IEEE

On the Dependability of Bidirectional Encoder Representations from Transformers (BERT) to Soft Errors

Abstract: Transformers are widely used in natural language processing and computer vision, and Bidirectional Encoder Representations from Transformers (BERT) is one of the most popular pre-trained ...
IEEE

Efficient Spatiotemporal Prediction Transformer for Cooperative Satellite Remote Sensing

Abstract: Satellite remote sensing cooperation is essential for ensuring efficient data transmission in real-time applications. Network traffic prediction plays a crucial role in optimizing data ...
Hosted on MSN

Transformer encoder architecture explained simply

We break down the Encoder architecture in Transformers, layer by layer! If you've ever wondered how models like BERT and GPT process text, this is your ultimate guide. We look at the entire design of ...
GitHub

Add HumanV: decoder-only causal LM #42982

Dictionary containing the configuration parameters for the RoPE embeddings. Must include `rope_theta`. Dictionary containing the configuration parameters for the RoPE embeddings. attention_bias ...
marktechpost

Google Introduces T5Gemma 2: Encoder Decoder Models with Multimodal Inputs via SigLIP and 128K Context

T5Gemma 2 follows the same adaptation idea introduced in T5Gemma, initialize an encoder-decoder model from a decoder-only checkpoint, then adapt with UL2. In the above figure the research team show ...
GitHub

Zero-Training Context Extension for Transformer Encoders via Nonlinear Absolute Positional Embeddings Interpolation

Official implementation of "Zero-Training Context Extension for Transformer Encoders via Nonlinear Absolute Positional Embeddings Interpolation". Paper preprint is coming soon. This implementation ...