Search Results for author: Kashif Rasul

Found 11 papers, 5 papers with code

Intrinsic Anomaly Detection for Multi-Variate Time Series

no code implementations29 Jun 2022 Stephan Rabanser, Tim Januschowski, Kashif Rasul, Oliver Borchert, Richard Kurle, Jan Gasthaus, Michael Bohlke-Schneider, Nicolas Papernot, Valentin Flunkert

We introduce a novel, practically relevant variation of the anomaly detection problem in multi-variate time series: intrinsic anomaly detection.

Anomaly Detection Navigate +2

VQ-AR: Vector Quantized Autoregressive Probabilistic Time Series Forecasting

no code implementations31 May 2022 Kashif Rasul, Young-Jin Park, Max Nihlén Ramström, Kyung-Min Kim

Time series models aim for accurate predictions of the future given the past, where the forecasts are used for important downstream tasks like business decision making.

Decision Making Inductive Bias +1

Context-invariant, multi-variate time series representations

no code implementations29 Sep 2021 Stephan Rabanser, Tim Januschowski, Kashif Rasul, Oliver Borchert, Richard Kurle, Jan Gasthaus, Michael Bohlke-Schneider, Nicolas Papernot, Valentin Flunkert

Modern time series corpora, in particular those coming from sensor-based data, exhibit characteristics that have so far not been adequately addressed in the literature on representation learning for time series.

Contrastive Learning Representation Learning +1

Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting

1 code implementation28 Jan 2021 Kashif Rasul, Calvin Seward, Ingmar Schuster, Roland Vollgraf

In this work, we propose \texttt{TimeGrad}, an autoregressive model for multivariate probabilistic time series forecasting which samples from the data distribution at each time step by estimating its gradient.

Multivariate Time Series Forecasting Probabilistic Time Series Forecasting

Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows

1 code implementation ICLR 2021 Kashif Rasul, Abdul-Saboor Sheikh, Ingmar Schuster, Urs Bergmann, Roland Vollgraf

In this work we model the multivariate temporal dynamics of time series via an autoregressive deep learning model, where the data distribution is represented by a conditioned normalizing flow.

Decision Making Multivariate Time Series Forecasting +2

Set Flow: A Permutation Invariant Normalizing Flow

no code implementations6 Sep 2019 Kashif Rasul, Ingmar Schuster, Roland Vollgraf, Urs Bergmann

We present a generative model that is defined on finite sets of exchangeable, potentially high dimensional, data.

FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP

1 code implementation NAACL 2019 Alan Akbik, Tanja Bergmann, Duncan Blythe, Kashif Rasul, Stefan Schweter, Rol Vollgraf,

We present FLAIR, an NLP framework designed to facilitate training and distribution of state-of-the-art sequence labeling, text classification and language models.

Chunking Named Entity Recognition +2

A Bandit Framework for Optimal Selection of Reinforcement Learning Agents

no code implementations10 Feb 2019 Andreas Merentitis, Kashif Rasul, Roland Vollgraf, Abdul-Saboor Sheikh, Urs Bergmann

This helps the bandit framework to select the best agents early, since these rewards are smoother and less sparse than the environment reward.

Inductive Bias reinforcement-learning

Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

33 code implementations25 Aug 2017 Han Xiao, Kashif Rasul, Roland Vollgraf

We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images of 70, 000 fashion products from 10 categories, with 7, 000 images per category.

BIG-bench Machine Learning

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