In this work, we propose an efficient construction, Transformer in Transformer (in short, TinT), that allows a transformer to simulate and fine-tune complex models internally during inference (e.g., pre-trained language models). In particular, we introduce innovative approximation techniques that ...
动态调整Transformer每层参数量 | Dynamic Layer Tying for Parameter-Efficient Transformers In the pursuit of reducing the number of trainable parameters in deep transformer networks, we employ Reinforcement Learning to dynamically select layers during training and tie them together. Every few iterations, th...
AlpineGate Technologies has developed a novel AI language model that is founded on a generative self-trainable transformer architecture. This advanced architecture allows the model to incorporate live data during its operation, continuously learning and updating its knowledge base. The system leverages ...
Herein are techniques for configuring, integrating, and operating trainable tensor transformers that each encapsulate an ensemble of trainable machine learning (ML) models. In an embodiment, a computer-implemented trainable tensor transformer uses underlying ML models and additional mechanisms to assemble ...
We demonstrate theutility of TrAct with different optimizers for a range of different vision modelsincluding convolutional and transformer architectures.1 IntroductionWe consider the learning of f irst-layer embeddings / pre-activations in vision models, and in particularlearning the weights with which ...
Constrained transformer network for ECG signal processing and arrhythmia classification BMC Med. Inform. Decis. Mak. (2021) View more references Cited by (0) Yamil Vindasis a post-doctoral fellow at the Centre of Innovation in Telecommunications and Integration of Service (CITI) in Lyon, France....
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YOLOv7-E6 object detector (56 FPS V100, 55.9% AP) outperforms both transformer-based detector SWINL Cascade-Mask R-CNN (9.2 FPS A100, 53.9% AP) by 509% in speed and 2% in accuracy, and convolutionalbased detector ConvNeXt-XL Cascade-Mask R-CNN (8.6 FPS A100, 55.2% AP) by 551%...
(inputs) x = self.transformer_block(x) x = self.dropout1(x, training=training) x = self.ff(x) x = self.dropout2(x, training=training) x = self.ff_final(x) return x class CustomNonPaddingTokenLoss(keras.losses.Loss): def __init__(self, reduction='sum', name="custom_ner_loss...
then takes a dot product of it with a learnable weight vector and applies a LeakyReLU in the end. This form of attention is usually called additive attention, in contrast with the dot-product attention used for the Transformer model. We then perform self-attention on the nodes, a shared at...