Search Results for author: Stefano Ermon

Found 215 papers, 115 papers with code

Bridging the Gap Between f-GANs and Wasserstein GANs

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.

Density Ratio Estimation Image Generation +1

End-to-End Diffusion Latent Optimization Improves Classifier Guidance

no code implementations23 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.

Denoising Image Generation

GlueGen: Plug and Play Multi-modal Encoders for X-to-image Generation

no code implementations17 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.

Image Generation

HIVE: Harnessing Human Feedback for Instructional Visual Editing

no code implementations16 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.

Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching

no code implementations5 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.

Continuous Control Imitation Learning

Long Horizon Temperature Scaling

1 code implementation7 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.


GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration

no code implementations30 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.

Blind Image Deblurring Denoising +1

Extreme Q-Learning: MaxEnt RL without Entropy

no code implementations5 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.

D4RL Offline RL +2

Building Coverage Estimation with Low-resolution Remote Sensing Imagery

no code implementations4 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.

Deep Latent State Space Models for Time-Series Generation

no code implementations24 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.

Time Series Analysis

Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models

1 code implementation3 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.

Concrete Score Matching: Generalized Score Matching for Discrete Data

no code implementations2 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.

Density Estimation

LMPriors: Pre-Trained Language Models as Task-Specific Priors

no code implementations22 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.

Causal Inference Common Sense Reasoning +2

Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation

no code implementations9 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.


Exploration via Planning for Information about the Optimal Trajectory

1 code implementation6 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.

Reinforcement Learning (RL)

On Distillation of Guided Diffusion Models

1 code implementation6 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.

Denoising Image Generation +1

Generalizing Bayesian Optimization with Decision-theoretic Entropies

no code implementations4 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.

Decision Making

Towards General-Purpose Representation Learning of Polygonal Geometries

1 code implementation29 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.

Representation Learning

ButterflyFlow: Building Invertible Layers with Butterfly Matrices

no code implementations28 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.

Density Estimation

Efficient Tuning of Particle Accelerator Emittance via Bayesian Algorithm Execution and Virtual Objectives

no code implementations10 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.

A General Recipe for Likelihood-free Bayesian Optimization

1 code implementation27 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.

Modular Conformal Calibration

no code implementations23 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).


Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

1 code implementation9 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.

Common Sense Reasoning Memorization

FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness

2 code implementations27 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.

Document Classification

Training and Inference on Any-Order Autoregressive Models the Right Way

1 code implementation26 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.

Image Inpainting Language Modelling +1

Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations

no code implementations15 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.

Data Compression

Tracking Urbanization in Developing Regions with Remote Sensing Spatial-Temporal Super-Resolution

no code implementations4 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.

Image Super-Resolution Object Tracking +1

Generative Modeling Helps Weak Supervision (and Vice Versa)

1 code implementation22 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.

Data Augmentation Image Classification

Dual Diffusion Implicit Bridges for Image-to-Image Translation

1 code implementation16 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.

Image-to-Image Translation Translation

GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation

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.

Drug Discovery

LISA: Learning Interpretable Skill Abstractions from Language

no code implementations28 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.

Imitation Learning Quantization

Imitation Learning by Estimating Expertise of Demonstrators

1 code implementation2 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.

Continuous Control Imitation Learning

Denoising Diffusion Restoration Models

1 code implementation27 Jan 2022 Bahjat Kawar, Michael Elad, Stefano Ermon, Jiaming Song

Many interesting tasks in image restoration can be cast as linear inverse problems.

Colorization Deblurring +4

Conditional Imitation Learning for Multi-Agent Games

no code implementations5 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.

Imitation Learning Tensor Decomposition

IS-COUNT: Large-scale Object Counting from Satellite Images with Covariate-based Importance Sampling

1 code implementation16 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.

Object Counting object-detection +1

Quantifying and Understanding Adversarial Examples in Discrete Input Spaces

no code implementations12 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.

Sentiment Analysis

An Experimental Design Perspective on Model-Based Reinforcement Learning

1 code implementation9 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.

Continuous Control Experimental Design +3

A Unified Framework for Multi-distribution Density Ratio Estimation

no code implementations7 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.

Density Ratio Estimation Representation Learning

BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery

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.

Causal Discovery Stochastic Optimization +1

HyperSPNs: Compact and Expressive Probabilistic Circuits

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.

Density Estimation

D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation

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.

Conditional Image Generation Image Manipulation +1

Reliable Decisions with Threshold Calibration

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.


