1 code implementation • ICML 2020 • Jiaming Song, Stefano Ermon
Generative adversarial networks (GANs) variants approximately minimize divergences between the model and the data distribution using a discriminator.
no code implementations • 23 Mar 2023 • Bram Wallace, Akash Gokul, Stefano Ermon, Nikhil Naik
Classifier guidance -- using the gradients of an image classifier to steer the generations of a diffusion model -- has the potential to dramatically expand the creative control over image generation and editing.
no code implementations • 17 Mar 2023 • Can Qin, Ning Yu, Chen Xing, Shu Zhang, Zeyuan Chen, Stefano Ermon, Yun Fu, Caiming Xiong, ran Xu
Empirical results show that GlueNet can be trained efficiently and enables various capabilities beyond previous state-of-the-art models: 1) multilingual language models such as XLM-Roberta can be aligned with existing T2I models, allowing for the generation of high-quality images from captions beyond English; 2) GlueNet can align multi-modal encoders such as AudioCLIP with the Stable Diffusion model, enabling sound-to-image generation; 3) it can also upgrade the current text encoder of the latent diffusion model for challenging case generation.
no code implementations • 16 Mar 2023 • Shu Zhang, Xinyi Yang, Yihao Feng, Can Qin, Chia-Chih Chen, Ning Yu, Zeyuan Chen, Huan Wang, Silvio Savarese, Stefano Ermon, Caiming Xiong, ran Xu
Incorporating human feedback has been shown to be crucial to align text generated by large language models to human preferences.
no code implementations • 5 Mar 2023 • Lantao Yu, Tianhe Yu, Jiaming Song, Willie Neiswanger, Stefano Ermon
In this case, a well-known issue is the distribution shift between the learned policy and the behavior policy that collects the offline data.
1 code implementation • 21 Feb 2023 • Michael Poli, Stefano Massaroli, Eric Nguyen, Daniel Y. Fu, Tri Dao, Stephen Baccus, Yoshua Bengio, Stefano Ermon, Christopher Ré
Recent advances in deep learning have relied heavily on the use of large Transformers due to their ability to learn at scale.
Ranked #25 on
Language Modelling
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1 code implementation • 7 Feb 2023 • Andy Shih, Dorsa Sadigh, Stefano Ermon
LHTS is compatible with all likelihood-based models, and optimizes for the long-horizon likelihood of samples.
no code implementations • 30 Jan 2023 • Naoki Murata, Koichi Saito, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon
Pre-trained diffusion models have been successfully used as priors in a variety of linear inverse problems, where the goal is to reconstruct a signal from noisy linear measurements.
no code implementations • 5 Jan 2023 • Divyansh Garg, Joey Hejna, Matthieu Geist, Stefano Ermon
Using EVT, we derive our \emph{Extreme Q-Learning} framework and consequently online and, for the first time, offline MaxEnt Q-learning algorithms, that do not explicitly require access to a policy or its entropy.
no code implementations • 4 Jan 2023 • Enci Liu, Chenlin Meng, Matthew Kolodner, Eun Jee Sung, Sihang Chen, Marshall Burke, David Lobell, Stefano Ermon
In this paper, we propose a method for estimating building coverage using only publicly available low-resolution satellite imagery that is more frequently updated.
no code implementations • 24 Dec 2022 • Linqi Zhou, Michael Poli, Winnie Xu, Stefano Massaroli, Stefano Ermon
Methods based on ordinary differential equations (ODEs) are widely used to build generative models of time-series.
1 code implementation • 26 Nov 2022 • Michael Poli, Stefano Massaroli, Federico Berto, Jinykoo Park, Tri Dao, Christopher Ré, Stefano Ermon
Instead, this work introduces a blueprint for frequency domain learning through a single transform: transform once (T1).
1 code implementation • 3 Nov 2022 • Muyang Li, Ji Lin, Chenlin Meng, Stefano Ermon, Song Han, Jun-Yan Zhu
With 1. 2%-area edited regions, our method reduces the computation of DDIM by 7. 5$\times$ and GauGAN by 18$\times$ while preserving the visual fidelity.
no code implementations • 2 Nov 2022 • Chenlin Meng, Kristy Choi, Jiaming Song, Stefano Ermon
To this end, we propose an analogous score function called the "Concrete score", a generalization of the (Stein) score for discrete settings.
no code implementations • 22 Oct 2022 • Kristy Choi, Chris Cundy, Sanjari Srivastava, Stefano Ermon
Particularly in low-data regimes, an outstanding challenge in machine learning is developing principled techniques for augmenting our models with suitable priors.
no code implementations • 9 Oct 2022 • Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon
Score-based generative models learn a family of noise-conditional score functions corresponding to the data density perturbed with increasingly large amounts of noise.
1 code implementation • 6 Oct 2022 • Viraj Mehta, Ian Char, Joseph Abbate, Rory Conlin, Mark D. Boyer, Stefano Ermon, Jeff Schneider, Willie Neiswanger
In this work, we develop a method that allows us to plan for exploration while taking both the task and the current knowledge about the dynamics into account.
1 code implementation • 6 Oct 2022 • Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik P. Kingma, Stefano Ermon, Jonathan Ho, Tim Salimans
For standard diffusion models trained on the pixel-space, our approach is able to generate images visually comparable to that of the original model using as few as 4 sampling steps on ImageNet 64x64 and CIFAR-10, achieving FID/IS scores comparable to that of the original model while being up to 256 times faster to sample from.
no code implementations • 4 Oct 2022 • Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon
Bayesian optimization (BO) is a popular method for efficiently inferring optima of an expensive black-box function via a sequence of queries.
1 code implementation • 29 Sep 2022 • Gengchen Mai, Chiyu Jiang, Weiwei Sun, Rui Zhu, Yao Xuan, Ling Cai, Krzysztof Janowicz, Stefano Ermon, Ni Lao
For the spatial domain approach, we propose ResNet1D, a 1D CNN-based polygon encoder, which uses circular padding to achieve loop origin invariance on simple polygons.
no code implementations • 28 Sep 2022 • Chenlin Meng, Linqi Zhou, Kristy Choi, Tri Dao, Stefano Ermon
Normalizing flows model complex probability distributions using maps obtained by composing invertible layers.
