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Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech.

, , , , , and . NeurIPS, (2020)

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Selecting Informative Contexts Improves Language Model Finetuning., , and . CoRR, (2020)Incorporating Context into Language Encoding Models for fMRI., and . NeurIPS, page 6629-6638. (2018)Multi-timescale Representation Learning in LSTM Language Models., , , and . ICLR, OpenReview.net, (2021)Deep Generative Modeling for Scene Synthesis via Hybrid Representations., , , , , , and . CoRR, (2018)Deep Generative Modeling for Scene Synthesis via Hybrid Representations., , , , , , and . ACM Trans. Graph., 39 (2): 17:1-17:21 (2020)Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech., , , , , and . NeurIPS, (2020)Sparse and low-rank approximations of large symmetric matrices using biharmonic interpolation., and . CoRR, (2017)Low-dimensional Structure in the Space of Language Representations is Reflected in Brain Responses., , , and . NeurIPS, page 8332-8344. (2021)Self-Supervised Models of Audio Effectively Explain Human Cortical Responses to Speech., , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 21927-21944. PMLR, (2022)Approximating Stacked and Bidirectional Recurrent Architectures with the Delayed Recurrent Neural Network., , , , , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 9648-9658. PMLR, (2020)