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The field of deep generative modeling has grown rapidly in the last few years.
Approximating human judgment of generated image quality
Y. Alex Kolchinski, Sharon Zhou, Shengjia Zhao, Mitchell Gordon, and Stefano Ermon · 1912
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Prediction and entropy of printed english
C. E. Shannon · 1951
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The need for biases in learning generalizations (rutgers computer science tech. rept. cbm-tr-117)
Tom M Mitchell · 1980
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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 · 2001
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
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Population stratification and spurious allelic association
Lon R Cardon and Lyle J Palmer · 2003
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Miles D. Cranmer, Sam Greydanus, Stephan Hoyer, Peter W. Battaglia, David N. Spergel, and Shirley Ho · 2003
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
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On the complexity of real functions
Mark Braverman · 2005
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Linear and generalized linear mixed models and their applications , volume 1
Jiming Jiang and Thuan Nguyen · 2007
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Efficient transformers: A survey
Yi Tay, Mostafa Dehghani, Dara Bahri, and Donald Metzler · 2009
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Complexity questions in non-uniform random variate generation
Luc Devroye · 2010
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Scaling laws for autoregressive generative modeling
Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, Tom B. Brown, Prafulla Dhariwal, Scott Gray, Chris Hallacy, Benjamin Mann, Alec Radford, Aditya Ramesh, Nick Ryder, Daniel M. Ziegler, John Schulman, Dario Amodei, and Sam McCandlish · 2010
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Bayesian active learning for classification and preference learning
Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel · 2011
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The neural autoregressive distribution estimator
Hugo Larochelle and Iain Murray · 2011
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Big data analytics in healthcare: promise and potential
Wullianallur Raghupathi and Viju Raghupathi · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyung Hyun Cho, and Yoshua Bengio · 2015
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Deep generative image models using a laplacian pyramid of adversarial networks
Emily L Denton, Soumith Chintala, Rob Fergus, et al · 2015
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Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio · 2015
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Learning to compose neural networks for question answering
Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein · 2016
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Neural module networks
Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein · 2016
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RETAIN: an interpretable predictive model for healthcare using reverse time attention mechanism
Edward Choi, Mohammad Taha Bahadori, Joshua A. Kulas, Andy Schuetz, Walter F. Stewart, and Jimeng Sun · 2016
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Generating images with perceptual similarity metrics based on deep networks
Alexey Dosovitskiy and Thomas Brox · 2016
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"why should i trust you?": Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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A note on the evaluation of generative models
L. Theis, A. van den Oord, and M. Bethge · 2016
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Bias and fairness in large language models: A survey
Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, Md Mehrab Tanjim, Sungchul Kim, Franck Dernoncourt, Tong Yu, Ruiyi Zhang, and Nesreen K. Ahmed · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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A unified approach to interpreting model predictions
Scott M. Lundberg and Su-In Lee · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Attention is all you need
Ashish Vaswani, Noam M. Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Counterfactual explanations without opening the black box: Automated decisions and the gdpr
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2017
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Relational inductive biases, deep learning, and graph networks
Peter Battaglia, Jessica Blake Chandler Hamrick, Victor Bapst, Alvaro Sanchez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andy Ballard, Justin Gilmer, George E. Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Jayne Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, and Razvan Pascanu · 2018
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Demystifying MMD GANs
Mikołaj Bińkowski, Dougal J. Sutherland, Michael Arbel, and Arthur Gretton · 2018
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Quantizing deep convolutional networks for efficient inference: A whitepaper
Raghuraman Krishnamoorthi · 2018
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Predictive uncertainty estimation via prior networks
Andrey Malinin and Mark Gales · 2018
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The Book of Why: The New Science of Cause and Effect
Judea Pearl and Dana Mackenzie · 2018
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A structured review of the validity of BLEU
Ehud Reiter · 2018
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Assessing generative models via precision and recall
Mehdi SM Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, and Sylvain Gelly · 2018
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mixup: Beyond empirical risk minimization, 2018
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz · 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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Texygen: A benchmarking platform for text generation models
Yaoming Zhu, Sidi Lu, Lei Zheng, Jiaxian Guo, Weinan Zhang, Jun Wang, and Yong Yu · 2018
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Jointly measuring diversity and quality in text generation models
Danial Alihosseini, Ehsan Montahaei, and Mahdieh Soleymani Baghshah · 2019
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Invariant risk minimization
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Language gans falling short
Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joelle Pineau, and Laurent Charlin · 2019