1 code implementation • 23 Sep 2022 • Bahjat Kawar, Jiaming Song, Stefano Ermon, Michael Elad
Diffusion models can be used as learned priors for solving various inverse problems.
1 code implementation • 13 Sep 2022 • Yann Dubois, Tatsunori Hashimoto, Stefano Ermon, Percy Liang
For non-contrastive learning, we use our framework to derive a simple and novel objective.
no code implementations • 10 Sep 2022 • Sara A. Miskovich, Willie Neiswanger, William Colocho, Claudio Emma, Jacqueline Garrahan, Timothy Maxwell, Christopher Mayes, Stefano Ermon, Auralee Edelen, Daniel Ratner
Traditional black-box optimizers such as Bayesian optimization are slow and inefficient when dealing with such objectives as they must acquire the full series of measurements, but return only the emittance, with each query.
no code implementations • 17 Jul 2022 • Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu, Erik Rozi, Yutong He, Marshall Burke, David B. Lobell, Stefano Ermon
Unsupervised pre-training methods for large vision models have shown to enhance performance on downstream supervised tasks.
1 code implementation • 27 Jun 2022 • Jiaming Song, Lantao Yu, Willie Neiswanger, Stefano Ermon
To extend BO to a broader class of models and utilities, we propose likelihood-free BO (LFBO), an approach based on likelihood-free inference.
no code implementations • 23 Jun 2022 • Charles Marx, Shengjia Zhao, Willie Neiswanger, Stefano Ermon
We introduce a versatile class of algorithms for recalibration in regression that we call Modular Conformal Calibration (MCC).
1 code implementation • 9 Jun 2022 • Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Parrish, Allen Nie, Aman Hussain, Amanda Askell, Amanda Dsouza, Ambrose Slone, Ameet Rahane, Anantharaman S. Iyer, Anders Andreassen, Andrea Madotto, Andrea Santilli, Andreas Stuhlmüller, Andrew Dai, Andrew La, Andrew Lampinen, Andy Zou, Angela Jiang, Angelica Chen, Anh Vuong, Animesh Gupta, Anna Gottardi, Antonio Norelli, Anu Venkatesh, Arash Gholamidavoodi, Arfa Tabassum, Arul Menezes, Arun Kirubarajan, Asher Mullokandov, Ashish Sabharwal, Austin Herrick, Avia Efrat, Aykut Erdem, Ayla Karakaş, B. Ryan Roberts, Bao Sheng Loe, Barret Zoph, Bartłomiej Bojanowski, Batuhan Özyurt, Behnam Hedayatnia, Behnam Neyshabur, Benjamin Inden, Benno Stein, Berk Ekmekci, Bill Yuchen Lin, Blake Howald, Cameron Diao, Cameron Dour, Catherine Stinson, Cedrick Argueta, César Ferri Ramírez, Chandan Singh, Charles Rathkopf, Chenlin Meng, Chitta Baral, Chiyu Wu, Chris Callison-Burch, Chris Waites, Christian Voigt, Christopher D. Manning, Christopher Potts, Cindy Ramirez, Clara E. Rivera, Clemencia Siro, Colin Raffel, Courtney Ashcraft, Cristina Garbacea, Damien Sileo, Dan Garrette, Dan Hendrycks, Dan Kilman, Dan Roth, Daniel Freeman, Daniel Khashabi, Daniel Levy, Daniel Moseguí González, Danielle Perszyk, Danny Hernandez, Danqi Chen, Daphne Ippolito, Dar Gilboa, David Dohan, David Drakard, David Jurgens, Debajyoti Datta, Deep Ganguli, Denis Emelin, Denis Kleyko, Deniz Yuret, Derek Chen, Derek Tam, Dieuwke Hupkes, Diganta Misra, Dilyar Buzan, Dimitri Coelho Mollo, Diyi Yang, Dong-Ho Lee, Ekaterina Shutova, Ekin Dogus Cubuk, Elad Segal, Eleanor Hagerman, Elizabeth Barnes, Elizabeth Donoway, Ellie Pavlick, Emanuele Rodola, Emma Lam, Eric Chu, Eric Tang, Erkut Erdem, Ernie Chang, Ethan A. Chi, Ethan Dyer, Ethan Jerzak, Ethan Kim, Eunice Engefu Manyasi, Evgenii Zheltonozhskii, Fanyue Xia, Fatemeh Siar, Fernando Martínez-Plumed, Francesca Happé, Francois Chollet, Frieda Rong, Gaurav Mishra, Genta Indra Winata, Gerard de Melo, Germán Kruszewski, Giambattista Parascandolo, Giorgio Mariani, Gloria Wang, Gonzalo Jaimovitch-López, Gregor Betz, Guy Gur-Ari, Hana Galijasevic, Hannah Kim, Hannah Rashkin, Hannaneh Hajishirzi, Harsh Mehta, Hayden Bogar, Henry Shevlin, Hinrich Schütze, Hiromu Yakura, Hongming Zhang, Hugh Mee Wong, Ian Ng, Isaac Noble, Jaap Jumelet, Jack Geissinger, Jackson Kernion, Jacob Hilton, Jaehoon Lee, Jaime Fernández Fisac, James B. Simon, James Koppel, James Zheng, James Zou, Jan Kocoń, Jana Thompson, Jared Kaplan, Jarema Radom, Jascha Sohl-Dickstein, Jason Phang, Jason Wei, Jason Yosinski, Jekaterina Novikova, Jelle Bosscher, Jennifer Marsh, Jeremy Kim, Jeroen Taal, Jesse Engel, Jesujoba Alabi, Jiacheng Xu, Jiaming Song, Jillian Tang, Joan Waweru, John Burden, John Miller, John U. Balis, Jonathan Berant, Jörg Frohberg, Jos Rozen, Jose Hernandez-Orallo, Joseph Boudeman, Joseph Jones, Joshua B. Tenenbaum, Joshua S. Rule, Joyce Chua, Kamil Kanclerz, Karen Livescu, Karl Krauth, Karthik Gopalakrishnan, Katerina Ignatyeva, Katja Markert, Kaustubh D. Dhole, Kevin Gimpel, Kevin Omondi, Kory Mathewson, Kristen