Density Ratio Estimation via Infinitesimal Classification

1 code implementation22 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.

Classification Density Ratio Estimation +1

Solving Inverse Problems in Medical Imaging with Score-Based Generative Models

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.

Computed Tomography (CT)

SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning

1 code implementation8 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.

BIG-bench Machine Learning

Estimating High Order Gradients of the Data Distribution by Denoising

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.

Denoising Image Generation

H-Entropy Search: Generalizing Bayesian Optimization with a Decision-theoretic Uncertainty Measure

no code implementations29 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.

Sphere2Vec: Self-Supervised Location Representation Learning on Spherical Surfaces

no code implementations29 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.

Image Classification Representation Learning +1

An Experimental Design Perspective on Exploration in Reinforcement Learning

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.

Continuous Control Experimental Design +2

Provably Calibrated Regression Under Distribution Drift

no code implementations29 Sep 2021 Shengjia Zhao, Yusuke Tashiro, Danny Tse, Stefano Ermon

Accurate uncertainty quantification is a key building block of trustworthy machine learning systems.

regression Time Series Prediction

Mind Your Bits and Errors: Prioritizing the Bits that Matter in Variational Autoencoders

no code implementations29 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.

Equivariant Neural Network for Factor Graphs

no code implementations29 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.

Inductive Bias

On the Opportunities and Risks of Foundation Models

1 code implementation16 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.

Transfer Learning

SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations

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.

Denoising Image Generation

Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration

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.

Decision Making

Multi-Agent Imitation Learning with Copulas

no code implementations10 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.

Imitation Learning

CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation

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.

Image Generation Imputation +1

Featurized Density Ratio Estimation

1 code implementation5 Jul 2021 Kristy Choi, Madeline Liao, Stefano Ermon

Density ratio estimation serves as an important technique in the unsupervised machine learning toolbox.

Data Augmentation Density Ratio Estimation +1

IQ-Learn: Inverse soft-Q Learning for Imitation

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.

Atari Games Continuous Control +3

Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis

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.

Object Counting Super-Resolution

Temporal Predictive Coding For Model-Based Planning In Latent Space

3 code implementations14 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.

Model-based Reinforcement Learning Representation Learning

D2C: Diffusion-Denoising Models for Few-shot Conditional Generation

2 code implementations12 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.

Conditional Image Generation Denoising +2

Improving Compositionality of Neural Networks by Decoding Representations to Inputs

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.

Fairness Out-of-Distribution Detection

Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information

1 code implementation19 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).

Experimental Design Gaussian Processes

On the Critical Role of Conventions in Adaptive Human-AI Collaboration

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.

Hybrid Mutual Information Lower-bound Estimators for Representation Learning

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.

Representation Learning

Anytime Sampling for Autoregressive Models via Ordered Autoencoding

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.

Audio Generation

Local Calibration: Metrics and Recalibration

no code implementations22 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.

Decision Making Fairness

Neural Network Compression for Noisy Storage Devices

no code implementations15 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.

Neural Network Compression

Negative Data Augmentation

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.

Action Recognition Anomaly Detection +8

Maximum Likelihood Training of Score-Based Diffusion Models

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)

Data Augmentation Image Generation

Non-Markovian Predictive Coding For Planning In Latent Space

no code implementations1 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.

Model-based Reinforcement Learning Representation Learning

Understanding Classifiers with Generative Models

no code implementations1 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.

Two-sample testing

H-divergence: A Decision-Theoretic Discrepancy Measure for Two Sample Tests

no code implementations1 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.

Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients

no code implementations30 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.

Decision Making

PiRank: Scalable Learning To Rank via Differentiable Sorting

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.


Score-Based Generative Modeling through Stochastic Differential Equations

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.

Colorization Image Inpainting +1

Efficient Conditional Pre-training for Transfer Learning

no code implementations20 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.

Transfer Learning

Geography-Aware Self-Supervised Learning

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)

Contrastive Learning Image Classification +4

Autoregressive Score Matching

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.

Density Estimation Image Denoising +1

Probabilistic Circuits for Variational Inference in Discrete Graphical Models

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).

Variational Inference

Imitation with Neural Density Models

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.

Density Estimation Imitation Learning +2

Denoising Diffusion Implicit Models

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.

Denoising Image Generation

Understanding Classifier Mistakes with Generative Models

no code implementations5 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.

