Sandeep Madireddy

Latest

• Memristor-Spikelearn: A Spiking Neural Network Simulator for Studying Synaptic Plasticity under Realistic Memristor Behaviors
• Quantifying Uncertainty in Large Language Models: Applications in Molecular Chemistry Tasks
• Scaling transformer neural networks for skillful and reliable medium-range weather forecasting
• AstroMLab 1: Who wins astronomy jeopardy!?
• Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields
• REMEDI: Corrective Transformations for Improved Neural Entropy Estimation
• BPNAS: Bayesian Progressive Neural Architecture Search
• Parametric Sensitivities of a Wind-driven Baroclinic Ocean Using Neural Surrogates
• Scaling Transformers for Skillful and Reliable Medium-range Weather Forecasting
• Sequential Bayesian Neural Subnetwork Ensembles
• Improving performance in continual learning tasks using bio-inspired architectures
• Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck
• Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck
• A domain-agnostic approach for characterization of lifelong learning systems
• Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures
• HPC Storage Service Autotuning Using Variational-Autoencoder-Guided Asynchronous Bayesian Optimization
• A Taxonomy of Error Sources in HPC I/O Machine Learning Models
• Application of machine learning and artificial intelligence to extend EFIT equilibrium reconstruction
• Application of machine learning and artificial intelligence to extend EFIT equilibrium reconstruction
• AutoML for Neuromorphic Computing and Application-Driven Co-Design: Asynchronous, Massively Parallel Optimization of Spiking Architectures
• Biological Underpinnings for Lifelong Learning Machines: A Perspective
• DeepAdversaries: Examining the robustness of deep learning models for galaxy morphology classification
• General policy mapping: online continual reinforcement learning inspired on the insect brain
• Single Gaussian Process Method for Arbitrary Tokamak Regimes with a Statistical Analysis
• Single Gaussian process method for arbitrary tokamak regimes with a statistical analysis
• Towards continually learning application performance models
• Unified probabilistic neural architecture and weight ensembling improves model robustness
• $łess$i$greater$In situ$łess$/i$greater$ compression artifact removal in scientific data using deep transfer learning and experience replay
• A Modular Deep Learning Pipeline for Galaxy-Scale Strong Gravitational Lens Detection and Modeling
• DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification
• Neuromorphic Architectures for Edge Computing Under Extreme Environments
• Physical Benchmarking for AI-Generated Cosmic Web
• Physical benchmarking for AI-generated cosmic web
• Calibration of hyperelastic constitutive models: the role of boundary conditions, search algorithms, and experimental variability
• Gauge: An Interactive Data-Driven Visualization Tool for HPC Application I/O Performance Analysis
• HPC I/O Throughput Bottleneck Analysis with Explainable Local Models
• In situ compression artifact removal in scientific data using deep transfer learning and experience replay
• Multilayer Neuromodulated Architectures for Memory-Constrained Online Continual Learning
• Time-series learning of latent-space dynamics for reduced-order model closure
• Towards Generalizable Models of I/O Throughput
• Adaptive Learning for Concept Drift in Application Performance Modeling
• Improving Scalability of Parallel CNN Training by Adjusting Mini-Batch Size at Run-Time
• Modular Deep Learning Analysis of Galaxy-Scale Strong Lensing Images
• Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning
• On the inference of viscoelastic constants from stress relaxation experiments
• Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection
• Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models
• Value-Added Chemical Discovery Using Reinforcement Learning
• Modeling I/O Performance Variability Using Conditional Variational Autoencoders
• Machine Learning Based Parallel I/O Predictive Modeling: A Case Study on Lustre File Systems
• Analysis and Correlation of Application I/O Performance and System-Wide I/O Activity
• Uncertainty Quantification Using the Nearest Neighbor Gaussian Process
• Bayesian calibration of hyperelastic constitutive models of soft tissue
• A Bayesian approach to selecting hyperelastic constitutive models of soft tissue