Chiafullo, Ksenia Shkaruta, Kumar Shridhar, Kyle McDonell, Kyle Richardson, Laria Reynolds, Leo Gao, Li Zhang, Liam Dugan, Lianhui Qin, Lidia Contreras-Ochando, Louis-Philippe Morency, Luca Moschella, Lucas Lam, Lucy Noble, Ludwig Schmidt, Luheng He, Luis Oliveros Colón, Luke Metz, Lütfi Kerem Şenel, Maarten Bosma, Maarten Sap, Maartje ter Hoeve, Maheen Farooqi, Manaal Faruqui, Mantas Mazeika, Marco Baturan, Marco Marelli, Marco Maru, Maria Jose Ramírez Quintana, Marie Tolkiehn, Mario Giulianelli, Martha Lewis, Martin Potthast, Matthew L. Leavitt, Matthias Hagen, Mátyás Schubert, Medina Orduna Baitemirova, Melody Arnaud, Melvin McElrath, Michael A. Yee, Michael Cohen, Michael Gu, Michael Ivanitskiy, Michael Starritt, Michael Strube, Michał Swędrowski, Michele Bevilacqua, Michihiro Yasunaga, Mihir Kale, Mike Cain, Mimee Xu, Mirac Suzgun, Mo Tiwari, Mohit Bansal, Moin Aminnaseri, Mor Geva, Mozhdeh Gheini, Mukund Varma T, Nanyun Peng, Nathan Chi, Nayeon Lee, Neta Gur-Ari Krakover, Nicholas Cameron, Nicholas Roberts, Nick Doiron, Nikita Nangia, Niklas Deckers, Niklas Muennighoff, Nitish Shirish Keskar, Niveditha S. Iyer, Noah Constant, Noah Fiedel, Nuan Wen, Oliver Zhang, Omar Agha, Omar Elbaghdadi, Omer Levy, Owain Evans, Pablo Antonio Moreno Casares, Parth Doshi, Pascale Fung, Paul Pu Liang, Paul Vicol, Pegah Alipoormolabashi, Peiyuan Liao, Percy Liang, Peter Chang, Peter Eckersley, Phu Mon Htut, Pinyu Hwang, Piotr Miłkowski, Piyush Patil, Pouya Pezeshkpour, Priti Oli, Qiaozhu Mei, Qing Lyu, Qinlang Chen, Rabin Banjade, Rachel Etta Rudolph, Raefer Gabriel, Rahel Habacker, Ramón Risco Delgado, Raphaël Millière, Rhythm Garg, Richard Barnes, Rif A. Saurous, Riku Arakawa, Robbe Raymaekers, Robert Frank, Rohan Sikand, Roman Novak, Roman Sitelew, Ronan LeBras, Rosanne Liu, Rowan Jacobs, Rui Zhang, Ruslan Salakhutdinov, Ryan Chi, Ryan Lee, Ryan Stovall, Ryan Teehan, Rylan Yang, Sahib Singh, Saif M. Mohammad, Sajant Anand, Sam Dillavou, Sam Shleifer, Sam Wiseman, Samuel Gruetter, Samuel R. Bowman, Samuel S. Schoenholz, Sanghyun Han, Sanjeev Kwatra, Sarah A. Rous, Sarik Ghazarian, Sayan Ghosh, Sean Casey, Sebastian Bischoff, Sebastian Gehrmann, Sebastian Schuster, Sepideh Sadeghi, Shadi Hamdan, Sharon Zhou, Shashank Srivastava, Sherry Shi, Shikhar Singh, Shima Asaadi, Shixiang Shane Gu, Shubh Pachchigar, Shubham Toshniwal, Shyam Upadhyay, Shyamolima, Debnath, Siamak Shakeri, Simon Thormeyer, Simone Melzi, Siva Reddy, Sneha Priscilla Makini, Soo-Hwan Lee, Spencer Torene, Sriharsha Hatwar, Stanislas Dehaene, Stefan Divic, Stefano Ermon, Stella Biderman, Stephanie Lin, Stephen Prasad, Steven T. Piantadosi, Stuart M. Shieber, Summer Misherghi, Svetlana Kiritchenko, Swaroop Mishra, Tal Linzen, Tal Schuster, Tao Li, Tao Yu, Tariq Ali, Tatsu Hashimoto, Te-Lin Wu, Théo Desbordes, Theodore Rothschild, Thomas Phan, Tianle Wang, Tiberius Nkinyili, Timo Schick, Timofei Kornev, Timothy Telleen-Lawton, Titus Tunduny, Tobias Gerstenberg, Trenton Chang, Trishala Neeraj, Tushar Khot, Tyler Shultz, Uri Shaham, Vedant Misra, Vera Demberg, Victoria Nyamai, Vikas Raunak, Vinay Ramasesh, Vinay Uday Prabhu, Vishakh Padmakumar, Vivek Srikumar, William Fedus, William Saunders, William Zhang, Wout Vossen, Xiang Ren, Xiaoyu Tong, Xinran Zhao, Xinyi Wu, Xudong Shen, Yadollah Yaghoobzadeh, Yair Lakretz, Yangqiu Song, Yasaman Bahri, Yejin Choi, Yichi Yang, Yiding Hao, Yifu Chen, Yonatan Belinkov, Yu Hou, Yufang Hou, Yuntao Bai, Zachary Seid, Zhuoye Zhao, Zijian Wang, Zijie J. Wang, ZiRui Wang, Ziyi Wu
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
2 code implementations • 27 May 2022 • Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, Christopher Ré
We also extend FlashAttention to block-sparse attention, yielding an approximate attention algorithm that is faster than any existing approximate attention method.
1 code implementation • 26 May 2022 • Andy Shih, Dorsa Sadigh, Stefano Ermon
Conditional inference on arbitrary subsets of variables is a core problem in probabilistic inference with important applications such as masked language modeling and image inpainting.
no code implementations • 15 Apr 2022 • Michael Poli, Winnie Xu, Stefano Massaroli, Chenlin Meng, Kuno Kim, Stefano Ermon
We investigate how to leverage the representations produced by Neural Collages in various tasks, including data compression and generation.
no code implementations • 4 Apr 2022 • Yutong He, William Zhang, Chenlin Meng, Marshall Burke, David B. Lobell, Stefano Ermon
Automated tracking of urban development in areas where construction information is not available became possible with recent advancements in machine learning and remote sensing.
