sent_debias
transferlearning
| sent_debias | transferlearning | |
|---|---|---|
| 1 | 1 | |
| 55 | 12,946 | |
| - | - | |
| 0.0 | 7.8 | |
| over 1 year ago | 27 days ago | |
| Python | Python | |
| MIT License | MIT License |
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sent_debias
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academic ethics issues in NLP
following on from the above, to what extent should we trust big models and the built in biases that they learn from huge scraped datasets? Many current SOTA trends for doing few shot learning on nlp tasks involve fine tuning existing large language models. There are lots of interesting research is going on around understanding and removing these biases like this paper from Liang and Li @ ACL2020. A related point is explainability - again some interesting work going on around things like rationale generation this now somewhat old paper by Lei et al 2016 gives some good context
transferlearning
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[D] Medium Article: Adaptive Learning for Time Series Forecasting
The src is available in https://github.com/jindongwang/transferlearning I'll also publish about how to code the model for time series
What are some alternatives?
zshot - Zero and Few shot named entity & relationships recognition
stackoverflow-better-stats - Better statistics about Stack Overflow's 2023 Developer Survey
PaddleHelix - Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集
awesome-artificial-intelligence-research - A curated list of Artificial Intelligence (AI) Research, tracks the cutting edge trending of AI research, including recommender systems, computer vision, machine learning, etc.
TS-TCC - [IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"
Transfer-Learning-Library - Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
Efficient-VDVAE - Official Pytorch and JAX implementation of "Efficient-VDVAE: Less is more"
FSL-Mate - FSL-Mate: A collection of resources for few-shot learning (FSL).
train-procgen - Code for the paper "Leveraging Procedural Generation to Benchmark Reinforcement Learning"
fast-artistic-videos - Video style transfer using feed-forward networks.
natural-adv-examples - A Harder ImageNet Test Set (CVPR 2021)
multi-domain-imbalance - [ECCV 2022] Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization, and Beyond