Stefano Ermon
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Researcher, Computer Scientist, Stanford University
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Stefano Ermon's Degrees
- PhD Computer Science University of California, Berkeley
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(Suggest an Edit or Addition)Stefano Ermon's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- Generative Adversarial Imitation Learning (2016) (1991)
- Combining satellite imagery and machine learning to predict poverty (2016) (1101)
- Generative Modeling by Estimating Gradients of the Data Distribution (2019) (962)
- Score-Based Generative Modeling through Stochastic Differential Equations (2020) (952)
- On the Opportunities and Risks of Foundation Models (2021) (938)
- PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples (2017) (634)
- A DIRT-T Approach to Unsupervised Domain Adaptation (2018) (470)
- MOPO: Model-based Offline Policy Optimization (2020) (410)
- Accurate Uncertainties for Deep Learning Using Calibrated Regression (2018) (394)
- Improved Techniques for Training Score-Based Generative Models (2020) (375)
- Coupling between oxygen redox and cation migration explains unusual electrochemistry in lithium-rich layered oxides (2017) (372)
- InfoVAE: Information Maximizing Variational Autoencoders (2017) (360)
- Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping (2015) (354)
- Closed-loop optimization of fast-charging protocols for batteries with machine learning (2020) (334)
- Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data (2017) (301)
- Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning (2019) (290)
- InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations (2017) (288)
- Label-Free Supervision of Neural Networks with Physics and Domain Knowledge (2016) (282)
- A Survey on Behavior Recognition Using WiFi Channel State Information (2017) (238)
- Constructing Unrestricted Adversarial Examples with Generative Models (2018) (229)
- Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2022) (218)
- Graphite: Iterative Generative Modeling of Graphs (2018) (215)
- InfoVAE: Balancing Learning and Inference in Variational Autoencoders (2019) (193)
- End-to-End Learning of Motion Representation for Video Understanding (2018) (183)
- Using publicly available satellite imagery and deep learning to understand economic well-being in Africa (2020) (180)
- High‐Voltage Charging‐Induced Strain, Heterogeneity, and Micro‐Cracks in Secondary Particles of a Nickel‐Rich Layered Cathode Material (2019) (161)
- SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations (2021) (159)
- Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models (2017) (159)
- Maximum Likelihood Training of Score-Based Diffusion Models (2021) (153)
- Sliced Score Matching: A Scalable Approach to Density and Score Estimation (2019) (152)
- Learning Controllable Fair Representations (2018) (142)
- Multi-Agent Generative Adversarial Imitation Learning (2018) (128)
- Towards Deeper Understanding of Variational Autoencoding Models (2017) (127)
- Deep Transfer Learning for Crop Yield Prediction with Remote Sensing Data (2018) (125)
- Understanding the Limitations of Variational Mutual Information Estimators (2019) (123)
- Denoising Diffusion Restoration Models (2022) (123)
- SDEdit: Image Synthesis and Editing with Stochastic Differential Equations (2021) (123)
- GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation (2022) (123)
- Tile2Vec: Unsupervised representation learning for spatially distributed data (2018) (122)
- Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization (2013) (119)
- Model-Free Imitation Learning with Policy Optimization (2016) (118)
- Using satellite imagery to understand and promote sustainable development (2019) (117)
- Stochastic Optimization of Sorting Networks via Continuous Relaxations (2019) (107)
- Learning Hierarchical Features from Deep Generative Models (2017) (97)
- Solving Inverse Problems in Medical Imaging with Score-Based Generative Models (2021) (94)
- Weakly Supervised Disentanglement with Guarantees (2019) (92)
- A-NICE-MC: Adversarial Training for MCMC (2017) (91)
- Learning Neural PDE Solvers with Convergence Guarantees (2019) (91)
