Action Chunking with Transformers (ACT)
Action Chunking with Transformers (ACT) is an imitation learning algorithm for fine-grained robotic manipulation that predicts a short sequence (a "chunk") of future actions at once instead of a single next…
Transformer Models
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Grouped-Query Attention
Grouped-query attention (GQA) is an attention mechanism for transformer language models that partitions the query heads into a small number of groups, where every query head in a group shares one key…
KV Cache
KV cache, short for key-value cache, is transient model state used during Transformer generation.
Masked autoencoder (MAE)
Masked autoencoder (MAE) is a self-supervised learning method for vision transformers that masks roughly 75% of an input image's patches and trains a network to reconstruct the missing pixels from the small…
Mixture of Depths
Mixture of Depths (MoD) is a technique for dynamically allocating computation to individual tokens within transformer-based language models.
Multi-Head Self-Attention
Multi-head self-attention is the core sequence-mixing mechanism of the Transformer architecture: it runs several scaled dot-product attention operations ("heads") in parallel over different learned projections…
Multi-head Latent Attention
Multi-head Latent Attention (MLA) is an attention mechanism for transformer models that achieves a 93.3% reduction in key-value cache size while maintaining or exceeding the performance of traditional…
RoBERTa
RoBERTa (Robustly Optimized BERT Pretraining Approach) is an open-source natural language processing model released in July 2019 by researchers at Facebook AI (now Meta AI) and the University of Washington…
Self-attention
Self-attention is a neural network operation in which each position forms a data-dependent mixture of information from positions in the same input sequence or set.
Sparse attention
Sparse attention is a family of techniques that cut the computational and memory cost of the attention mechanism in transformer models by letting each token attend to only a subset of other tokens in a sequence
XLNet
XLNet is a generalized autoregressive pretraining method for natural language processing that combines the strengths of autoregressive and autoencoding language models.