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Slice finder: Automated data slicing for model validation
Yeounoh Chung, Tim Kraska, Neoklis Polyzotis, Ki Hyun Tae, and Steven Euijong Whang · 2019
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Don’t take the easy way out: Ensemble based methods for avoiding known dataset biases
Christopher Clark, Mark Yatskar, and Luke Zettlemoyer · 2019
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Modeling Epistemic and Aleatoric Uncertainty with Bayesian Neural Networks and Latent Variables
Stefan Depeweg · 2019
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SOM-VAE: Interpretable discrete representation learning on time series
Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, and Gunnar Rätsch · 2019
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Lipstick on a pig: Debiasing methods cover up systematic gender biases in word embeddings but do not remove them
Hila Gonen and Yoav Goldberg · 2019
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Minimal random code learning: Getting bits back from compressed model parameters
Marton Havasi, Robert Peharz, and José Miguel Hernández-Lobato · 2019
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Variational autoencoders with jointly optimized latent dependency structure
Jiawei He, Yu Gong, Joseph Marino, Greg Mori, and Andreas Lehrmann · 2019
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Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
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Attention is not Explanation
Sarthak Jain and Byron C. Wallace · 2019
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Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
Tom McCoy, Ellie Pavlick, and Tal Linzen · 2019
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The seven tools of causal inference, with reflections on machine learning
Judea Pearl · 2019
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Language models are unsupervised multitask learners, 2019
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Maziar Raissi, Paris Perdikaris, and George E Karniadakis · 2019
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Ordered neurons: Integrating tree structures into recurrent neural networks
Yikang Shen, Shawn Tan, Alessandro Sordoni, and Aaron Courville · 2019
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Applications of machine learning in drug discovery and development
Jessica Vamathevan, Dominic Clark, Paul Czodrowski, Ian Dunham, Edgardo Ferran, George Lee, Bin Li, Anant Madabhushi, Parantu Shah, Michaela Spitzer, et al · 2019
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The promise of artificial intelligence in chemical engineering: Is it here, finally?
Venkat Venkatasubramanian · 2019
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Analyzing the structure of attention in a transformer language model
Jesse Vig and Yonatan Belinkov · 2019
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Universal adversarial triggers for attacking and analyzing nlp
Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, and Sameer Singh · 2019
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Machine learning in materials science
Jing Wei, Xuan Chu, Xiang-Yu Sun, Kun Xu, Hui-Xiong Deng, Jigen Chen, Zhongming Wei, and Ming Lei · 2019
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Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations
Robin Winter, Floriane Montanari, Frank Noé, and Djork-Arné Clevert · 2019
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Root mean square layer normalization
Biao Zhang and Rico Sennrich · 2019
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Hype: A benchmark for human eye perceptual evaluation of generative models
Sharon Zhou, Mitchell Gordon, Ranjay Krishna, Austin Narcomey, Li F Fei-Fei, and Michael Bernstein · 2019
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Fine-tuning language models from human preferences
Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving · 2019
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Robust optimal transport with applications in generative modeling and domain adaptation
Yogesh Balaji, Rama Chellappa, and Soheil Feizi · 2020
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Machine learning techniques for protein function prediction
Rosalin Bonetta and Gianluca Valentino · 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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Effectively unbiased fid and inception score and where to find them
Min Jin Chong and David Forsyth · 2020
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Disentangled and controllable face image generation via 3d imitative-contrastive learning
Yu Deng, Jiaolong Yang, Dong Chen, Fang Wen, and Xin Tong · 2020
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Calibration of probability predictions from machine-learning and statistical models
Carsten F. Dormann · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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Watch your up-convolution: CNN based generative deep neural networks are failing to reproduce spectral distributions
Ricard Durall, Margret Keuper, and Janis Keuper · 2020
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Uncertainty quantification in deep mri reconstruction
Vineet Edupuganti, Morteza Mardani, Shreyas Vasanawala, and John Pauly · 2020
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A disentangling invertible interpretation network for explaining latent representations
Patrick Esser, Robin Rombach, and Bjorn Ommer · 2020
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Latent space manipulation for high-resolution medical image synthesis via the stylegan
Lukas Fetty, Mikael Bylund, Peter Kuess, Gerd Heilemann, Tufve Nyholm, Dietmar Georg, and Tommy Löfstedt · 2020
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GP-VAE: Deep probabilistic time series imputation
Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch, and Stephan Mandt · 2020
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A review of challenges and opportunities in machine learning for health
Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L Beam, Irene Y Chen, and Rajesh Ranganath · 2020
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Interactive fiction games: A colossal adventure
Matthew Hausknecht, Prithviraj Ammanabrolu, Marc-Alexandre Côté, and Xingdi Yuan · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2020
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Ganspace: Discovering interpretable gan controls
Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, and Sylvain Paris · 2020
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Annotator rationales for labeling tasks in crowdsourcing
Mucahid Kutlu, Tyler McDonnell, Matthew Lease, and Tamer Elsayed · 2020