1 code implementation • 22 Mar 2022 • Benedikt Boecking, Nicholas Roberts, Willie Neiswanger, Stefano Ermon, Frederic Sala, Artur Dubrawski
The model outperforms baseline weak supervision label models on a number of multiclass image classification datasets, improves the quality of generated images, and further improves end-model performance through data augmentation with synthetic samples.
1 code implementation • 16 Mar 2022 • Xuan Su, Jiaming Song, Chenlin Meng, Stefano Ermon
Image translation with DDIBs relies on two diffusion models trained independently on each domain, and is a two-step process: DDIBs first obtain latent encodings for source images with the source diffusion model, and then decode such encodings using the target model to construct target images.
1 code implementation • ICLR 2022 • Minkai Xu, Lantao Yu, Yang song, Chence Shi, Stefano Ermon, Jian Tang
GeoDiff treats each atom as a particle and learns to directly reverse the diffusion process (i. e., transforming from a noise distribution to stable conformations) as a Markov chain.
no code implementations • 28 Feb 2022 • Divyansh Garg, Skanda Vaidyanath, Kuno Kim, Jiaming Song, Stefano Ermon
Learning policies that effectively utilize language instructions in complex, multi-task environments is an important problem in sequential decision-making.
1 code implementation • 2 Feb 2022 • Mark Beliaev, Andy Shih, Stefano Ermon, Dorsa Sadigh, Ramtin Pedarsani
In this work, we show that unsupervised learning over demonstrator expertise can lead to a consistent boost in the performance of imitation learning algorithms.
1 code implementation • 27 Jan 2022 • Bahjat Kawar, Michael Elad, Stefano Ermon, Jiaming Song
Many interesting tasks in image restoration can be cast as linear inverse problems.
no code implementations • 5 Jan 2022 • Andy Shih, Stefano Ermon, Dorsa Sadigh
In this work, we study the problem of conditional multi-agent imitation learning, where we have access to joint trajectory demonstrations at training time, and we must interact with and adapt to new partners at test time.
1 code implementation • 16 Dec 2021 • Chenlin Meng, Enci Liu, Willie Neiswanger, Jiaming Song, Marshall Burke, David Lobell, Stefano Ermon
We show empirically that the proposed framework achieves strong performance on estimating the number of buildings in the United States and Africa, cars in Kenya, brick kilns in Bangladesh, and swimming pools in the U. S., while requiring as few as 0. 01% of satellite images compared to an exhaustive approach.
no code implementations • 12 Dec 2021 • Volodymyr Kuleshov, Evgenii Nikishin, Shantanu Thakoor, Tingfung Lau, Stefano Ermon
In this work, we seek to understand and extend adversarial examples across domains in which inputs are discrete, particularly across new domains, such as computational biology.
1 code implementation • 9 Dec 2021 • Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger
In particular, we leverage ideas from Bayesian optimal experimental design to guide the selection of state-action queries for efficient learning.
no code implementations • 7 Dec 2021 • Lantao Yu, Yujia Jin, Stefano Ermon
Binary density ratio estimation (DRE), the problem of estimating the ratio $p_1/p_2$ given their empirical samples, provides the foundation for many state-of-the-art machine learning algorithms such as contrastive representation learning and covariate shift adaptation.
1 code implementation • NeurIPS 2021 • Chris Cundy, Aditya Grover, Stefano Ermon
We propose Bayesian Causal Discovery Nets (BCD Nets), a variational inference framework for estimating a distribution over DAGs characterizing a linear-Gaussian SEM.
1 code implementation • NeurIPS 2021 • Andy Shih, Dorsa Sadigh, Stefano Ermon
Probabilistic circuits (PCs) are a family of generative models which allows for the computation of exact likelihoods and marginals of its probability distributions.
1 code implementation • NeurIPS 2021 • Abhishek Sinha, Jiaming Song, Chenlin Meng, Stefano Ermon
Conditional generative models of high-dimensional images have many applications, but supervision signals from conditions to images can be expensive to acquire.
no code implementations • NeurIPS 2021 • Roshni Sahoo, Shengjia Zhao, Alyssa Chen, Stefano Ermon
We propose a stronger notion of calibration called threshold calibration, which is exactly the condition required to ensure that decision loss is predicted accurately for threshold decisions.
1 code implementation • 22 Nov 2021 • Kristy Choi, Chenlin Meng, Yang song, Stefano Ermon
We then estimate the instantaneous rate of change of the bridge distributions indexed by time (the "time score") -- a quantity defined analogously to data (Stein) scores -- with a novel time score matching objective.
1 code implementation • NeurIPS Workshop Deep_Invers 2021 • Yang song, Liyue Shen, Lei Xing, Stefano Ermon
These measurements are typically synthesized from images using a fixed physical model of the measurement process, which hinders the generalization capability of models to unknown measurement processes.
1 code implementation • 8 Nov 2021 • Christopher Yeh, Chenlin Meng, Sherrie Wang, Anne Driscoll, Erik Rozi, Patrick Liu, Jihyeon Lee, Marshall Burke, David B. Lobell, Stefano Ermon
Our goals for SustainBench are to (1) lower the barriers to entry for the machine learning community to contribute to measuring and achieving the SDGs; (2) provide standard benchmarks for evaluating machine learning models on tasks across a variety of SDGs; and (3) encourage the development of novel machine learning methods where improved model performance facilitates progress towards the SDGs.
no code implementations • NeurIPS 2021 • Chenlin Meng, Yang song, Wenzhe Li, Stefano Ermon
By leveraging Tweedie's formula on higher order moments, we generalize denoising score matching to estimate higher order derivatives.
no code implementations • NeurIPS 2021 • Lantao Yu, Jiaming Song, Yang song, Stefano Ermon
Energy-based models (EBMs) offer flexible distribution parametrization.
no code implementations • 29 Sep 2021 • Willie Neiswanger, Lantao Yu, Shengjia Zhao, Chenlin Meng, Stefano Ermon
For special cases of the loss and design space, we develop gradient-based methods to efficiently optimize our proposed family of acquisition functions, and demonstrate that the resulting BO procedure shows strong empirical performance on a diverse set of optimization tasks.
