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Found 20 papers, 12 papers with code
Global Prediction of COVID-19 Variant Emergence Using Dynamics-Informed Graph Neural Networks
The current methods for predicting the spread of new variants rely on statistical modeling, however, these methods work only when the new variant has already arrived in the region of interest and has a significant prevalence.
DTW+S: Shape-based Comparison of Time-series with Ordered Local Trend
Measuring distance or similarity between time-series data is a fundamental aspect of many applications including classification, clustering, and ensembling/alignment.
Acoustic-to-articulatory inversion for dysarthric speech: Are pre-trained self-supervised representations favorable?
We demonstrate the impact of different pre-trained features on this challenging AAI task, at low-resource conditions.
Spatio-Temporal Attention in Multi-Granular Brain Chronnectomes for Detection of Autism Spectrum Disorder
The traditional methods for detecting autism spectrum disorder (ASD) are expensive, subjective, and time-consuming, often taking years for a diagnosis, with many children growing well into adolescence and even adulthood before finally confirming the disorder.
The Variations of SIkJalpha Model for COVID-19 Forecasting and Scenario Projections
This paper presents the evolution of the SIkJalpha model and its many versions that have been used to submit to these collaborative efforts since the beginning of the pandemic.
Random Forest of Epidemiological Models for Influenza Forecasting
We demonstrate that our Random Forest-based approach is able to improve upon the forecasts of the individual predictors in terms of mean absolute error, coverage, and weighted interval score.
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.
Decoupling the Depth and Scope of Graph Neural Networks
We propose a design principle to decouple the depth and scope of GNNs -- to generate representation of a target entity (i. e., a node or an edge), we first extract a localized subgraph as the bounded-size scope, and then apply a GNN of arbitrary depth on top of the subgraph.
Ranked #3 on
Node Classification
on Reddit
Accelerating Large Scale Real-Time GNN Inference using Channel Pruning
In this paper, we propose to accelerate GNN inference by pruning the dimensions in each layer with negligible accuracy loss.
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.
Deep Graph Neural Networks with Shallow Subgraph Samplers
We propose a simple "deep GNN, shallow sampler" design principle to improve both the GNN accuracy and efficiency -- to generate representation of a target node, we use a deep GNN to pass messages only within a shallow, localized subgraph.
Accurate, Efficient and Scalable Training of Graph Neural Networks
For feature propagation within subgraphs, we improve cache utilization and reduce DRAM traffic by data partitioning.
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.
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.
SPEC2: SPECtral SParsE CNN Accelerator on FPGAs
On the other hand, weight pruning techniques address the redundancy in model parameters by converting dense convolutional kernels into sparse ones.
GraphSAINT: Graph Sampling Based Inductive Learning Method
Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs.
Ranked #1 on
Link Property Prediction
on ogbl-citation2
Accurate, Efficient and Scalable Graph Embedding
However, a major challenge is to reduce the complexity of layered GCNs and make them parallelizable and scalable on very large graphs -- state-of the art techniques are unable to achieve scalability without losing accuracy and efficiency.
