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Found 10 papers, 2 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.
Exploring Optical Flow Inclusion into nnU-Net Framework for Surgical Instrument Segmentation
This work seeks to employ OF maps as an additional input to the nnU-Net architecture to improve its performance in the surgical instrument segmentation task, taking advantage of the fact that instruments are the main moving objects in the surgical field.
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.
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.
Strength in Diversity: Multi-Branch Representation Learning for Vehicle Re-Identification
A lightweight solution using grouped convolution is also proposed to mimic the learning of loss-splitting into multiple embeddings while significantly reducing the model size.
Ranked #1 on
Vehicle Re-Identification
on VeRi-Wild Small
A Comprehensive Modeling Approach for Crop Yield Forecasts using AI-based Methods and Crop Simulation Models
In this paper, we propose a comprehensive approach for yield forecasting that combines data-driven solutions, crop simulation models, and model surrogates to support multiple user-profiles and needs when dealing with crop management decision-making.
SurgT challenge: Benchmark of Soft-Tissue Trackers for Robotic Surgery
This assessment uses benchmarking metrics that were purposely developed for this challenge, to verify the efficacy of unsupervised deep learning algorithms in tracking soft-tissue.
Context-aware Execution Migration Tool for Data Science Jupyter Notebooks on Hybrid Clouds
Interactive computing notebooks, such as Jupyter notebooks, have become a popular tool for developing and improving data-driven models.
Estimating crop yields with remote sensing and deep learning
Increasing the accuracy of crop yield estimates may allow improvements in the whole crop production chain, allowing farmers to better plan for harvest, and for insurers to better understand risks of production, to name a few advantages.
A Scalable Machine Learning System for Pre-Season Agriculture Yield Forecast
Yield forecast is essential to agriculture stakeholders and can be obtained with the use of machine learning models and data coming from multiple sources.
