61 citations · 92 across the 8 of their papers we have counts for
9 papers
Chronos: Learning the Language of Time Series
Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen +15
We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a f…
Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting
Marcel Kollovieh, Abdul Fatir Ansari, Michael Bohlke-Schneider +3
Diffusion models have achieved state-of-the-art performance in generative modeling tasks across various domains. Prior works on time series diffusion models have primarily focused…
Generative Modeling with Flow-Guided Density Ratio Learning
Alvin Heng, Abdul Fatir Ansari, Harold Soh
We present Flow-Guided Density Ratio Learning (FDRL), a simple and scalable approach to generative modeling which builds on the stale (time-independent) approximation of the gradie…
Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series
Abdul Fatir Ansari, Alvin Heng, Andre Lim +1
Learning accurate predictive models of real-world dynamic phenomena (e.g., climate, biological) remains a challenging task. One key issue is that the data generated by both natural…
Deep Explicit Duration Switching Models for Time Series
Abdul Fatir Ansari, Konstantinos Benidis, Richard Kurle +5
Many complex time series can be effectively subdivided into distinct regimes that exhibit persistent dynamics. Discovering the switching behavior and the statistical patterns in th…
Refining Deep Generative Models via Discriminator Gradient Flow
Abdul Fatir Ansari, Ming Liang Ang, Harold Soh
Deep generative modeling has seen impressive advances in recent years, to the point where it is now commonplace to see simulated samples (e.g., images) that closely resemble real-w…
Event-Driven Visual-Tactile Sensing and Learning for Robots
Tasbolat Taunyazov, Weicong Sng, Hian Hian See +5
This work contributes an event-driven visual-tactile perception system, comprising a novel biologically-inspired tactile sensor and multi-modal spike-based learning. Our neuromorph…
A Characteristic Function Approach to Deep Implicit Generative Modeling
Abdul Fatir Ansari, Jonathan Scarlett, Harold Soh
Implicit Generative Models (IGMs) such as GANs have emerged as effective data-driven models for generating samples, particularly images. In this paper, we formulate the problem of…
Hyperprior Induced Unsupervised Disentanglement of Latent Representations
Abdul Fatir Ansari, Harold Soh
We address the problem of unsupervised disentanglement of latent representations learnt via deep generative models. In contrast to current approaches that operate on the evidence l…