- Audio Super Resolution using Neural Networks (2017) (90)
- Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting (2019) (88)
- Bias and Generalization in Deep Generative Models: An Empirical Study (2018) (85)
- A Theory of Usable Information Under Computational Constraints (2020) (84)
- Adversarial Examples for Natural Language Classification Problems (2018) (84)
- Permutation Invariant Graph Generation via Score-Based Generative Modeling (2020) (80)
- Geography-Aware Self-Supervised Learning (2020) (79)
- Fair Generative Modeling via Weak Supervision (2019) (77)
- Multi-Agent Adversarial Inverse Reinforcement Learning (2019) (74)
- Amortized Inference Regularization (2018) (71)
- Efficient Object Detection in Large Images Using Deep Reinforcement Learning (2019) (68)
- HiPPO: Recurrent Memory with Optimal Polynomial Projections (2020) (68)
- CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation (2021) (68)
- Bayesian Optimization of a Free-Electron Laser. (2019) (66)
- Best arm identification in multi-armed bandits with delayed feedback (2018) (66)
- Neural Joint Source-Channel Coding (2018) (65)
- FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness (2022) (65)
- Embed and Project: Discrete Sampling with Universal Hashing (2013) (64)
- Low-density Parity Constraints for Hashing-Based Discrete Integration (2014) (64)
- Modeling Sparse Deviations for Compressed Sensing using Generative Models (2018) (63)
- Learning Hierarchical Features from Generative Models (2017) (61)
- Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance (2018) (60)
- Semantic Segmentation of Crop Type in Africa: A Novel Dataset and Analysis of Deep Learning Methods (2019) (58)
- D2C: Diffusion-Denoising Models for Few-shot Conditional Generation (2021) (58)
- Feature-Enhanced Probabilistic Models for Diffusion Network Inference (2012) (57)
- Sparse Gaussian Processes for Bayesian Optimization (2016) (56)
- Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learning (2018) (55)
- Generating Interpretable Poverty Maps using Object Detection in Satellite Images (2020) (54)
- MintNet: Building Invertible Neural Networks with Masked Convolutions (2019) (53)
- Uniform Solution Sampling Using a Constraint Solver As an Oracle (2012) (52)
- Predicting Economic Development using Geolocated Wikipedia Articles (2019) (52)
- AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows (2019) (49)
- Diversity can be Transferred: Output Diversification for White- and Black-box Attacks (2020) (49)
- Meta-Inverse Reinforcement Learning with Probabilistic Context Variables (2019) (48)
- Reparameterizable Subset Sampling via Continuous Relaxations (2019) (48)
- Negative Data Augmentation (2021) (47)
- Learning When and Where to Zoom With Deep Reinforcement Learning (2020) (45)
- IQ-Learn: Inverse soft-Q Learning for Imitation (2021) (44)
- Poverty Prediction with Public Landsat 7 Satellite Imagery and Machine Learning (2017) (43)
- Adaptive Concentration Inequalities for Sequential Decision Problems (2016) (42)
- The Information-Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Modeling (2018) (41)
- Learning and Inference via Maximum Inner Product Search (2016) (39)
- Inferring The Latent Structure of Human Decision-Making from Raw Visual Inputs (2017) (39)
- Calibrated Model-Based Deep Reinforcement Learning (2019) (39)
- Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations (2019) (38)
- On Distillation of Guided Diffusion Models (2022) (38)
- Bayesian Optimization of FEL Performance at LCLS (2016) (38)
- Boosted Generative Models (2016) (38)
- Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization (2018) (38)
- Multi-label Contrastive Predictive Coding (2020) (36)
- Semi-Supervised Multitask Learning on Multispectral Satellite Images Using Wasserstein Generative Adversarial Networks (GANs) for Predicting Poverty (2019) (34)
- Optimization With Parity Constraints: From Binary Codes to Discrete Integration (2013) (33)
- Individual Calibration with Randomized Forecasting (2020) (33)
- Pattern Decomposition with Complex Combinatorial Constraints: Application to Materials Discovery (2014) (33)
- Neural Variational Inference and Learning in Undirected Graphical Models (2017) (32)
