A quick reimplementation of the two datasets ("digits" and "commands") proposed in the paper "An Investigation of Few-Shot Learning in Spoken Term Classification"
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Updated
Mar 13, 2024 - Python
A quick reimplementation of the two datasets ("digits" and "commands") proposed in the paper "An Investigation of Few-Shot Learning in Spoken Term Classification"
Implementation of Prototypical Networks for Few Shot Learning (https://arxiv.org/abs/1703.05175) in Pytorch
Generative AI - Use Watsonx to respond to natural language questions using RAG (context, few-shot, watson-studio, rag, vector-database, foundation-models, llm, prompt-engineering, retrieval-augmented-generation, milvus).
An analysis and comparison of transfer learning and meta-learning for the task of few-shot classification of flowers with particular interest in cases where data is limited.
A demonstration repo for how to do automatic translation using local llms.
Official PyTorch implementation of SynergyNeRF: "Synergistic Integration of Coordinate Network and Tensorial Feature for Improving NeRFs from Sparse Inputs (ICML2024)"
[AAAI-2024] Pytorch implementation of "ColNeRF: Collaboration for Generalizable Sparse Input Neural Radiance Field"
XCS224U - Winter 2021 - Syed/Aiswarya - Intent Classification - Few Shot - Code and Dataset
This repository is made public for reproducibility of our recent work on Training Naturalized Semantic Parsers with Very Little Data
Source code for NeurIPS 2020 paper "Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding"
Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learning. NAACL 2024.
The official source code for Task-Equivariant Graph Few-shot Learning (TEG) at KDD 2023.
Evaluation framework for different few-shot-learning algorithm
Cross domain few-shot transfer learning from MiniImageNet to EuroSAT_RGB and CUB
This is the code of AAAI'21 paper "Tailoring Embedding Function to Heterogeneous Few-Shot Tasks by Global and Local Feature Adaptors".
NAACL'21 A Comparative Study on Schema-Guided Dialogue State Tracking
MetaVAE Implementation in Pytorch
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