ALP-GMM is is an algorithm that learns to generate a learning curriculum for black box reinforcement learning agents, whereby it sequentially samples parameters controlling a stochastic procedural generation of tasks or environments.

**T5**, or **Text-to-Text Transfer Transformer**, is a [Transformer](https://paperswithcode.com/method/transformer) based architecture that uses a text-to-text approach. Every task – including translation, question answering, and classification – is cast as feeding the model text as input and training it to generate some target text. This allows for the use of the same model, loss function, hyperparameters, etc. across our diverse set of tasks. The changes compared to [BERT](https://paperswithcode.com/method/bert) include:

- adding a *causal* decoder to the bidirectional architecture.
- replacing the fill-in-the-blank cloze task with a mix of alternative pre-training tasks.

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

ALP-GMM

Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments

**PeleeNet** is a convolutional neural network  and object detection backbone that is a variation of [DenseNet](https://paperswithcode.com/method/densenet) with optimizations to meet a memory and computational budget. Unlike competing networks, it does not use depthwise convolutions and instead relies on regular convolutions.

Source	Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com