- Domain Adaptive Imitation Learning (2019) (32)
- NECST: Neural Joint Source-Channel Coding (2018) (31)
- Training Deep Energy-Based Models with f-Divergence Minimization (2020) (31)
- Dual Diffusion Implicit Bridges for Image-to-Image Translation (2022) (28)
- Cloud Removal from Satellite Images using Spatiotemporal Generator Networks (2020) (28)
- Closing the Gap Between Short and Long XORs for Model Counting (2015) (27)
- Mapping Missing Population in Rural India: A Deep Learning Approach with Satellite Imagery (2019) (26)
- Efficient Learning of Generative Models via Finite-Difference Score Matching (2020) (26)
- Variational Rejection Sampling (2018) (26)
- Learning to Interpret Satellite Images Using Wikipedia (2018) (26)
- Temporal Predictive Coding For Model-Based Planning In Latent Space (2021) (26)
- BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery (2021) (25)
- Experience Replay with Likelihood-free Importance Weights (2020) (25)
- Meta-Amortized Variational Inference and Learning (2019) (25)
- Exact Sampling with Integer Linear Programs and Random Perturbations (2016) (25)
- Learning with Weak Supervision from Physics and Data-Driven Constraints (2018) (25)
- Unsupervised Data Mining in nanoscale X-ray Spectro-Microscopic Study of NdFeB Magnet (2016) (24)
- Bayesian learning for rapid prediction of lithium-ion battery-cycling protocols (2021) (24)
- Monitoring Ethiopian Wheat Fungus with Satellite Imagery and Deep Feature Learning (2017) (24)
- Learning Large-Scale Dynamic Discrete Choice Models of Spatio-Temporal Preferences with Application to Migratory Pastoralism in East Africa (2015) (24)
- Belief Propagation Neural Networks (2020) (23)
- Using machine learning to discover shape descriptors for predicting emulsion stability in a microfluidic channel. (2019) (23)
- Bridging the Gap Between $f$-GANs and Wasserstein GANs (2019) (23)
- Flow-GAN: Bridging implicit and prescribed learning in generative models (2017) (23)
- Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks (2019) (22)
- Learning policies for battery usage optimization in electric vehicles (2012) (22)
- Learning to Interpret Satellite Images in Global Scale Using Wikipedia (2019) (22)
- SMT-Aided Combinatorial Materials Discovery (2012) (22)
- Deep Hybrid Models: Bridging Discriminative and Generative Approaches (2017) (22)
- Generative Adversarial Examples (2018) (21)
- Estimating Uncertainty Online Against an Adversary (2016) (21)
- On the Critical Role of Conventions in Adaptive Human-AI Collaboration (2021) (20)
- Gaussianization Flows (2020) (19)
- Trust Estimation in autonomic networks: a statistical mechanics approach (2009) (19)
- Predictive Coding for Locally-Linear Control (2020) (19)
- Unsupervised Out-of-Distribution Detection with Batch Normalization (2019) (18)
- Probabilistic planning with non-linear utility functions and worst-case guarantees (2012) (18)
- Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search (2017) (18)
- Improved Autoregressive Modeling with Distribution Smoothing (2021) (17)
- Bayesian optimization and attribute adjustment (2018) (17)
- Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration (2021) (17)
- Scalable deep learning to identify brick kilns and aid regulatory capacity (2021) (16)
- Evaluating the Disentanglement of Deep Generative Models through Manifold Topology (2020) (16)
- Farm Parcel Delineation Using Spatio-temporal Convolutional Networks (2020) (16)
- Solving Marginal MAP Problems with NP Oracles and Parity Constraints (2016) (15)
- Adaptive Antithetic Sampling for Variance Reduction (2019) (15)
- JPEG Artifact Correction using Denoising Diffusion Restoration Models (2022) (15)
- Autoregressive Models (2008) (15)
- Efficient Poverty Mapping from High Resolution Remote Sensing Images (2021) (14)
- Autotuning Stencil Computations with Structural Ordinal Regression Learning (2017) (14)
- SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning (2021) (14)
- Playing games against nature: optimal policies for renewable resource allocation (2010) (14)
- Risk-Sensitive Policies for Sustainable Renewable Resource Allocation (2011) (14)
- Probabilistic Circuits for Variational Inference in Discrete Graphical Models (2020) (14)
