CoAtNets is a hybrid model built by Google’s Brain Team and has recently gained the attention of deep learning practitioners.
In many of the cases, we see that the traditional neural networks are not capable of holding and working on long and large information. attention layer can help a neural
Before 2015 when the first attention model was proposed, machine translation was based on the simple encoder-decoder model, a stack of RNN and LSTM layers. The encoder is used to
Perceiver is a transformer-based model that uses both cross attention and self-attention layers to generate representations of multimodal data. A latent array is used to extract information from the input
DETR(Detection Transformer) is an end to end object detection model that does object classification and localization i.e boundary box detection. It is a simple encoder-decoderTransformer with a novel loss function
DeLighT is a deep and light-weight transformer that distributes parameters efficiently among transformer blocks and layers
MultiSpeaker Text to Speech synthesis refers to a system with the ability to generate speech in different users’ voices. Collecting data and training on it for each user can be
Google AI unveiled a new neural network architecture called Transformer in 2017. The GoogleAI team had claimed the Transformer worked better than leading approaches such as recurrent neural networks and
Evolved Transformer has been evolved with neural architecture search (NAS) to perform sequence-to-sequence tasks such as neural machine translation (NMT)
XLnet is an extension of the Transformer-XL model. It learns bidirectional contexts using an autoregressive method. Let’s first understand the shortcomings of the BERT model so that we can better
ALBERT is a lite version of BERT which shrinks down the BERT in size while maintaining the performance.
Our brain is modular by design and is characterised by distinct but interacting subsystems which handle important functions like memory, language and perception. Attention is studied in neuroscience as an
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