no code implementations • 29 Sep 2021 • Gengchen Mai, Yao Xuan, Wenyun Zuo, Yutong He, Stefano Ermon, Jiaming Song, Krzysztof Janowicz, Ni Lao
Location encoding is valuable for a multitude of tasks where both the absolute positions and local contexts (image, text, and other types of metadata) of spatial objects are needed for accurate predictions.
no code implementations • ICLR 2022 • Viraj Mehta, Biswajit Paria, Jeff Schneider, Willie Neiswanger, Stefano Ermon
In particular, we leverage ideas from Bayesian optimal experimental design to guide the selection of state-action queries for efficient learning.
no code implementations • 29 Sep 2021 • Shengjia Zhao, Yusuke Tashiro, Danny Tse, Stefano Ermon
Accurate uncertainty quantification is a key building block of trustworthy machine learning systems.
no code implementations • 29 Sep 2021 • Rui Shu, Stefano Ermon
In this work, we consider the task of image generative modeling with variational autoencoders and posit that the nature of high-dimensional image data distributions poses an intrinsic challenge.
no code implementations • ICLR 2022 • Shengjia Zhao, Abhishek Sinha, Yutong He, Aidan Perreault, Jiaming Song, Stefano Ermon
Measuring the discrepancy between two probability distributions is a fundamental problem in machine learning and statistics.
no code implementations • 29 Sep 2021 • Fan-Yun Sun, Jonathan Kuck, Hao Tang, Stefano Ermon
Several indices used in a factor graph data structure can be permuted without changing the underlying probability distribution.
1 code implementation • 16 Aug 2021 • Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, aditi raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, Percy Liang
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
1 code implementation • ICLR 2022 • Chenlin Meng, Yutong He, Yang song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon
The key challenge is balancing faithfulness to the user input (e. g., hand-drawn colored strokes) and realism of the synthesized image.
no code implementations • NeurIPS 2021 • Shengjia Zhao, Michael P. Kim, Roshni Sahoo, Tengyu Ma, Stefano Ermon
In this work, we introduce a new notion -- \emph{decision calibration} -- that requires the predicted distribution and true distribution to be ``indistinguishable'' to a set of downstream decision-makers.
no code implementations • 10 Jul 2021 • Hongwei Wang, Lantao Yu, Zhangjie Cao, Stefano Ermon
Multi-agent imitation learning aims to train multiple agents to perform tasks from demonstrations by learning a mapping between observations and actions, which is essential for understanding physical, social, and team-play systems.
3 code implementations • NeurIPS 2021 • Yusuke Tashiro, Jiaming Song, Yang song, Stefano Ermon
In this paper, we propose Conditional Score-based Diffusion models for Imputation (CSDI), a novel time series imputation method that utilizes score-based diffusion models conditioned on observed data.
1 code implementation • 5 Jul 2021 • Kristy Choi, Madeline Liao, Stefano Ermon
Density ratio estimation serves as an important technique in the unsupervised machine learning toolbox.
3 code implementations • NeurIPS 2021 • Divyansh Garg, Shuvam Chakraborty, Chris Cundy, Jiaming Song, Matthieu Geist, Stefano Ermon
In many sequential decision-making problems (e. g., robotics control, game playing, sequential prediction), human or expert data is available containing useful information about the task.
Ranked #1 on
MuJoCo Games
on Walker2d
1 code implementation • NeurIPS 2021 • Yutong He, Dingjie Wang, Nicholas Lai, William Zhang, Chenlin Meng, Marshall Burke, David B. Lobell, Stefano Ermon
High-resolution satellite imagery has proven useful for a broad range of tasks, including measurement of global human population, local economic livelihoods, and biodiversity, among many others.
3 code implementations • 14 Jun 2021 • Tung Nguyen, Rui Shu, Tuan Pham, Hung Bui, Stefano Ermon
High-dimensional observations are a major challenge in the application of model-based reinforcement learning (MBRL) to real-world environments.
2 code implementations • 12 Jun 2021 • Abhishek Sinha, Jiaming Song, Chenlin Meng, Stefano Ermon
Conditional generative models of high-dimensional images have many applications, but supervision signals from conditions to images can be expensive to acquire.
no code implementations • NeurIPS 2021 • Mike Wu, Noah Goodman, Stefano Ermon
In traditional software programs, it is easy to trace program logic from variables back to input, apply assertion statements to block erroneous behavior, and compose programs together.
1 code implementation • 19 Apr 2021 • Willie Neiswanger, Ke Alexander Wang, Stefano Ermon
Given such an $\mathcal{A}$, and a prior distribution over $f$, we refer to the problem of inferring the output of $\mathcal{A}$ using $T$ evaluations as Bayesian Algorithm Execution (BAX).
1 code implementation • ICLR 2021 • Andy Shih, Arjun Sawhney, Jovana Kondic, Stefano Ermon, Dorsa Sadigh
Humans can quickly adapt to new partners in collaborative tasks (e. g. playing basketball), because they understand which fundamental skills of the task (e. g. how to dribble, how to shoot) carry over across new partners.
no code implementations • ICLR 2021 • Chenlin Meng, Jiaming Song, Yang song, Shengjia Zhao, Stefano Ermon
While autoregressive models excel at image compression, their sample quality is often lacking.
no code implementations • ICLR Workshop Neural_Compression 2021 • Abhishek Sinha, Jiaming Song, Stefano Ermon
We illustrate that with one set of representations, the hybrid approach is able to achieve good performance on multiple downstream tasks such as classification, reconstruction, and generation.
1 code implementation • ICLR 2021 • Yilun Xu, Yang song, Sahaj Garg, Linyuan Gong, Rui Shu, Aditya Grover, Stefano Ermon
Experimentally, we demonstrate in several image and audio generation tasks that sample quality degrades gracefully as we reduce the computational budget for sampling.
no code implementations • 22 Feb 2021 • Rachel Luo, Aadyot Bhatnagar, Yu Bai, Shengjia Zhao, Huan Wang, Caiming Xiong, Silvio Savarese, Stefano Ermon, Edward Schmerling, Marco Pavone
In this work, we propose the local calibration error (LCE) to span the gap between average and individual reliability.
no code implementations • 15 Feb 2021 • Berivan Isik, Kristy Choi, Xin Zheng, Tsachy Weissman, Stefano Ermon, H. -S. Philip Wong, Armin Alaghi
Compression and efficient storage of neural network (NN) parameters is critical for applications that run on resource-constrained devices.
