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...achieves higher accuracy compared to skipping tokens, implying that paying attention to unimportant tokens is better than completely ignoring them. As the popularity of neural networks has grown, significant attention has been given to make them faster and lighter. In particular, relevant wor ...

...d fine-tuning approach. Very briefly, the transformer architecture defines attention over the embeddings in a layer such that the feedforward weights are a func ...it, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. ...

...STM, and CNN models with various variations applied, such as two models of attention, negative sampling, entity embedding or sentence-only embedding, etc. ..., without attention, has significantly better performance than all others. Attention-based pooling, up-sampling, and data augmentation are also tested, but they ...

...An alternate option is BERT [3] or transformer-based models [4] with cross attention between query and passage pairs which can be optimized for a specific task. ...and <math>d</math>, <math> \theta </math> are the parameters of the cross-attention model. The architectures of these two models can be seen below in figure 1. ...

...c/paper/7255-attend-and-predict-understanding-gene-regulation-by-selective-attention-on-chromatin.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index ...

...nterest for this problem. Structured prediction has attracted considerable attention because it applies to many learning problems and poses unique theoretical a ...alterations to the functions <math>\alpha</math> and <math>\phi</math>. In attention each node aggregates features of neighbors through a function of neighbor's ...

...l which is similar to most image captioning models except that it exploits attention and linguistic information. Several recent approaches trained the captionin ...the authors have reasoned out about the type of phrases and exploited the attention mechanism over the image. The model receives an image as input and outputs ...

'''Title:''' Bi-Directional Attention Flow for Question Answering [1] Bi-Directional Attention Flow For Machine Comprehension - https://arxiv.org/abs/1611.01603 ...

...puting input gradients [13] and decomposing predictions [8], 2) developing attention-based models, which illustrate where neural networks focus during inference ...: Bahdanau et al. (2014) - These are a different class of models which use attention modules(different architectures) to help focus the neural network to decide ...

...ese advanced methods in field of vision. This paper claims that neither of attention, and convolutions are necessary which the claim is proved by its well-stabl ...

The model uses a sequence to sequence model with attention, without input-feeding. Both the encoder and decoder are 3 layer LSTMs, and ...

...t would be interesting to use this backbone with Mask R-CNN and see if the attention helps capture longer range dependencies and thus produce better segmentatio ...

...ecreases towards the lower ranks on result pages due to the reduced visual attention from the user. A more recent click model, referred to as the ''cascade mode ...

...er also described related work in Personalized location recommendation and attention mechanism in the recommendation. The recent studies on location recommendat ...nd <math>W_a</math> and <math>w_t</math> are the learned parameters in the attention layer and aggregation layer. ...

...The sequence of hidden units is then processed by the decoder, a GRU with attention, to produce probabilities over sequences of output characters. ...symbol list of the desired size, we apply a standard encoder-decoder with attention. ...

...n the figure, the memory is read twice, which is termed multiple “hops” of attention. ...e controller, where <math> R_1</math> is a $d$ × $d$ rotation matrix . The attention over the memory can then be repeated using <math> u_1</math> instead of $q$ ...

Attention is all you need. ''CoRR'', abs/1706.03762. ...

...rrent Neural Network and Maximum Entropy-based models have gained a lot of attention and are considered the most successful models. However, the main drawback o ...

...e fact that better translation is generated when using more context in the attention mechanism. ...ighted contextual information summarizing the source sentence x using some attention mechanism. ...

...quently, machine learning and machine reasoning have received considerable attention given the short history of computer science. The statistical nature of mach Little attention has been paid to the rules that describe how to assemble trainable models t ...