Two-sample testing

Privacy Preserving Recalibration under Domain Shift

no code implementations21 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.

Privacy Preserving

Multi-label Contrastive Predictive Coding

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.

Knowledge Distillation Multi-class Classification +3

Efficient Learning of Generative Models via Finite-Difference Score Matching

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.

Unsupervised Calibration under Covariate Shift

no code implementations29 Jun 2020 Anusri Pampari, Stefano Ermon

A probabilistic model is said to be calibrated if its predicted probabilities match the corresponding empirical frequencies.

Decision Making Domain Adaptation

Experience Replay with Likelihood-free Importance Weights

1 code implementation23 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.

OpenAI Gym reinforcement-learning +1

A Framework for Sample Efficient Interval Estimation with Control Variates

1 code implementation18 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.


Individual Calibration with Randomized Forecasting

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.

Decision Making Fairness +1

Improved Techniques for Training Score-Based Generative Models

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.

Image Generation

Predicting Livelihood Indicators from Community-Generated Street-Level Imagery

1 code implementation15 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.

Efficient Poverty Mapping using Deep Reinforcement Learning

no code implementations7 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.

object-detection Object Detection +2

Evaluating the Disentanglement of Deep Generative Models through Manifold Topology

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.


Farmland Parcel Delineation Using Spatio-temporal Convolutional Networks

no code implementations11 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.

Diversity can be Transferred: Output Diversification for White- and Black-box Attacks

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.

Training Deep Energy-Based Models with f-Divergence Minimization

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.

Gaussianization Flows

3 code implementations4 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.

Predictive Coding for Locally-Linear Control

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.

Decision Making

Permutation Invariant Graph Generation via Score-Based Generative Modeling

1 code implementation2 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).

Graph Generation

Learning When and Where to Zoom with Deep Reinforcement Learning

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.

reinforcement-learning Reinforcement Learning (RL)

Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving

1 code implementation10 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.

Generating Interpretable Poverty Maps using Object Detection in Satellite Images

no code implementations5 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.

Feature Importance Humanitarian +2

Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks

3 code implementations14 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.

Cloud Removal Image Generation +1

Efficient Object Detection in Large Images using Deep Reinforcement Learning

3 code implementations9 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.

object-detection Object Detection +2

Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations.

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.

Audio Super-Resolution Super-Resolution +2

Approximating Human Judgment of Generated Image Quality

no code implementations30 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.

Approximating the Permanent by Sampling from Adaptive Partitions

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.

Fair Generative Modeling via Weak Supervision

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.

Image Generation

Weakly Supervised Disentanglement with Guarantees

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.


Bridging the Gap Between $f$-GANs and Wasserstein GANs

1 code implementation22 Oct 2019 Jiaming Song, Stefano Ermon

Generative adversarial networks (GANs) have enjoyed much success in learning high-dimensional distributions.

Image Generation

Understanding the Limitations of Variational Mutual Information Estimators

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.

Domain Adaptive Imitation Learning

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.

Imitation Learning

Towards Certified Defense for Unrestricted Adversarial Attacks

no code implementations25 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.

Adversarial Attack

Cross Domain Imitation Learning

no code implementations25 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.

Imitation Learning

Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations

1 code implementation14 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.

Audio Super-Resolution Super-Resolution +2

Multi-Agent Adversarial Inverse Reinforcement Learning

1 code implementation30 Jul 2019 Lantao Yu, Jiaming Song, Stefano Ermon

Reinforcement learning agents are prone to undesired behaviors due to reward mis-specification.

reinforcement-learning Reinforcement Learning (RL)

MintNet: Building Invertible Neural Networks with Masked Convolutions

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.

Image Generation

Generative Modeling by Estimating Gradients of the Data Distribution

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.

Image Inpainting

Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting

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.

Data Augmentation

Learning Neural PDE Solvers with Convergence Guarantees

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.

Sliced Score Matching: A Scalable Approach to Density and Score Estimation

6 code implementations17 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.

Variational Inference

Learning to Interpret Satellite Images in Global Scale Using Wikipedia

3 code implementations7 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.

Predicting Economic Development using Geolocated Wikipedia Articles

no code implementations5 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.

Distributed generation of privacy preserving data with user customization

no code implementations20 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.

Privacy Preserving

Bias Correction of Learned Generative Models via Likelihood-free Importance Weighting

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.

Data Augmentation

Training Variational Autoencoders with Buffered Stochastic Variational Inference

no code implementations27 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.

Variational Inference