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※ Writing while taking a course 「Stanford CS224N NLP with Deep Learning」 ※ https://www.youtube.com/watch?v=0LixFSa7yts&list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4&index=6&t=2101sLecture 7 - Translation, Seq2Seq, Attention Neural machine translation means you're using a neural network to do machine translation. But in practice, it's meant slightly more than that. It has meant that we're going to buil..

※ Writing while taking a course 「Stanford CS224N NLP with Deep Learning」 ※ https://www.youtube.com/watch?v=0LixFSa7yts&list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4&index=6&t=2101sLecture 6 - Simple and LSTM RNNs We can regard the hidden states as a representation of a word in context. That the low, that we have just a word vector for terribly. But we then looked at our context and we've created a hid..

※ Writing while taking a course 「Stanford CS224N NLP with Deep Learning」 ※ https://www.youtube.com/watch?v=0LixFSa7yts&list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4&index=6&t=2101sLecture 6 - Simple and LSTM RNNsAs to understanding why something that's different is happening here, the thing to notice is that the cell state from t-1 is passing right through to be the cell state at time t, without very ..

https://arxiv.org/pdf/2107.03502AbstractThe imputation of missing values in time series has many applications in healthcare and finance. While autoregressive models are natural candidates for time series imputation, score-based diffusion models have recently outperformed existing counterparts including autoregressive models in many tasks such as image generation and audio synthesis, and would be..

https://arxiv.org/pdf/2307.00754https://github.com/17000cyh/IMDiffusion.gitABSTRACT Anomaly detection in multivariate time series data is of paramount importance for ensuring the efficient operation of large-scale systems across diverse domains. However, accurately detecting anomalies in such data poses significant challenges due to the need for precise modeling of complex multivariate time seri..

https://arxiv.org/pdf/1606.03657AbstractThis paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner. InfoGAN is a generative adversarial network that also maximizes the mutual information between a small subset of the latent variables and the observation. We derive a ..

https://arxiv.org/pdf/1411.1784Abstract Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits condi..

https://arxiv.org/pdf/1705.08868Abstract Adversarial learning of probabilistic models has recently emerged as a promising alternative to maximum likelihood. Implicit models such as generative adversarial networks (GAN) often generate better samples compared to explicit models trained by maximum likelihood. Yet, GANs sidestep the characterization of an explicit density which makes quantitative ev..