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Found 13 papers, 4 papers with code
Attention, Distillation, and Tabularization: Towards Practical Neural Network-Based Prefetching
DART accelerates the large model inference by 170x and the distilled model by 9. 4x.
Phases, Modalities, Temporal and Spatial Locality: Domain Specific ML Prefetcher for Accelerating Graph Analytics
Our predictors achieve 6. 80-16. 02% higher F1-score for delta and 11. 68-15. 41% higher accuracy-at-10 for page prediction compared with LSTM and vanilla attention models.
TransforMAP: Transformer for Memory Access Prediction
Data Prefetching is a technique that can hide memory latency by fetching data before it is needed by a program.
Fine-Grained Address Segmentation for Attention-Based Variable-Degree Prefetching
To reduce vocabulary size, we use fine-grained address segmentation as input.
The EpiBench Platform to Propel AI/ML-based Epidemic Forecasting: A Prototype Demonstration Reaching Human Expert-level Performance
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
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.
Maximum Entropy Model Rollouts: Fast Model Based Policy Optimization without Compounding Errors
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
+1
Data-driven Identification of Number of Unreported Cases for COVID-19: Bounds and Limitations
A critical factor that can hinder accurate long-term forecasts, is the number of unreported/asymptomatic cases.
Learning to Forecast and Forecasting to Learn from the COVID-19 Pandemic
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
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.
Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximation
Safety-aware exploration is another challenge in real systems since certain actions at particular states may result in catastrophic outcomes.
Model-based Reinforcement Learning
Model Predictive Control
+5
Not all Embeddings are created Equal: Extracting Entity-specific Substructures for RDF Graph Embedding
However, such approaches lead to representations comprising mostly "popular", instead of "relevant", entities in the KG.
Holistic Measures for Evaluating Prediction Models in Smart Grids
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.