- Accelerating Natural Gradient with Higher-Order Invariance (2018) (13)
- Output Diversified Initialization for Adversarial Attacks (2020) (13)
- Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference (2018) (13)
- Reward Identification in Inverse Reinforcement Learning (2021) (13)
- Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Information (2021) (12)
- A Hybrid Approach for Probabilistic Inference using Random Projections (2015) (12)
- Estimating High Order Gradients of the Data Distribution by Denoising (2021) (12)
- Computing the Density of States of Boolean Formulas (2010) (12)
- SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery (2022) (11)
- Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference (2016) (11)
- A Lagrangian Perspective on Latent Variable Generative Models (2018) (11)
- Multi-agent Adversarial Inverse Reinforcement Learning with Latent Variables (2020) (11)
- Shape optimization in laminar flow with a label-guided variational autoencoder (2017) (11)
- Reliable Decisions with Threshold Calibration (2021) (10)
- Efficient Poverty Mapping using Deep Reinforcement Learning (2020) (10)
- Variable Elimination in the Fourier Domain (2015) (10)
- Comparing Distributions by Measuring Differences that Affect Decision Making (2022) (10)
- Improved Training with Curriculum GANs (2018) (9)
- Predicting Livelihood Indicators from Community-Generated Street-Level Imagery (2021) (9)
- Importance Sampling over Sets: A New Probabilistic Inference Scheme (2015) (9)
- Nonlinear Equation Solving: A Faster Alternative to Feedforward Computation (2020) (9)
- Designing Fast Absorbing Markov Chains (2014) (9)
- Unsupervised Calibration under Covariate Shift (2020) (9)
- Towards a foundation model for geospatial artificial intelligence (vision paper) (2022) (9)
- Density Ratio Estimation via Infinitesimal Classification (2021) (9)
- Deep Learning For Crop Yield Prediction in Africa (2019) (9)
- IS-COUNT: Large-scale Object Counting from Satellite Images with Covariate-based Importance Sampling (2021) (9)
- Imitation Learning by Estimating Expertise of Demonstrators (2022) (8)
- Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis (2021) (8)
- Hierarchical modeling of seed variety yields and decision making for future planting plans (2017) (8)
- Distributed generation of privacy preserving data with user customization (2019) (8)
- Improving Self-Supervised Learning by Characterizing Idealized Representations (2022) (8)
- Approximate Inference via Weighted Rademacher Complexity (2018) (8)
- Adaptive Hashing for Model Counting (2019) (7)
- Cross Domain Imitation Learning (2019) (7)
- A Survey of Human Activity Recognition Using WiFi CSI (2017) (7)
- Tile2Vec: Unsupervised representation learning for remote sensing data (2018) (7)
- Efficient Conditional Pre-training for Transfer Learning (2020) (7)
- Featurized Density Ratio Estimation (2021) (7)
- Differentiable Subset Sampling (2019) (7)
- Accelerated Adaptive Markov Chain for Partition Function Computation (2011) (7)
- Reliable Confidence Estimation via Online Learning (2016) (6)
- Anytime Sampling for Autoregressive Models via Ordered Autoencoding (2021) (6)
- PiRank: Scalable Learning To Rank via Differentiable Sorting (2020) (6)
- Imitation with Neural Density Models (2020) (6)
- Density Propagation and Improved Bounds on the Partition Function (2012) (6)
- Approximating the Permanent by Sampling from Adaptive Partitions (2019) (6)
- Regularizing Score-based Models with Score Fokker-Planck Equations (2022) (6)
- Autoregressive Score Matching (2020) (6)
- A Flat Histogram Method for Computing the Density of States of Combinatorial Problems (2011) (6)
- Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving (2020) (6)
- An Experimental Design Perspective on Model-Based Reinforcement Learning (2021) (6)
- Streamlining variational inference for constraint satisfaction problems (2018) (6)
- Neural Network Compression for Noisy Storage Devices (2021) (5)
- Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models (2022) (5)
- A General Recipe for Likelihood-free Bayesian Optimization (2022) (5)
- Concrete Score Matching: Generalized Score Matching for Discrete Data (2022) (5)
