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Transformer Modifications
Published in Uday Kamath, Kenneth L. Graham, Wael Emara, Transformers for Machine Learning, 2022
Uday Kamath, Kenneth L. Graham, Wael Emara
UT has many commonalities with the existing neural architectures, such as the Neural GPU [136] and the Neural Turing Machine [98]. It can also be shown to be equivalent to a multi-layer transformer with tied parameters across its layers. Graves proposed Adaptive Computation Time (ACT), which allows RNNs to learn dynamically how many computational steps to take between accepting input and emitting an output (ponder time) to overcome the issues of a fixed number of computational steps per symbol. UT incorporates a dynamic ACT halting mechanism to each position inspired by Graves's ACT to condition the computation per symbol based on the complexity. The research shows that UTs outperform standard transformers on a wide range of NLP and NLU and achieve a new state-of-the-art in complex tasks such as the LAMBADA language modeling.
Deep Probabilistic Machine Learning for Intelligent Control
Published in Alex Martynenko, Andreas Bück, Intelligent Control in Drying, 2018
Gated recurrent networks like LSTM and GRU are important networks that have gained increasing popularity for temporal processing. Each processing unit in these networks has some form of gated memory. An additional step is to take the idea further in the form of an external memory. The first version of such a model was the neural Turing machine (NTM), which was later refined as the differentiable neural computer (DNC). A graphical outline of such a model is illustrated in Figure 11.12.
Evolution of Long Short-Term Memory (LSTM) in Air Pollution Forecasting
Published in Monika Mangla, Subhash K. Shinde, Vaishali Mehta, Nonita Sharma, Sachi Nandan Mohanty, Handbook of Research on Machine Learning, 2022
Satheesh Abimannan, Deepak Kochhar, Yue-Shan Chang, K. Thirunavukkarasu
Neural Turing machine (NTM), Long short-term memory (LSTM), and Gated recurrent units (GRU) are a few types of RNN models. LSTM has three gates such as input, output, and forget gate. GRU has two gates such as reset and update gate and couples forget as well as input gates. GRU has
Memory-augmented meta-learning on meta-path for fast adaptation cold-start recommendation
Published in Connection Science, 2022
Tianyuan Li, Xin Su, Wei Liu, Wei Liang, Meng-Yen Hsieh, Zhuhui Chen, XuChong Liu, Hong Zhang
In general, after the training parameters have been trained, the neural network will put the samples directly into the trained model for calculation, and then retrieve the results without interacting with the memory. For few-shot learning with sparse data, or models with human–computer interaction, it is difficult to achieve the goal only by connecting calculation between parameters within the model. Weston et al. (2014) first implemented the memory-augmented network using the neural Turing machine model, which consists of a controller and a memory module. The controller writes data to the memory module with a writing head and reads data from the memory module with a reading head. In the memory-augmented network, the feature information is closely associated with the corresponding label in the writing process, and the feature vector is accurately classified in the reading process.