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Questioning the ai: informing design practices for explainable ai user experiences
Q Vera Liao, Daniel Gruen, and Sarah Miller · 2020
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Adversarial training for large neural language models
Xiaodong Liu, Hao Cheng, Pengcheng He, Weizhu Chen, Yu Wang, Hoifung Poon, and Jianfeng Gao · 2020
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Weakly-supervised disentanglement without compromises
Francesco Locatello, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, and Michael Tschannen · 2020
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Interpretability and explainability: A machine learning zoo mini-tour
Ricards Marcinkevics and Julia E. Vogt · 2020
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Does syntax need to grow on trees? sources of hierarchical inductive bias in sequence-to-sequence networks
R. Thomas McCoy, Robert Frank, and Tal Linzen · 2020
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Reliable fidelity and diversity metrics for generative models
Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjey Choi, and Jaejun Yoo · 2020
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Controlling style and semantics in weakly-supervised image generation
Dario Pavllo, Aurelien Lucchi, and Thomas Hofmann · 2020
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Deep structural causal models for tractable counterfactual inference
Nick Pawlowski, Daniel Coelho de Castro, and Ben Glocker · 2020
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Distributionally robust neural networks
Shiori Sagawa*, Pang Wei Koh*, Tatsunori B. Hashimoto, and Percy Liang · 2020
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Glu variants improve transformer
Noam Shazeer · 2020
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Interpreting the latent space of gans for semantic face editing
Yujun Shen, Jinjin Gu, Xiaoou Tang, and Bolei Zhou · 2020
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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Energy and policy considerations for modern deep learning research
Emma Strubell, Ananya Ganesh, and Andrew McCallum · 2020
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Catastrophic forgetting and mode collapse in gans
Hoang Thanh-Tung and Truyen Tran · 2020
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A theory of usable information under computational constraints
Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, and Stefano Ermon · 2020
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Variational Bayesian quantization
Yibo Yang, Robert Bamler, and Stephan Mandt · 2020
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RGGNet: Tolerance aware LiDAR-camera online calibration with geometric deep learning and generative model
Kaiwen Yuan, Zhenyu Guo, and Z. Jane Wang · 2020
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A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U. Rajendra Acharya, Vladimir Makarenkov, and Saeid Nahavandi · 2021
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Zero-Cost Proxies for Lightweight NAS
Mohamed S. Abdelfattah, Abhinav Mehrotra, Łukasz Dudziak, and Nicholas D. Lane · 2021
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On the dangers of stochastic parrots: Can language models be too big?
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell · 2021
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Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković · 2021
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All that‘s ‘human’ is not gold: Evaluating human evaluation of generated text
Elizabeth Clark, Tal August, Sofia Serrano, Nikita Haduong, Suchin Gururangan, and Noah A. Smith · 2021
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A historical perspective of explainable artificial intelligence
Roberto Confalonieri, Ludovik Coba, Benedikt Wagner, and Tarek R. Besold · 2021
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Explaining latent representations with a corpus of examples
Jonathan Crabbe, Zhaozhi Qian, Fergus Imrie, and Mihaela van der Schaar · 2021
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Incorporating symbolic domain knowledge into graph neural networks
Tirtharaj Dash, Ashwin Srinivasan, and Lovekesh Vig · 2021
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Measuring and Improving Consistency in Pretrained Language Models
Yanai Elazar, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Eduard Hovy, Hinrich Schütze, and Yoav Goldberg · 2021
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A mathematical framework for transformer circuits
Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, et al · 2021
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Taming transformers for high-resolution image synthesis
Patrick Esser, Robin Rombach, and Bjorn Ommer · 2021
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Efficiently modeling long sequences with structured state spaces
Albert Gu, Karan Goel, and Christopher Re · 2021
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Robust transfer learning with pretrained language models through adapters
Wenjuan Han, Bo Pang, and Ying Nian Wu · 2021
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Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
Eyke Hüllermeier and Willem Waegeman · 2021
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Scalable Gaussian process variational autoencoders
Metod Jazbec, Matt Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, and Gunnar Rätsch · 2021
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Have we learned to explain?: How interpretability methods can learn to encode predictions in their interpretations
Neil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs, and Rajesh Ranganath · 2021
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Highly accurate protein structure prediction with alphafold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, and Demis Hassabis · 2021
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Controlled text generation as continuous optimization with multiple constraints
Sachin Kumar, Eric Malmi, Aliaksei Severyn, and Yulia Tsvetkov · 2021
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Large language models can be strong differentially private learners
Xuechen Li, Florian Tramer, Percy Liang, and Tatsunori Hashimoto · 2021
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Machine learning for precision medicine
Sarah J MacEachern and Nils D Forkert · 2021
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T-DPSOM: An interpretable clustering method for unsupervised learning of patient health states
Laura Manduchi, Matthias Hüser, Martin Faltys, Julia Vogt, Gunnar Rätsch, and Vincent Fortuin · 2021
Cited alongside, same era.
