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Found 18 papers, 3 papers with code
Injecting New Knowledge into Large Language Models via Supervised Fine-Tuning
We present a novel dataset generation process that leads to more effective knowledge ingestion through SFT, and our results show considerable performance improvements in Q&A tasks related to out-of-domain knowledge.
Long-Range Backscatter Connectivity via Spaceborne Synthetic Aperture Radar
SarComms is a new communication method that enables passive satellite backscatter connectivity using existing spaceborne synthetic aperture radar (SAR) signals.
RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture
Our pipeline consists of multiple stages, including extracting information from PDFs, generating questions and answers, using them for fine-tuning, and leveraging GPT-4 for evaluating the results.
Domain Adaptation for Sustainable Soil Management using Causal and Contrastive Constraint Minimization
Using the sensor data, we preserve underlying causal relations among sensor attributes and organic matter.
GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Models
Our analysis highlights GPT-4's ability to achieve a passing score on exams to earn credits for renewing agronomist certifications, answering 93% of the questions correctly and outperforming earlier general-purpose models, which achieved 88% accuracy.
Enhancing Network Management Using Code Generated by Large Language Models
Analyzing network topologies and communication graphs plays a crucial role in contemporary network management.
Knowledge Guided Representation Learning and Causal Structure Learning in Soil Science
Process-based models and analyzing observed data provide two avenues for improving our understanding of soil processes.
Affordable Artificial Intelligence -- Augmenting Farmer Knowledge with AI
Farms produce hundreds of thousands of data points on the ground daily.
SciAI4Industry -- Solving PDEs for industry-scale problems with deep learning
Solving partial differential equations with deep learning makes it possible to reduce simulation times by multiple orders of magnitude and unlock scientific methods that typically rely on large numbers of sequential simulations, such as optimization and uncertainty quantification.
DeepG2P: Fusing Multi-Modal Data to Improve Crop Production
Agriculture is at the heart of the solution to achieve sustainability in feeding the world population, but advancing our understanding on how agricultural output responds to climatic variability is still needed.
Causal Modeling of Soil Processes for Improved Generalization
Measuring and monitoring soil organic carbon is critical for agricultural productivity and for addressing critical environmental problems.
Machine learning can guide experimental approaches for protein digestibility estimations
Food protein digestibility and bioavailability are critical aspects in addressing human nutritional demands, particularly when seeking sustainable alternatives to animal-based proteins.
Model-Parallel Fourier Neural Operators as Learned Surrogates for Large-Scale Parametric PDEs
Fourier neural operators (FNOs) are a recently introduced neural network architecture for learning solution operators of partial differential equations (PDEs), which have been shown to perform significantly better than comparable deep learning approaches.
FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations
To address these challenges, we leverage Federated Learning (FL), where ground stations and satellites collaboratively train a global ML model without sharing the captured images on the satellites.
No Size Fits All: Automated Radio Configuration for LPWANs
Yet, for a given network that supports hundreds of devices over multiple miles, the network operator typically needs to specify the same configuration or among a small subset of configurations for all the client devices to communicate with the gateway.
Seeing Through Clouds in Satellite Images
This paper presents a neural-network-based solution to recover pixels occluded by clouds in satellite images.
Artificial Intelligence for Digital Agriculture at Scale: Techniques, Policies, and Challenges
Digital agriculture has the promise to transform agricultural throughput.
Learning to Align Images using Weak Geometric Supervision
Such descriptors are often derived using supervised learning on existing datasets with ground truth correspondences.