2 code implementations • ICLR 2021 • Abhishek Sinha, Kumar Ayush, Jiaming Song, Burak Uzkent, Hongxia Jin, Stefano Ermon
Empirically, models trained with our method achieve improved conditional/unconditional image generation along with improved anomaly detection capabilities.
Ranked #6 on
Image Generation
on CIFAR-100
3 code implementations • NeurIPS 2021 • Yang song, Conor Durkan, Iain Murray, Stefano Ermon
Score-based diffusion models synthesize samples by reversing a stochastic process that diffuses data to noise, and are trained by minimizing a weighted combination of score matching losses.
Ranked #5 on
Image Generation
on ImageNet 32x32
(bpd metric)
no code implementations • 1 Jan 2021 • Tung Nguyen, Rui Shu, Tuan Pham, Hung Bui, Stefano Ermon
High-dimensional observations are a major challenge in the application of model-based reinforcement learning (MBRL) to real-world environments.
no code implementations • 1 Jan 2021 • Laëtitia Shao, Yang song, Stefano Ermon
Although deep neural networks are effective on supervised learning tasks, they have been shown to be brittle.
no code implementations • 1 Jan 2021 • Shengjia Zhao, Abhishek Sinha, Yutong He, Aidan Perreault, Jiaming Song, Stefano Ermon
Based on ideas from decision theory, we investigate a new class of discrepancies that are based on the optimal decision loss.
no code implementations • 30 Dec 2020 • Chris Cundy, Stefano Ermon
We consider the task of training a policy that maximizes reward while minimizing disclosure of certain sensitive state variables through the actions.
1 code implementation • NeurIPS 2021 • Robin Swezey, Aditya Grover, Bruno Charron, Stefano Ermon
A key challenge with machine learning approaches for ranking is the gap between the performance metrics of interest and the surrogate loss functions that can be optimized with gradient-based methods.
8 code implementations • ICLR 2021 • Yang song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole
Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9. 89 and FID of 2. 20, a competitive likelihood of 2. 99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model.
Ranked #11 on
Image Generation
on CIFAR-10
no code implementations • 20 Nov 2020 • Shuvam Chakraborty, Burak Uzkent, Kumar Ayush, Kumar Tanmay, Evan Sheehan, Stefano Ermon
Finally, we improve standard ImageNet pre-training by 1-3% by tuning available models on our subsets and pre-training on a dataset filtered from a larger scale dataset.
1 code implementation • ICCV 2021 • Kumar Ayush, Burak Uzkent, Chenlin Meng, Kumar Tanmay, Marshall Burke, David Lobell, Stefano Ermon
Contrastive learning methods have significantly narrowed the gap between supervised and unsupervised learning on computer vision tasks.
Ranked #1 on
Semantic Segmentation
on SpaceNet 1
(using extra training data)
no code implementations • 15 Nov 2020 • Shengjia Zhao, Stefano Ermon
Decision makers often need to rely on imperfect probabilistic forecasts.
no code implementations • NeurIPS 2020 • Chenlin Meng, Lantao Yu, Yang song, Jiaming Song, Stefano Ermon
To increase flexibility, we propose autoregressive conditional score models (AR-CSM) where we parameterize the joint distribution in terms of the derivatives of univariate log-conditionals (scores), which need not be normalized.
1 code implementation • NeurIPS 2020 • Andy Shih, Stefano Ermon
Inference in discrete graphical models with variational methods is difficult because of the inability to re-parameterize gradients of the Evidence Lower Bound (ELBO).
no code implementations • NeurIPS 2021 • Kuno Kim, Akshat Jindal, Yang song, Jiaming Song, Yanan Sui, Stefano Ermon
We propose a new framework for Imitation Learning (IL) via density estimation of the expert's occupancy measure followed by Maximum Occupancy Entropy Reinforcement Learning (RL) using the density as a reward.
no code implementations • NeurIPS Workshop DL-IG 2020 • Berivan Isik, Kristy Choi, Xin Zheng, H.-S. Philip Wong, Stefano Ermon, Tsachy Weissman, Armin Alaghi
Efficient compression and storage of neural network (NN) parameters is critical for resource-constrained, downstream machine learning applications.
14 code implementations • ICLR 2021 • Jiaming Song, Chenlin Meng, Stefano Ermon
Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample.
no code implementations • 5 Oct 2020 • Laëtitia Shao, Yang song, Stefano Ermon
From this observation, we develop a detection criteria for samples on which a classifier is likely to fail at test time.
no code implementations • 21 Aug 2020 • Rachel Luo, Shengjia Zhao, Jiaming Song, Jonathan Kuck, Stefano Ermon, Silvio Savarese
In an extensive empirical study, we find that our algorithm improves calibration on domain-shift benchmarks under the constraints of differential privacy.
3 code implementations • NeurIPS 2020 • Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, Christopher Re
A central problem in learning from sequential data is representing cumulative history in an incremental fashion as more data is processed.
Ranked #7 on
Sequential Image Classification
on Sequential MNIST
no code implementations • NeurIPS 2020 • Jiaming Song, Stefano Ermon
We demonstrate that the proposed approach is able to lead to better mutual information estimation, gain empirical improvements in unsupervised representation learning, and beat a current state-of-the-art knowledge distillation method over 10 out of 13 tasks.
1 code implementation • NeurIPS 2020 • Tianyu Pang, Kun Xu, Chongxuan Li, Yang song, Stefano Ermon, Jun Zhu
Several machine learning applications involve the optimization of higher-order derivatives (e. g., gradients of gradients) during training, which can be expensive in respect to memory and computation even with automatic differentiation.
no code implementations • NeurIPS 2020 • Jonathan Kuck, Shuvam Chakraborty, Hao Tang, Rachel Luo, Jiaming Song, Ashish Sabharwal, Stefano Ermon
Learned neural solvers have successfully been used to solve combinatorial optimization and decision problems.
no code implementations • 29 Jun 2020 • Anusri Pampari, Stefano Ermon
A probabilistic model is said to be calibrated if its predicted probabilities match the corresponding empirical frequencies.
