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Found 21 papers, 4 papers with code
REMEDI: Corrective Transformations for Improved Neural Entropy Estimation
The approach combines the minimization of the cross-entropy for simple, adaptive base models and the estimation of their deviation, in terms of the relative entropy, from the data density.
Scaling transformer neural networks for skillful and reliable medium-range weather forecasting
At the core of Stormer is a randomized forecasting objective that trains the model to forecast the weather dynamics over varying time intervals.
Surrogate Neural Networks to Estimate Parametric Sensitivity of Ocean Models
Modeling is crucial to understanding the effect of greenhouse gases, warming, and ice sheet melting on the ocean.
Towards Continually Learning Application Performance Models
Machine learning-based performance models are increasingly being used to build critical job scheduling and application optimization decisions.
Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures
The ability to learn continuously from an incoming data stream without catastrophic forgetting is critical to designing intelligent systems.
AutoML for neuromorphic computing and application-driven co-design: asynchronous, massively parallel optimization of spiking architectures
In this work we have extended AutoML inspired approaches to the exploration and optimization of neuromorphic architectures.
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed.
General policy mapping: online continual reinforcement learning inspired on the insect brain
Our model leverages the offline training of a feature extraction and a common general policy layer to enable the convergence of RL algorithms in online settings.
Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection
For the first time, we demonstrate the successful use of domain adaptation on two very different observational datasets (from SDSS and DECaLS).
Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness
To this end, we propose a Unified probabilistic architecture and weight ensembling Neural Architecture Search (UraeNAS) that leverages advances in probabilistic neural architecture search and approximate Bayesian inference to generate ensembles form the joint distribution of neural network architectures and weights.
HPC Storage Service Autotuning Using Variational-Autoencoder-Guided Asynchronous Bayesian Optimization
Distributed data storage services tailored to specific applications have grown popular in the high-performance computing (HPC) community as a way to address I/O and storage challenges.
Sequential Bayesian Neural Subnetwork Ensembles
Deep neural network ensembles that appeal to model diversity have been used successfully to improve predictive performance and model robustness in several applications.
Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck
In addition, it provides a mechanism for learning a joint distribution of the latent variable and the sparsity and hence can account for the complete uncertainty in the latent space.
DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification
On the other hand, we show that training with domain adaptation improves model robustness and mitigates the effects of these perturbations, improving the classification accuracy by 23% on data with higher observational noise.
Applications and Techniques for Fast Machine Learning in Science
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery.
Neuromodulated Neural Architectures with Local Error Signals for Memory-Constrained Online Continual Learning
Using high performing configurations metalearned in the single task learning setting, we achieve superior continual learning performance on Split-MNIST, and Split-CIFAR-10 data as compared with other memory-constrained learning approaches, and match that of the state-of-the-art memory-intensive replay-based approaches.
Multilayer Neuromodulated Architectures for Memory-Constrained Online Continual Learning
We focus on the problem of how to achieve online continual learning under memory-constrained conditions where the input data may not be known \emph{a priori}.
A Modular Deep Learning Pipeline for Galaxy-Scale Strong Gravitational Lens Detection and Modeling
Upcoming large astronomical surveys are expected to capture an unprecedented number of strong gravitational lensing systems.
Value-Added Chemical Discovery Using Reinforcement Learning
Computer-assisted synthesis planning aims to help chemists find better reaction pathways faster.
Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models
Rapid simulations of advection-dominated problems are vital for multiple engineering and geophysical applications.
Neuromorphic Architecture Optimization for Task-Specific Dynamic Learning
Our results show that optimal learning rules can be dataset-dependent even within similar tasks.
