Results 11 to 20 of about 1,072,227 (356)
Neural Computation, 1992 Although Bayesian analysis has been in use since Laplace, the Bayesian method of model-comparison has only recently been developed in depth. In this paper, the Bayesian approach to regularization and model-comparison is demonstrated by studying the ...
MacKay, David J. C.
core +3 more sources
Extending Context Window of Large Language Models via Positional Interpolation [PDF]
arXiv.org, 2023 We present Position Interpolation (PI) that extends the context window sizes of RoPE-based pretrained LLMs such as LLaMA models to up to 32768 with minimal fine-tuning (within 1000 steps), while demonstrating strong empirical results on various tasks ...
Shouyuan Chen +3 more
semanticscholar +1 more source
MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation [PDF]
arXiv.org, 2022 Video prediction is a challenging task. The quality of video frames from current state-of-the-art (SOTA) generative models tends to be poor and generalization beyond the training data is difficult.
Vikram S. Voleti +2 more
semanticscholar +1 more source
N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting [PDF]
AAAI Conference on Artificial Intelligence, 2022 Recent progress in neural forecasting accelerated improvements in the performance of large-scale forecasting systems. Yet, long-horizon forecasting remains a very difficult task.
Cristian Challu +5 more
semanticscholar +1 more source
XVFI: eXtreme Video Frame Interpolation [PDF]
IEEE International Conference on Computer Vision, 2021 In this paper, we firstly present a dataset (X4K1000FPS) of 4K videos of 1000 fps with the extreme motion to the research community for video frame interpolation (VFI), and propose an extreme VFI network, called XVFI-Net, that first handles the VFI for ...
Hyeonjun Sim, Jihyong Oh, Munchurl Kim
semanticscholar +1 more source
Time Lens: Event-based Video Frame Interpolation [PDF]
Computer Vision and Pattern Recognition, 2021 State-of-the-art frame interpolation methods generate intermediate frames by inferring object motions in the image from consecutive key-frames. In the absence of additional information, first-order approximations, i.e.
Stepan Tulyakov +6 more
semanticscholar +1 more source
Softmax Splatting for Video Frame Interpolation [PDF]
Computer Vision and Pattern Recognition, 2020 Differentiable image sampling in the form of backward warping has seen broad adoption in tasks like depth estimation and optical flow prediction. In contrast, how to perform forward warping has seen less attention, partly due to additional challenges ...
Simon Niklaus, Feng Liu
semanticscholar +1 more source
Video Frame Interpolation Transformer [PDF]
Computer Vision and Pattern Recognition, 2021 Existing methods for video interpolation heavily rely on deep convolution neural networks, and thus suffer from their intrinsic limitations, such as content-agnostic kernel weights and restricted receptive field.
Zhihao Shi +4 more
semanticscholar +1 more source
Australasian Journal of Philosophy, 2022 Decision theory requires agents to assign probabilities to states of the world and utilities to the possible outcomes of different actions. When agents commit to having the probabilities and/or utilities in a decision problem defined by objective features of the world, they may find themselves unable to decide which actions maximize expected utility ...
Cohen, Jonathan, Sober, Elliott
openaire +2 more sources