- Tight Variational Bounds via Random Projections and I-Projections (2015) (5)
- HyperSPNs: Compact and Expressive Probabilistic Circuits (2021) (5)
- Variational Bayes on Monte Carlo Steroids (2016) (5)
- Multi-Agent Imitation Learning with Copulas (2021) (5)
- Towards general-purpose representation learning of polygonal geometries (2022) (5)
- Flexible Approximate Inference via Stratified Normalizing Flows (2020) (4)
- Using publicly available satellite imagery and deep learning to understand economic well-being in Africa (2020) (4)
- Modular Conformal Calibration (2022) (4)
- Localized Calibration: Metrics and Recalibration (2021) (4)
- Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration (2020) (4)
- ButterflyFlow: Building Invertible Layers with Butterfly Matrices (2022) (4)
- Deterministic Policy Optimization by Combining Pathwise and Score Function Estimators for Discrete Action Spaces (2017) (4)
- Amortized Variational Compressive Sensing (2018) (4)
- Semi-supervised deep kernel learning (2016) (3)
- Generalizing Bayesian Optimization with Decision-theoretic Entropies (2022) (3)
- Domain Adaptation for Human Fall Detection Using WiFi Channel State Information (2019) (3)
- General Bounds on Satisfiability Thresholds for Random CSPs via Fourier Analysis (2017) (3)
- LISA: Learning Interpretable Skill Abstractions from Language (2022) (3)
- LMPriors: Pre-Trained Language Models as Task-Specific Priors (2022) (3)
- Stencil Autotuning with Ordinal Regression: Extended Abstract (2017) (3)
- Training Variational Autoencoders with Buffered Stochastic Variational Inference (2019) (3)
- Generative Adversarial Learning of Markov Chains (2017) (3)
- Extreme Q-Learning: MaxEnt RL without Entropy (2023) (3)
- Collaborative multiagent Gaussian inference in a dynamic environment using belief propagation (2010) (3)
- Understanding Classifier Mistakes with Generative Models (2020) (3)
- Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations (2022) (3)
- A Framework for Sample Efficient Interval Estimation with Control Variates (2020) (2)
- A Survey on Behaviour Recognition Using WiFi Channel State Information (2017) (2)
- Local calibration: metrics and recalibration (2021) (2)
- Hybrid Deep Discriminative/Generative Models for Semi-Supervised Learning (2017) (2)
- Transform Once: Efficient Operator Learning in Frequency Domain (2022) (2)
- A Unified Framework for Multi-distribution Density Ratio Estimation (2021) (2)
- Pseudo-Spherical Contrastive Divergence (2021) (2)
- AlignFlow: Learning from multiple domains via normalizing flows (2019) (2)
- A message passing approach to multiagent gaussian inference for dynamic processes (2011) (2)
- Improving Compositionality of Neural Networks by Decoding Representations to Inputs (2021) (2)
- Uncovering Hidden Structure through Parallel Problem Decomposition for the Set Basis Problem (2015) (2)
- Predicting Livelihood Indicators from Crowdsourced Street Level Images (2020) (2)
- Approximating Human Judgment of Generated Image Quality (2019) (2)
- LSH Softmax: Sub-Linear Learning and Inference of the Softmax Layer in Deep Architectures (2018) (2)
- Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients (2020) (2)
- Deep Latent State Space Models for Time-Series Generation (2022) (1)
- Learning policies for battery usage optimization in electric vehicles (2013) (1)
- Training and Inference on Any-Order Autoregressive Models the Right Way (2022) (1)
- End-to-End Diffusion Latent Optimization Improves Classifier Guidance (2023) (1)
- Bayesian Algorithm Execution for Tuning Particle Accelerator Emittance with Partial Measurements (2022) (1)
- Understanding economic development in rural Africa using satellite imagery, building footprints and deep models (2022) (1)
- HYBRID MUTUAL INFORMATION LOWER-BOUND ESTIMATORS FOR REPRESENTATION LEARNING (2021) (1)
- Conditional Imitation Learning for Multi-Agent Games (2022) (1)
- Privacy Preserving Recalibration under Domain Shift (2020) (1)
- Tracking Urbanization in Developing Regions with Remote Sensing Spatial-Temporal Super-Resolution (2022) (1)
- Building Coverage Estimation with Low-resolution Remote Sensing Imagery (2023) (1)
- PiRank: Learning To Rank via Differentiable Sorting (2020) (1)
