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ExACT
6月 2025
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
5月 2025
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
9月 2023
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
11月 2022
Scikit-learn friendly library to interpret, and prompt-engineer text datasets using large language models.
Admin-Torch
4月 2022
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)
10月 2021
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
10月 2021
This PyTorch package implements Taming Sparsely Activated Transformer with Stochastic Experts.
Focal Transformer
8月 2021
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
7月 2021
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…