Awesome multi-modal large language paper/project, collections of popular training strategies, e.g., PEFT, LoRA.
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Updated
Mar 31, 2024
Awesome multi-modal large language paper/project, collections of popular training strategies, e.g., PEFT, LoRA.
Gitana SDK
Decoding the Learned Features of Masked Autoencoders in Semantic Segmentation Tasks
Domain Foundation Models for Time Series Classification
A project webpage for the EHRMamba paper.
This repository contains the python package for Helical
MixEval, a ground-truth-based dynamic benchmark derived from off-the-shelf benchmark mixtures, which evaluates LLMs with a highly capable model ranking (i.e., 0.96 correlation with Chatbot Arena) while running locally and quickly (6% the time and cost of running MMLU), with its queries being stably updated every month to avoid contamination.
Using an LLM to discover the genetic causes of rare disease
Fine-tuning foundation model for severe weather event prediction in the U.S. with 3-6 months of lead time
Official implementation of ICLR 2024 paper "Contrastive Learning Is Spectral Clustering On Similarity Graph" (https://arxiv.org/abs/2303.15103)
Open-Source Python Software for Functional MRI Analysis
Solution for NeurIPS 2023 - MedFM Challenge
Implementation of Adaptive Machine Learning for Resource-Constrained Environments: A Comparative Study on CPU Utilization Prediction
Official repository for my MSc thesis: "Addressing Goal Misgeneralization with Natural Language Interfaces."
Projects and summaries for the Machine Learning [PPGEEC2318] course at UFRN, taught by Professor Ivanovitch Silva.
Awesome Semantic Textual Similarity: a curated list of Semantic Textual Similarity in Large Language Models and NLP
Website of Olivier Bernard, Professor at the university of Lyon (INSA) and Deputy Director of the CREATIS research laboratory
Simple Image Search powered by Multimodal Foundation Models (OpenAI Clip and Microsoft GLIP)
A lightweight library to support the development of applications using LLMs
[MICCAI'2024] EndoDAC: Efficient Adapting Foundation Model for Self-Supervised Depth Estimation from Any Endoscopic Camera
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