Abdul Fatir Ansari | DeepAI

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∙ 07/21/2023

Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting

Diffusion models have achieved state-of-the-art performance in generativ...

0 Marcel Kollovieh, et al. ∙

∙ 03/07/2023

Generative Modeling with Flow-Guided Density Ratio Learning

We present Flow-Guided Density Ratio Learning (FDRL), a simple and scala...

0 Alvin Heng, et al. ∙

∙ 01/26/2023

Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series

Learning accurate predictive models of real-world dynamic phenomena (e.g...

0 Abdul Fatir Ansari, et al. ∙

∙ 10/26/2021

Deep Explicit Duration Switching Models for Time Series

Many complex time series can be effectively subdivided into distinct reg...

20 Abdul Fatir Ansari, et al. ∙

∙ 12/01/2020

Refining Deep Generative Models via Wasserstein Gradient Flows

Deep generative modeling has seen impressive advances in recent years, t...

0 Abdul Fatir Ansari, et al. ∙

∙ 09/15/2020

Event-Driven Visual-Tactile Sensing and Learning for Robots

This work contributes an event-driven visual-tactile perception system, ...

0 Tasbolat Taunyazov, et al. ∙

∙ 09/16/2019

A Characteristic Function Approach to Deep Implicit Generative Modeling

In this paper, we formulate the problem of learning an Implicit Generati...

12 Abdul Fatir Ansari, et al. ∙

∙ 09/12/2018

Hyperprior Induced Unsupervised Disentanglement of Latent Representations

We address the problem of unsupervised disentanglement of latent represe...

0 Abdul Fatir Ansari, et al. ∙

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