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TransformerGAN: Symbolic music generation using a learned

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We highlight a few additional tricks that helped us train on long sequences 2 TransformerGAN Given a music sequence xr from the the unknown data distribution - Information from assets.amazon.science

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Two Decades of Recommender Systems at Amazoncom

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IEEE Internet Computing article has faced many challenges and seen much development over the years Here we describe some of the updates improve - Information from assets.amazon.science

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Video Contrastive Learning with Global Context Amazon

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j )) At this point the problem is formulated as a (N+1)way classifi cation task and can be optimized by InfoNCE loss [47] as LNCE(q p N−) − log - Information from assets.amazon.science

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Crosslingual Alignment Methods for Multilingual BERT

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Multilingual BERT (mBERT) (Devlin et al 2019) is the BERT architecture trained on data from 104 languages where all languages are embedded in the - Information from assets.amazon.science

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A Selfsupervised Model for Speech Representation Learning

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Wav2vec C introduces a novel representation learning tech RNNT [18 19 20] ASR models are widely used for deploy able endtoend speech recognition - Information from assets.amazon.science

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Structured Variationally Autoencoded Optimization Amazon

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erating Variational autoencoder (SGVAE) We de scribe and used it in the context of mapping kernel combinations produced by a contextfree grammar into - Information from assets.amazon.science

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Continual Learning in Practice Amazon Science

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The principle characteristic of a ML system is that it is datadriven Given these we can learn the (locally) optimal policy: the best action to take - Information from assets.amazon.science

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A simple transferlearning extension of Hyperband Amazon

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Hyperband [1] has become a popular method to tune the hyperparameters (HPs) of expensive machine learning models whose performance depends on the amount - Information from assets.amazon.science

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Leveraging Hierarchical Representations for Preserving

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stores for training machine learning models and supporting data driven decisions there were existing artifacts such as the Poincaré embeddings - Information from assets.amazon.science

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Neural Machine Translation For Paraphrase Generation

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Our approach consists of machine translation (MT) inspired encoderdecoder deep recurrent neural network We evaluate our model on the impact it has on ASK - Information from assets.amazon.science

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Towards Good Practices in Selfsupervised Representation

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Unsupervised Feature Learning via Non Parametric Instancelevel Discrimination In CVPR 2018 [14] Oord A v d Y Li O Vinyals Representation learning - Information from assets.amazon.science

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enhancingaudioaugmentationmethodswithconsistency

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training deep neural networks is to use data augmentation as it [13] but for unsupervised learning of nonaudio tasks - Information from assets.amazon.science

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COVID19 Knowledge Graph: Accelerating Information

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schema of the data and then enriched with biomedical entity information extracted from the link prediction analysis by extracting graph embeddings - Information from assets.amazon.science

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Contrastive Document Representation Learning with Graph

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our graph document model we design a sim of our approaches in document classification theart transformer models that handle long text input5 - Information from assets.amazon.science

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A simple transferlearning extension of Hyperband Amazon

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Hyperband [1] has become a popular method to tune the hyperparameters (HPs) of combining random search to a successive halving technique to reallocate - Information from assets.amazon.science

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DISENTANGLEMENT FOR AUDIOVISUAL EMOTION

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manifested through speech and facial expressions each providing tional states we colearn identity representations with an emotion - Information from assets.amazon.science

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ReStGAN: A step towards visually guided shopper experience

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ception scoregender and 86% reduction in FID score (lower is better) over the traditional losses for the negative pairs in the matchingaware discrim - Information from assets.amazon.science

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Detecting and Correcting for Label Shift with Black Box

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Abstract Faced with distribution shift between training and test set we wish to detect and quantify the shift and to correct our classifiers without test - Information from assets.amazon.science

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fewshotqaaframeworkforfewshotlearningofquestion

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Similarly it would require knowing the answer length in advance to have SpanBERT predict multimasked tokens Given that their pretraining objectives - Information from assets.amazon.science