1 code implementation • 23 Jun 2020 • Samarth Sinha, Jiaming Song, Animesh Garg, Stefano Ermon
The use of past experiences to accelerate temporal difference (TD) learning of value functions, or experience replay, is a key component in deep reinforcement learning.
1 code implementation • 18 Jun 2020 • Shengjia Zhao, Christopher Yeh, Stefano Ermon
We consider the problem of estimating confidence intervals for the mean of a random variable, where the goal is to produce the smallest possible interval for a given number of samples.
no code implementations • ICML 2020 • Shengjia Zhao, Tengyu Ma, Stefano Ermon
We show that calibration for individual samples is possible in the regression setup if the predictions are randomized, i. e. outputting randomized credible intervals.
8 code implementations • NeurIPS 2020 • Yang Song, Stefano Ermon
Score-based generative models can produce high quality image samples comparable to GANs, without requiring adversarial optimization.
Ranked #66 on
Image Generation
on CIFAR-10
1 code implementation • 15 Jun 2020 • Jihyeon Lee, Dylan Grosz, Burak Uzkent, Sicheng Zeng, Marshall Burke, David Lobell, Stefano Ermon
Major decisions from governments and other large organizations rely on measurements of the populace's well-being, but making such measurements at a broad scale is expensive and thus infrequent in much of the developing world.
no code implementations • 7 Jun 2020 • Kumar Ayush, Burak Uzkent, Kumar Tanmay, Marshall Burke, David Lobell, Stefano Ermon
The combination of high-resolution satellite imagery and machine learning have proven useful in many sustainability-related tasks, including poverty prediction, infrastructure measurement, and forest monitoring.
1 code implementation • ICLR 2021 • Sharon Zhou, Eric Zelikman, Fred Lu, Andrew Y. Ng, Gunnar Carlsson, Stefano Ermon
Learning disentangled representations is regarded as a fundamental task for improving the generalization, robustness, and interpretability of generative models.
5 code implementations • NeurIPS 2020 • Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Zou, Sergey Levine, Chelsea Finn, Tengyu Ma
We also characterize the trade-off between the gain and risk of leaving the support of the batch data.
no code implementations • 11 Apr 2020 • Han Lin Aung, Burak Uzkent, Marshall Burke, David Lobell, Stefano Ermon
Using satellite imaging can be a scalable and cost effective manner to perform the task of farm parcel delineation to collect this valuable data.
1 code implementation • NeurIPS 2020 • Yusuke Tashiro, Yang song, Stefano Ermon
Adversarial attacks often involve random perturbations of the inputs drawn from uniform or Gaussian distributions, e. g., to initialize optimization-based white-box attacks or generate update directions in black-box attacks.
1 code implementation • ICML 2020 • Lantao Yu, Yang song, Jiaming Song, Stefano Ermon
Experimental results demonstrate the superiority of f-EBM over contrastive divergence, as well as the benefits of training EBMs using f-divergences other than KL.
3 code implementations • 4 Mar 2020 • Chenlin Meng, Yang song, Jiaming Song, Stefano Ermon
Iterative Gaussianization is a fixed-point iteration procedure that can transform any continuous random vector into a Gaussian one.
1 code implementation • ICML 2020 • Rui Shu, Tung Nguyen, Yin-Lam Chow, Tuan Pham, Khoat Than, Mohammad Ghavamzadeh, Stefano Ermon, Hung H. Bui
High-dimensional observations and unknown dynamics are major challenges when applying optimal control to many real-world decision making tasks.
1 code implementation • 2 Mar 2020 • Chenhao Niu, Yang song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon
In particular, we design a permutation equivariant, multi-channel graph neural network to model the gradient of the data distribution at the input graph (a. k. a., the score function).
2 code implementations • CVPR 2020 • Burak Uzkent, Stefano Ermon
While high resolution images contain semantically more useful information than their lower resolution counterparts, processing them is computationally more expensive, and in some applications, e. g. remote sensing, they can be much more expensive to acquire.
1 code implementation • ICLR 2020 • Yilun Xu, Shengjia Zhao, Jiaming Song, Russell Stewart, Stefano Ermon
We propose a new framework for reasoning about information in complex systems.
1 code implementation • 10 Feb 2020 • Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon
Feedforward computation, such as evaluating a neural network or sampling from an autoregressive model, is ubiquitous in machine learning.
no code implementations • 5 Feb 2020 • Kumar Ayush, Burak Uzkent, Marshall Burke, David Lobell, Stefano Ermon
Accurate local-level poverty measurement is an essential task for governments and humanitarian organizations to track the progress towards improving livelihoods and distribute scarce resources.
3 code implementations • 14 Dec 2019 • Vishnu Sarukkai, Anirudh Jain, Burak Uzkent, Stefano Ermon
In contrast, we cast the problem of cloud removal as a conditional image synthesis challenge, and we propose a trainable spatiotemporal generator network (STGAN) to remove clouds.
Ranked #2 on
Cloud Removal
on SEN12MS-CR-TS
3 code implementations • 9 Dec 2019 • Burak Uzkent, Christopher Yeh, Stefano Ermon
Traditionally, an object detector is applied to every part of the scene of interest, and its accuracy and computational cost increases with higher resolution images.
1 code implementation • NeurIPS 2019 • Sawyer Birnbaum, Volodymyr Kuleshov, Zayd Enam, Pang Wei W. Koh, Stefano Ermon
Learning representations that accurately capture long-range dependencies in sequential inputs --- including text, audio, and genomic data --- is a key problem in deep learning.
Ranked #6 on
Audio Super-Resolution
on VCTK Multi-Speaker
no code implementations • 30 Nov 2019 • Y. Alex Kolchinski, Sharon Zhou, Shengjia Zhao, Mitchell Gordon, Stefano Ermon
Generative models have made immense progress in recent years, particularly in their ability to generate high quality images.
1 code implementation • NeurIPS 2019 • Jonathan Kuck, Tri Dao, Hamid Rezatofighi, Ashish Sabharwal, Stefano Ermon
Computing the permanent of a non-negative matrix is a core problem with practical applications ranging from target tracking to statistical thermodynamics.