- Exploration via Planning for Information about the Optimal Trajectory (2022) (1)
- Rapid bacterial identification based on surface-enhanced Raman scattering and machine learning (Conference Presentation) (Withdrawal Notice) (2018) (1)
- Equivariant Neural Network for Factor Graphs (2021) (1)
- Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation (2022) (1)
- Challenges in KDD and ML for Sustainable Development (2021) (1)
- Materials Discovery : New Opportunities at the Intersection of Constraint Reasoning and Learning ∗ (2012) (1)
- Uncovering Hidden Structure through Parallel Problem Decomposition (2014) (1)
- GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration (2023) (1)
- A statistical mechanics approach to trust management in autonomic networks (2012) (1)
- Bit Prioritization in Variational Autoencoders via Progressive Coding (2022) (1)
- Trust Estimation in Autonomic Networks: A Message Passing Approach (2009) (1)
- But Are You Sure? An Uncertainty-Aware Perspective on Explainable AI (2022) (1)
- Efficient Tuning of Particle Accelerator Emittance via Bayesian Algorithm Execution and Virtual Objectives (2022) (1)
- MUDiff: Unified Diffusion for Complete Molecule Generation (2023) (0)
- Long Horizon Temperature Scaling (2023) (0)
- Resampled Proposal Distributions for Variational Inference and Learning (2017) (0)
- Generative Modeling Helps Weak Supervision (and Vice Versa) (2022) (0)
- Hyena Hierarchy: Towards Larger Convolutional Language Models (2023) (0)
- MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning (2023) (0)
- Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching (2023) (0)
- Dynamic, Reversible Oxygen Redox As a Mediator of Voltage Hysteresis in Lithium-Rich Layered Oxide Electrodes (2017) (0)
- Neural Variational Random Field Learning (2016) (0)
- GlueGen: Plug and Play Multi-modal Encoders for X-to-image Generation (2023) (0)
- Image Synthesis and Editing with Stochastic Differential Equations (2021) (0)
- CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations (2023) (0)
- ABEL-B ASED S UPERVISION (2017) (0)
- Geometric Latent Diffusion Models for 3D Molecule Generation (2023) (0)
- A DIRT-T A PPROACH TO U NSUPERVISED D OMAIN (2018) (0)
- Bayesian Algorithm Execution for Tuning Particle Accelerator Emittance with a Virtual Objective (2022) (0)
- Local Calibration: Metrics and Recalibration (Supplementary Material) (2022) (0)
- SCORE-BASED GENERATIVE MODELS (2021) (0)
- Our approach most closely resembles a recent line of work involving the Gumbel distribution ( Hazan and Jaakkola 2012 (2017) (0)
- A Mechanism Design Alternative to Individual Calibration (2021) (0)
- A LIGN F LOW : L EARNING FROM MULTIPLE DOMAINS VIA NORMALIZING FLOWS (2019) (0)
- HIVE: Harnessing Human Feedback for Instructional Visual Editing (2023) (0)
- Time Series Super Resolution withTemporal Adaptive Batch Normalization (2018) (0)
- Repeated Interactions Convention Dependence HighLow ρi ρ 2 ρ 3 Rule representation Convention representation 4 player chess Friendly Rock Paper Scissors time gt gp (2021) (0)
- Quantifying and Understanding Adversarial Examples in Discrete Input Spaces (2021) (0)
- Trading-off Learning and Inference in Deep Latent Variable Models (2018) (0)
- Featurized Density Ratio Estimation (Supplementary Material) (2021) (0)
- Ideal Abstractions for Decision-Focused Learning (2023) (0)
- ournal of Statistical Mechanics: J Theory and Experiment Forest fire spread with non-universal critical behavior (2019) (0)
- U NDERSTANDING THE L IMITATIONS OF V ARIATIONAL M UTUAL I NFORMATION E STIMATORS (2020) (0)
- Decision Making And Inference Under Limited Information And High Dimensionality (2015) (0)
- Coupling between oxygen redox and cation migration explains unusual electrochemistry in lithium-rich layered oxides (2017) (0)
- VIA CONTINUOUS RELAXATIONS (2019) (0)
- Reflected Diffusion Models (2023) (0)
- Towards Certified Defense for Unrestricted Adversarial Attacks (2019) (0)
- Discrete Integration by Decoding Binary Codes (2013) (0)
- Label-Free Object Detection in Videos of Physical Interactions with GANs (2017) (0)
- On the Limits of Learning Representations with Label-Based Supervision (2017) (0)
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