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ExACT
2025年6月
ExACT is an approach for teaching AI agents to explore more effectively, enabling them to intelligently navigate their environments, gather valuable information, evaluate options, and identify optimal decision-making and planning strategies.
Magma
2025年5月
Magma is a multimodal foundation model designed to both understand and act in digital and physical environments. Magma builds on the foundation models paradigm that pretraining on a larger amount of more diverse datasets allows these models to generalize better…
ReinMax
2023年9月
Bridging Discrete and Backpropagation: Straight-Through and Beyond—Guided by our findings, we propose a novel method called ReinMax, which integrates Heun’s Method, a second-order numerical method for solving ODEs, to approximate the gradient. Our method, ReinMax, achieves second-order accuracy without requiring…
imodelsX
2022年11月
Scikit-learn friendly library to interpret, and prompt-engineer text datasets using large language models.
Admin-Torch
2022年4月
Here, we provide a plug-in-and-play implementation of Admin, which stabilizes previously-diverged Transformer training and achieves better performance, without introducing additional hyper-parameters. The design of Admin is half-precision friendly and can be reparameterized into the original Transformer.
LiST (Lite Self-Training)
2021年10月
We present a new method LiST for efficient fine-tuning of large pre-trained language models (PLMs) in few-shot learning settings. LiST significantly improves over recent methods that adopt prompt fine-tuning using two key techniques. The first one is the use of…
Stochastic Mixture-of-Experts
2021年10月
This PyTorch package implements Taming Sparsely Activated Transformer with Stochastic Experts.
Focal Transformer
2021年8月
This is a codebase for our recently released paper “Focal Self-attention for Local-Global Interactions in Vision Transformers”. It developed a new sparse self-attention mechanism called focal self-attention towards more effective and efficient vision transformers. The goal is the release the…
Microsoft KaggleDBQA Dataset: Realistic Evaluation of Text-to-SQL Parsers
2021年7月
Microsoft KaggleDBQA is a cross-domain and complex evaluation dataset of real Web databases, with domain-specific data types, original formatting, and unrestricted questions. It also provides database documentation, which contain rich in-domain knowledge. The nature of obscure and abbreviated column/table names…