Search Results for author: Viktor K. Prasanna

Found 11 papers, 4 papers with code

TransforMAP: Transformer for Memory Access Prediction

no code implementations29 May 2022 Pengmiao Zhang, Ajitesh Srivastava, Anant V. Nori, Rajgopal Kannan, Viktor K. Prasanna

Data Prefetching is a technique that can hide memory latency by fetching data before it is needed by a program.

The EpiBench Platform to Propel AI/ML-based Epidemic Forecasting: A Prototype Demonstration Reaching Human Expert-level Performance

no code implementations4 Feb 2021 Ajitesh Srivastava, Tianjian Xu, Viktor K. Prasanna

In this paper, we introduce a prototype of EpiBench which is currently running and accepting submissions for the task of forecasting COVID-19 cases and deaths in the US states and We demonstrate that we can utilize the prototype to develop an ensemble relying on fully automated epidemic forecasts (no human intervention) that reaches human-expert level ensemble currently being used by the CDC.


Fast and Accurate Forecasting of COVID-19 Deaths Using the SIkJ$α$ Model

4 code implementations10 Jul 2020 Ajitesh Srivastava, Tianjian Xu, Viktor K. Prasanna

Many of these methods are based on traditional epidemiological model which rely on simulations or Bayesian inference to simultaneously learn many parameters at a time.

Bayesian Inference

Maximum Entropy Model Rollouts: Fast Model Based Policy Optimization without Compounding Errors

no code implementations8 Jun 2020 Chi Zhang, Sanmukh Rao Kuppannagari, Viktor K. Prasanna

Furthermore, we propose to generate \emph{diverse} model rollouts by non-uniform sampling of the environment states such that the entropy of the model rollouts is maximized.

Model-based Reinforcement Learning reinforcement-learning

Data-driven Identification of Number of Unreported Cases for COVID-19: Bounds and Limitations

1 code implementation3 Jun 2020 Ajitesh Srivastava, Viktor K. Prasanna

A critical factor that can hinder accurate long-term forecasts, is the number of unreported/asymptomatic cases.

Epidemiology Management

Learning to Forecast and Forecasting to Learn from the COVID-19 Pandemic

1 code implementation23 Apr 2020 Ajitesh Srivastava, Viktor K. Prasanna

In particular, we show that changes in model parameters over time can help us quantify how well a state or a country has responded to the epidemic.


Towards High Performance, Portability, and Productivity: Lightweight Augmented Neural Networks for Performance Prediction

no code implementations17 Mar 2020 Ajitesh Srivastava, Naifeng Zhang, Rajgopal Kannan, Viktor K. Prasanna

More desirable is a high-level language where the domain-specialist simply specifies the workload in terms of high-level operations (e. g., matrix-multiply(A, B)), and the compiler identifies the best implementation fully utilizing the heterogeneous platform.

Holistic Measures for Evaluating Prediction Models in Smart Grids

no code implementations2 Jun 2014 Saima Aman, Yogesh Simmhan, Viktor K. Prasanna

The performance of prediction models is often based on "abstract metrics" that estimate the model's ability to limit residual errors between the observed and predicted values.

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