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E2V-SDE or: How I Learned to Stop Worrying and Love Plagiarism

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This (parody) video presents how our (!) E2V-SDE paper (that has been accepted to CVPR 2022) largely consists of texts that are uncredited verbatim copies from more than 10 previously published papers.
00:00 Intro
00:16 Latent ODEs for Irregularly-Sampled Time Series
00:37 Stochastic Adversarial Video Prediction
00:48 Continuous Latent Process Flows
00:58 Efficient and Accurate Gradients for Neural SDEs
01:08 Continuous Latent Process Flows
01:48 Scalable Gradients for Stochastic Differential Equations
01:55 Vid-ODE: Continuous-time Video Generation with Neural Ordinary Differential Equation
02:12 Stochastic Latent Residual Video Prediction
02:20 Latent ODEs for Irregularly-Sampled Time Series
02:26 Vid-ODE: Continuous-time Video Generation with Neural Ordinary Differential Equation
02:44 Stochastic Latent Residual Video Prediction
02:52 Vid-ODE: Continuous-time Video Generation with Neural Ordinary Differential Equation
03:10 Stochastic Adversarial Video Prediction
03:30 Vid-ODE: Continuous-time Video Generation with Neural Ordinary Differential Equation
03:35 High Speed and High Dynamic Range Video with an Event Camera
03:48 Learning to Reconstruct High Speed and High Dynamic Range Videos from Events
04:00 Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy
04:10 Event-based Video Reconstruction Using Transformer
04:32 Static2Dynamic: Video Inference From a Deep Glimpse
04:43 Vid-ODE: Continuous-time Video Generation with Neural Ordinary Differential Equation
05:29 Learning Continuous-Time Dynamics by Stochastic Differential Networks
05:41 Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise
06:32 Scalable Gradients for Stochastic Differential Equations
07:02 Vid-ODE: Continuous-time Video Generation with Neural Ordinary Differential Equation
00:00 Intro
00:16 Latent ODEs for Irregularly-Sampled Time Series
00:37 Stochastic Adversarial Video Prediction
00:48 Continuous Latent Process Flows
00:58 Efficient and Accurate Gradients for Neural SDEs
01:08 Continuous Latent Process Flows
01:48 Scalable Gradients for Stochastic Differential Equations
01:55 Vid-ODE: Continuous-time Video Generation with Neural Ordinary Differential Equation
02:12 Stochastic Latent Residual Video Prediction
02:20 Latent ODEs for Irregularly-Sampled Time Series
02:26 Vid-ODE: Continuous-time Video Generation with Neural Ordinary Differential Equation
02:44 Stochastic Latent Residual Video Prediction
02:52 Vid-ODE: Continuous-time Video Generation with Neural Ordinary Differential Equation
03:10 Stochastic Adversarial Video Prediction
03:30 Vid-ODE: Continuous-time Video Generation with Neural Ordinary Differential Equation
03:35 High Speed and High Dynamic Range Video with an Event Camera
03:48 Learning to Reconstruct High Speed and High Dynamic Range Videos from Events
04:00 Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy
04:10 Event-based Video Reconstruction Using Transformer
04:32 Static2Dynamic: Video Inference From a Deep Glimpse
04:43 Vid-ODE: Continuous-time Video Generation with Neural Ordinary Differential Equation
05:29 Learning Continuous-Time Dynamics by Stochastic Differential Networks
05:41 Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise
06:32 Scalable Gradients for Stochastic Differential Equations
07:02 Vid-ODE: Continuous-time Video Generation with Neural Ordinary Differential Equation
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