1 code implementation • ICML 2020 • Kristy Choi, Aditya Grover, Trisha Singh, Rui Shu, Stefano Ermon
Real-world datasets are often biased with respect to key demographic factors such as race and gender.
1 code implementation • ICLR 2020 • Rui Shu, Yining Chen, Abhishek Kumar, Stefano Ermon, Ben Poole
Learning disentangled representations that correspond to factors of variation in real-world data is critical to interpretable and human-controllable machine learning.
1 code implementation • 22 Oct 2019 • Jiaming Song, Stefano Ermon
Generative adversarial networks (GANs) have enjoyed much success in learning high-dimensional distributions.
no code implementations • 21 Oct 2019 • Jiaming Song, Yang song, Stefano Ermon
Based on this insight, we propose to exploit in-batch dependencies for OoD detection.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
1 code implementation • ICLR 2020 • Jiaming Song, Stefano Ermon
Variational approaches based on neural networks are showing promise for estimating mutual information (MI) between high dimensional variables.
1 code implementation • ICML 2020 • Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon
We formalize the Domain Adaptive Imitation Learning (DAIL) problem, which is a unified framework for imitation learning in the presence of viewpoint, embodiment, and dynamics mismatch.
no code implementations • 25 Sep 2019 • Shengjia Zhao, Yang song, Stefano Ermon
Our defense draws inspiration from differential privacy, and is based on intentionally adding noise to the classifier's outputs to limit the attacker's knowledge about the parameters.
no code implementations • 25 Sep 2019 • Kun Ho Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, Stefano Ermon
Informally, CDIL is the process of learning how to perform a task optimally, given demonstrations of the task in a distinct domain.
1 code implementation • NeurIPS 2019 • Lantao Yu, Tianhe Yu, Chelsea Finn, Stefano Ermon
Critically, our model can infer rewards for new, structurally-similar tasks from a single demonstration.
Ranked #1 on
MuJoCo Games
on Sawyer Pusher
1 code implementation • 14 Sep 2019 • Sawyer Birnbaum, Volodymyr Kuleshov, Zayd Enam, Pang Wei Koh, Stefano Ermon
Learning representations that accurately capture long-range dependencies in sequential inputs -- including text, audio, and genomic data -- is a key problem in deep learning.
Ranked #2 on
Audio Super-Resolution
on Voice Bank corpus (VCTK)
1 code implementation • 30 Jul 2019 • Lantao Yu, Jiaming Song, Stefano Ermon
Reinforcement learning agents are prone to undesired behaviors due to reward mis-specification.
1 code implementation • NeurIPS 2019 • Yang Song, Chenlin Meng, Stefano Ermon
To demonstrate their flexibility, we show that our invertible neural networks are competitive with ResNets on MNIST and CIFAR-10 classification.
Ranked #4 on
Image Generation
on MNIST
11 code implementations • NeurIPS 2019 • Yang Song, Stefano Ermon
We introduce a new generative model where samples are produced via Langevin dynamics using gradients of the data distribution estimated with score matching.
Ranked #101 on
Image Generation
on CIFAR-10
2 code implementations • NeurIPS 2019 • Aditya Grover, Jiaming Song, Alekh Agarwal, Kenneth Tran, Ashish Kapoor, Eric Horvitz, Stefano Ermon
A standard technique to correct this bias is importance sampling, where samples from the model are weighted by the likelihood ratio under model and true distributions.
1 code implementation • 19 Jun 2019 • Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon
Estimates of predictive uncertainty are important for accurate model-based planning and reinforcement learning.
Model-based Reinforcement Learning
reinforcement-learning
+1
no code implementations • ICLR 2019 • Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, Stefano Ermon
Partial differential equations (PDEs) are widely used across the physical and computational sciences.
1 code implementation • ICLR Workshop DeepGenStruct 2019 • Aditya Grover, Christopher Chute, Rui Shu, Zhangjie Cao, Stefano Ermon
Given datasets from multiple domains, a key challenge is to efficiently exploit these data sources for modeling a target domain.
6 code implementations • 17 May 2019 • Yang Song, Sahaj Garg, Jiaxin Shi, Stefano Ermon
However, it has been so far limited to simple, shallow models or low-dimensional data, due to the difficulty of computing the Hessian of log-density functions.
3 code implementations • 7 May 2019 • Burak Uzkent, Evan Sheehan, Chenlin Meng, Zhongyi Tang, Marshall Burke, David Lobell, Stefano Ermon
Despite recent progress in computer vision, finegrained interpretation of satellite images remains challenging because of a lack of labeled training data.
no code implementations • 5 May 2019 • Evan Sheehan, Chenlin Meng, Matthew Tan, Burak Uzkent, Neal Jean, David Lobell, Marshall Burke, Stefano Ermon
Progress on the UN Sustainable Development Goals (SDGs) is hampered by a persistent lack of data regarding key social, environmental, and economic indicators, particularly in developing countries.
no code implementations • 4 May 2019 • Wenjie Hu, Jay Harshadbhai Patel, Zoe-Alanah Robert, Paul Novosad, Samuel Asher, Zhongyi Tang, Marshall Burke, David Lobell, Stefano Ermon
Millions of people worldwide are absent from their country's census.
no code implementations • 20 Apr 2019 • Xiao Chen, Thomas Navidi, Stefano Ermon, Ram Rajagopal
Distributed devices such as mobile phones can produce and store large amounts of data that can enhance machine learning models; however, this data may contain private information specific to the data owner that prevents the release of the data.
no code implementations • ICLR Workshop DeepGenStruct 2019 • Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric Horvitz, Stefano Ermon
A standard technique to correct this bias is by importance weighting samples from the model by the likelihood ratio under the model and true distributions.
1 code implementation • ICLR 2019 • Aditya Grover, Eric Wang, Aaron Zweig, Stefano Ermon
Sorting input objects is an important step in many machine learning pipelines.
no code implementations • 27 Feb 2019 • Rui Shu, Hung H. Bui, Jay Whang, Stefano Ermon
The recognition network in deep latent variable models such as variational autoencoders (VAEs) relies on amortized inference for efficient posterior approximation that can scale up to large datasets.