not generalize strongly within the same domain
Lukas Schott, Julius Von Kügelgen, Frederik Träuble, Peter Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, and Wieland Brendel · 2021
When and why vision-language models behave like bag-of-words models, and what to do about it?
Mert Yuksekgonul, Federico Bianchi, Pratyusha Kalluri, Dan Jurafsky, and James Zou · 2022
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Compositional generalization in grounded language learning via induced model sparsity
Sam Spilsbury and Alexander Ilin · 2022
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Winoground: Probing vision and language models for visio-linguistic compositionality
Tristan Thrush, Ryan Jiang, Max Bartolo, Amanpreet Singh, Adina Williams, Douwe Kiela, and Candace Ross · 2022
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Does clip bind concepts? probing compositionality in large image models
Martha Lewis, Qinan Yu, Jack Merullo, and Ellie Pavlick · 2022
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Do vision-language pretrained models learn primitive concepts?
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Cited alongside, same era.
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
Cited alongside, same era.
Towards causal representation learning
Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio · 2021
Cited alongside, same era.
Redcaps: Web-curated image-text data created by the people, for the people
Karan Desai, Gaurav Kaul, Zubin Aysola, and Justin Johnson · 2021
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
Cited alongside, same era.
Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 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.
Dreamfusion: Text-to-3d using 2d diffusion
Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall · 2022
Cited alongside, same era.
Tian Yun, Usha Bhalla, Ellie Pavlick, and Chen Sun · 2022
Later among the works it cites.
Benchmarking compositionality with formal languages
Josef Valvoda, Naomi Saphra, Jonathan Rawski, Adina Williams, and Ryan Cotterell · 2022
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Hidden progress in deep learning: Sgd learns parities near the computational limit
Boaz Barak, Benjamin Edelman, Surbhi Goel, Sham Kakade, Eran Malach, and Cyril Zhang · 2022
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Texture: Text-guided texturing of 3d shapes
Elad Richardson, Gal Metzer, Yuval Alaluf, Raja Giryes, and Daniel Cohen-Or · 2023
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Score-based diffusion models in function space
Jae Hyun Lim, Nikola B Kovachki, Ricardo Baptista, Christopher Beckham, Kamyar Azizzadenesheli, Jean Kossaifi, Vikram Voleti, Jiaming Song, Karsten Kreis, Jan Kautz, et al · 2023
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Align your latents: High-resolution video synthesis with latent diffusion models
Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis · 2023
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Motion-conditioned diffusion model for controllable video synthesis
Tsai-Shien Chen, Chieh Hubert Lin, Hung-Yu Tseng, Tsung-Yi Lin, and Ming-Hsuan Yang · 2023
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Text2performer: Text-driven human video generation
Yuming Jiang, Shuai Yang, Tong Liang Koh, Wayne Wu, Chen Change Loy, and Ziwei Liu · 2023
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Video probabilistic diffusion models in projected latent space
Sihyun Yu, Kihyuk Sohn, Subin Kim, and Jinwoo Shin · 2023
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Pix2video: Video editing using image diffusion
Duygu Ceylan, Chun-Hao Paul Huang, and Niloy J Mitra · 2023
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Edit-a-video: Single video editing with object-aware consistency
Chaehun Shin, Heeseung Kim, Che Hyun Lee, Sang-gil Lee, and Sungroh Yoon · 2023
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Pair-diffusion: Object-level image editing with structure-and-appearance paired diffusion models
Vidit Goel, Elia Peruzzo, Yifan Jiang, Dejia Xu, Nicu Sebe, Trevor Darrell, Zhangyang Wang, and Humphrey Shi · 2023
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Preditor: Text guided image editing with diffusion prior
Hareesh Ravi, Sachin Kelkar, Midhun Harikumar, and Ajinkya Kale · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Codegeex: A pre-trained model for code generation with multilingual evaluations on humaneval-x
Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shan Wang, Yufei Xue, Zihan Wang, Lei Shen, Andi Wang, Yang Li, et al · 2023
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Multipl-e: A scalable and polyglot approach to benchmarking neural code generation
Federico Cassano, John Gouwar, Daniel Nguyen, Sydney Nguyen, Luna Phipps-Costin, Donald Pinckney, Ming-Ho Yee, Yangtian Zi, Carolyn Jane Anderson, Molly Q Feldman, et al · 2023
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Chatgpt for robotics: Design principles and model abilities
Sai Vemprala, Rogerio Bonatti, Arthur Bucker, and Ashish Kapoor · 2023
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A Boston Dynamics robot can now be run using ChatGPT. What could go wrong? , 2023
Boston Globe · 2023
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A Conversation With Bing’s Chatbot Left Me Deeply Unsettled , 2023
Kevin Roose · 2023
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Diffusion policy: Visuomotor policy learning via action diffusion
Cheng Chi, Siyuan Feng, Yilun Du, Zhenjia Xu, Eric Cousineau, Benjamin Burchfiel, and Shuran Song · 2023
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Edgi: Equivariant diffusion for planning with embodied agents
Johann Brehmer, Joey Bose, Pim De Haan, and Taco Cohen · 2023
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A picture is worth a thousand words: Language models plan from pixels
Anthony Z Liu, Lajanugen Logeswaran, Sungryull Sohn, and Honglak Lee · 2023
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A comprehensive benchmark of human-like relational reasoning for text-to-image foundation models
Colin Conwell and Tomer Ullman · 2023
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Reduce, reuse, recycle: Compositional generation with energy-based diffusion models and mcmc
Yilun Du, Conor Durkan, Robin Strudel, Joshua B Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, and Will Grathwohl · 2023
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A theory for emergence of complex skills in language models
Sanjeev Arora and Anirudh Goyal · 2023
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Progress measures for grokking via mechanistic interpretability
Neel Nanda, Lawrence Chan, Tom Liberum, Jess Smith, and Jacob Steinhardt · 2023
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Arbitrary style guidance for enhanced diffusion-based text-to-image generation
Zhihong Pan, Xin Zhou, and Hao Tian · 2023
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Ablating concepts in text-to-image diffusion models
Nupur Kumari, Bingliang Zhang, Sheng-Yu Wang, Eli Shechtman, Richard Zhang, and Jun-Yan Zhu · 2023
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Break it down: Evidence for structural compositionality in neural networks
Michael A Lepori, Thomas Serre, and Ellie Pavlick · 2023
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Compositional generalization from first principles
Thaddäus Wiedemer, Prasanna Mayilvahanan, Matthias Bethge, and Wieland Brendel · 2023
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Emergence in complex networks of simple agents
David G Green · 2023
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Are emergent abilities of large language models a mirage?
Rylan Schaeffer, Brando Miranda, and Sanmi Koyejo · 2023
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How capable can a transformer become? a study on synthetic, interpretable tasks
Rahul Ramesh, Mikail Khona, Robert P Dick, Hidenori Tanaka, and Ekdeep Singh Lubana · 2023
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