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Universal Approximation Using Well-Conditioned Normalizing Flows., , , and . NeurIPS, page 12700-12711. (2021)Representational aspects of depth and conditioning in normalizing flows., , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 5628-5636. PMLR, (2021)Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective., , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 24139-24172. PMLR, (2023)Extending and Improving Wordnet via Unsupervised Word Embeddings., , , and . CoRR, (2017)Simulated Tempering Langevin Monte Carlo II: An Improved Proof using Soft Markov Chain Decomposition, , and . (2018)cite arxiv:1812.00793Comment: 69 pages. arXiv admin note: text overlap with arXiv:1710.02736.On the Benefits of Memory for Modeling Time-Dependent PDEs., , , and . CoRR, (2024)Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond., , and . CoRR, (2023)Progressive distillation induces an implicit curriculum., , , , and . ICLR, OpenReview.net, (2025)Label optimal regret bounds for online local learning., , , and . CoRR, (2015)Masked Prediction: A Parameter Identifiability View., , , and . NeurIPS, (2022)