An identifiable double vae for disentangled representations
Graziano Mita, Maurizio Filippone, and Pietro Michiardi · 2021
Cited alongside, same era.
Diffusion based representation learning
Sarthak Mittal, Korbinian Abstreiter, Stefan Bauer, Bernhard Schölkopf, and Arash Mehrjou · 2021
Cited alongside, same era.
Manipulating and measuring model interpretability
Forough Poursabzi-Sangdeh, Daniel G Goldstein, Jake M Hofman, Jennifer Wortman Wortman Vaughan, and Hanna Wallach · 2021
Cited alongside, same era.
Robustness and generalization via generative adversarial training
Omid Poursaeed, Tianxing Jiang, Harry Yang, Serge J. Belongie, and Ser-Nam Lim · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Flow matching for generative modeling, 2023
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le · 2023
Later among the works it cites.
Chameleon: Plug-and-play compositional reasoning with large language models
Pan Lu, Baolin Peng, Hao Cheng, Michel Galley, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, and Jianfeng Gao · 2023
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Bayesian mri reconstruction with joint uncertainty estimation using diffusion models
Guanxiong Luo, Moritz Blumenthal, Martin Heide, and Martin Uecker · 2023
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Tree variational autoencoders
Laura Manduchi, Moritz Vandenhirtz, Alain Ryser, and Julia E Vogt · 2023
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On distillation of guided diffusion models
Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik Kingma, Stefano Ermon, Jonathan Ho, and Tim Salimans · 2023
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
Cited alongside, same era.
Scaling language models: Methods, analysis & insights from training gopher
Jack W Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, et al · 2021
Cited alongside, same era.
Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
Cited alongside, same era.
Detecting out-of-distribution samples via variational auto-encoder with reliable uncertainty estimation
Xuming Ran, Mingkun Xu, Lingrui Mei, Qi Xu, and Quanying Liu · 2021
Cited alongside, same era.
Skilful precipitation nowcasting using deep generative models of radar
Suman Ravuri, Karel Lenc, Matthew Willson, Dmitry Kangin, Remi Lam, Piotr Mirowski, Megan Fitzsimons, Maria Athanassiadou, Sheleem Kashem, Sam Madge, Rachel Prudden, Amol Mandhane, Aidan Clark, Andrew Brock, Karen Simonyan, Raia Hadsell, Niall Robinson, Ellen Clancy, Alberto Arribas, and Shakir Mohamed · 2021
Cited alongside, same era.
Evaluating the interpretability of generative models by interactive reconstruction
Andrew Ross, Nina Chen, Elisa Zhao Hang, Elena L. Glassman, and Finale Doshi-Velez · 2021
Cited alongside, same era.
How good is your tokenizer? on the monolingual performance of multilingual language models
Phillip Rust, Jonas Pfeiffer, Ivan Vulić, Sebastian Ruder, and Iryna Gurevych · 2021
Cited alongside, same era.
The imperative for regulatory oversight of large language models (or generative ai) in healthcare
Bertalan Meskó and Eric J. Topol · 2023
Later among the works it cites.
Embedding hard physical constraints in neural network coarse-graining of three-dimensional turbulence
Arvind Mohan, Nicholas Lubbers, Misha Chertkov, and Daniel Livescu · 2023
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Measuring axiomatic soundness of counterfactual image models
Miguel Monteiro, Fabio De Sousa Ribeiro, Nick Pawlowski, Daniel C. Castro, and Ben Glocker · 2023
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Scalable extraction of training data from (production) language models
Milad Nasr, Nicholas Carlini, Jonathan Hayase, Matthew Jagielski, A. Feder Cooper, Daphne Ippolito, Christopher A. Choquette-Choo, Eric Wallace, Florian Tramèr, and Katherine Lee · 2023
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OpenAI · 2023
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Epistemic neural networks
Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, and Benjamin Van Roy · 2023
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Bias and unfairness in machine learning models: A systematic review on datasets, tools, fairness metrics, and identification and mitigation methods
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