Results 31 to 40 of about 22,229,619 (357)
GAIA-1: A Generative World Model for Autonomous Driving [PDF]
arXiv.org, 2023 Autonomous driving promises transformative improvements to transportation, but building systems capable of safely navigating the unstructured complexity of real-world scenarios remains challenging.
Anthony Hu +7 more
semanticscholar +1 more source
Generative Agents: Interactive Simulacra of Human Behavior [PDF]
ACM Symposium on User Interface Software and Technology, 2023 Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools.
J. Park +5 more
semanticscholar +1 more source
A semantic segmentation scheme for night driving improved by irregular convolution
Frontiers in Neurorobotics, 2023 In order to solve the poor performance of real-time semantic segmentation of night road conditions in video images due to insufficient light and motion blur, this study proposes a scheme: a fuzzy information complementation strategy based on generative ...
Yang Xuantao, Han Junying, Liu Chenzhong
doaj +1 more source
Sensors, 2022 Brain decoding is a process of decoding human cognitive contents from brain activities. However, improving the accuracy of brain decoding remains difficult due to the unique characteristics of the brain, such as the small sample size and high ...
Takaaki Higashi +3 more
doaj +1 more source
IEEE Access, 2023 This paper tackles the electron microscope image processing for rubber material discovery. In rubber material science fields, electron microscope images are used to observe the properties of materials during their development process. Hence, by analyzing
Rintaro Yanagi +4 more
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Advanced Science, 2023 A multimodal deep‐learning (MDL) framework is presented for predicting physical properties of a ten‐dimensional acrylic polymer composite material by merging physical attributes and chemical data.
Shun Muroga, Yasuaki Miki, Kenji Hata
doaj +1 more source
Generalized Additive Models [PDF]
Statistical Science, 1984 The classical linear regression model expresses the response vector Y as a function of the predictor variables \(X_ i\) through the model \(Y=\sum_{i}X_ i\beta_ i+e\), where the \(X_ i\) are observed, the \(\beta_ i\) are estimated by least squares or some other technique, e is the vector of errors.
Hastie, Trevor, Tibshirani, Robert
openaire +3 more sources
Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model [PDF]
Neural Information Processing Systems, 2020 This paper studies a central issue in modern reinforcement learning, the sample efficiency, and makes progress toward solving an idealistic scenario that assumes access to a generative model or a simulator.
Gen Li +4 more
semanticscholar +1 more source
Rewriting a Deep Generative Model [PDF]
European Conference on Computer Vision, 2020 A deep generative model such as a GAN learns to model a rich set of semantic and physical rules about the target distribution, but up to now, it has been obscure how such rules are encoded in the network, or how a rule could be changed. In this paper, we
David Bau +4 more
semanticscholar +1 more source
Economic Theory, 2018 We extend the Luce model of discrete choice theory to satisfactorily handle zero-probability choices. The Luce model (or the Logit model) is the most widely applied and used model in stochastic choice, but it struggles to explain choices that are never made.
Echenique, Federico, Saito, Kota
openaire +3 more sources