Zobrazeno 1 - 10
of 4 554
pro vyhledávání: '"Linderman BY"'
Unmeasured confounding is one of the major concerns in causal inference from observational data. Proximal causal inference (PCI) is an emerging methodological framework to detect and potentially account for confounding bias by carefully leveraging a
Externí odkaz:
http://arxiv.org/abs/2409.08924
Discrete diffusion modeling is a promising framework for modeling and generating data in discrete spaces. To sample from these models, different strategies present trade-offs between computation and sample quality. A predominant sampling strategy is
Externí odkaz:
http://arxiv.org/abs/2407.21243
Conventional nonlinear RNNs are not naturally parallelizable across the sequence length, unlike transformers and linear RNNs. Lim et. al. (2024) therefore tackle parallelized evaluation of nonlinear RNNs, posing it as a fixed point problem solved wit
Externí odkaz:
http://arxiv.org/abs/2407.19115
Understanding how the collective activity of neural populations relates to computation and ultimately behavior is a key goal in neuroscience. To this end, statistical methods which describe high-dimensional neural time series in terms of low-dimensio
Externí odkaz:
http://arxiv.org/abs/2408.03330
State space models (SSMs) have shown remarkable empirical performance on many long sequence modeling tasks, but a theoretical understanding of these models is still lacking. In this work, we study the learning dynamics of linear SSMs to understand ho
Externí odkaz:
http://arxiv.org/abs/2407.07279
Autor:
Inkawhich, Matthew, Inkawhich, Nathan, Yang, Hao, Zhang, Jingyang, Linderman, Randolph, Chen, Yiran
An object detector's ability to detect and flag \textit{novel} objects during open-world deployments is critical for many real-world applications. Unfortunately, much of the work in open object detection today is disjointed and fails to adequately ad
Externí odkaz:
http://arxiv.org/abs/2404.10865
Effectively modeling long spatiotemporal sequences is challenging due to the need to model complex spatial correlations and long-range temporal dependencies simultaneously. ConvLSTMs attempt to address this by updating tensor-valued states with recur
Externí odkaz:
http://arxiv.org/abs/2310.19694
Patterns of microcircuitry suggest that the brain has an array of repeated canonical computational units. Yet neural representations are distributed, so the relevant computations may only be related indirectly to single-neuron transformations. It thu
Externí odkaz:
http://arxiv.org/abs/2310.03186
Sequential latent variable models (SLVMs) are essential tools in statistics and machine learning, with applications ranging from healthcare to neuroscience. As their flexibility increases, analytic inference and model learning can become challenging,
Externí odkaz:
http://arxiv.org/abs/2308.14864
An important problem in time-series analysis is modeling systems with time-varying dynamics. Probabilistic models with joint continuous and discrete latent states offer interpretable, efficient, and experimentally useful descriptions of such data. Co
Externí odkaz:
http://arxiv.org/abs/2306.03291