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dualreaderparseronhybridtextualandtabularevidencefor

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for Open Domain Question Answering Alexander Hanbo Li Patrick Ng Peng Xu Henghui Zhu Zhiguo Wang Bing Xiang AWS AI Labs Amazon - Information from assets.amazon.science

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Learning Deep Fair Graph Neural Networks Amazon Science

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One way to reach this goal is to learn a data representation that is expressive enough to describe the data and fair enough to remove the possibility to - Information from assets.amazon.science

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A Systematic Study of Model Distillation Methods in Natural

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“BoolQ”(Wang et al 2019a) predicted strate gies achieve 1002 distillation ratios (fraction official GLUE leaderboard to obtain test set results - Information from assets.amazon.science

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Regularized Graph Convolutional Networks for Short Text

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Regularized Graph Convolutional Networks for Short Text Classification Kshitij Tayal University of Minnesota CharCNN: Characterlevel CNNs as proposed - Information from assets.amazon.science

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Towards Good Practices in Selfsupervised Representation

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Selfsupervised representation learning has seen remarkable progress in the last few Momentum Contrast for Unsupervised Visual Representation Learning - Information from assets.amazon.science

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using vaes and normalizing flows for oneshot texttospeech

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Specifically we enhance the disentangle ment capabilities of a stateoftheart sequencetosequence based system with a Variational AutoEncoder (VAE) and a - Information from assets.amazon.science

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META QLEARNING Amazon Science

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Rl2 : Fast reinforcement learning via slow reinforcement learning arXiv:161102779 2016 Miroslav Dudık John Langford and Lihong Li - Information from assets.amazon.science

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Tabular Data Concept Type Detection Using StarTransformers

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paper we focus on learningbased approaches for column concept type detection without relying on TaBERT represents each cell by the column name column - Information from assets.amazon.science

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Towards Good Practices in Selfsupervised Representation

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Discriminative Unsupervised Feature Learning with Convolutional Neural Networks In NeurIPS 2014 [13] Wu Z Y Xiong S Yu et al Unsupervised Feature - Information from assets.amazon.science

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Collaborative Deep Denoising Autoencoder Framework for

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Recently deep learningbased recommender systems have been actively investigated [9][10] where each user and item features are combined (or averaged - Information from assets.amazon.science

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Collaborative Deep Denoising Autoencoder Framework for

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Different from existing matrix factorization and deep learning approaches this framework combines autoencoder collaborative filtering and embedded - Information from assets.amazon.science

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Discriminative Representation Loss (DRL) for Continual

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The use of episodic memories in continual learning is an efficient way to prevent the phenomenon of catastrophic for getting - Information from assets.amazon.science

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Short Text Classification using Graph Convolutional Network

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33rd Conference on Neural Information Processing Systems (NeurIPS 2019) Vancouver Canada Page 2 • Product Title Classification (PTC): classifying billions - Information from assets.amazon.science

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Tabular Data Concept Type Detection Using StarTransformers

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TaBERT represents each cell by the column name column We finetune TaBERT for the table column 3https://githubcom/facebookresearch/TaBERT - Information from assets.amazon.science

Information accuracy 100%
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EDUCATION TransformerGAN: Symbolic music generation using a learned

EDUCATION Two Decades of Recommender Systems at Amazoncom

EDUCATION Video Contrastive Learning with Global Context Amazon

EDUCATION Crosslingual Alignment Methods for Multilingual BERT

EDUCATION A Selfsupervised Model for Speech Representation Learning

EDUCATION Structured Variationally Autoencoded Optimization Amazon

EDUCATION Continual Learning in Practice Amazon Science

EDUCATION A simple transferlearning extension of Hyperband Amazon

EDUCATION Leveraging Hierarchical Representations for Preserving

EDUCATION Neural Machine Translation For Paraphrase Generation

EDUCATION Towards Good Practices in Selfsupervised Representation

EDUCATION enhancingaudioaugmentationmethodswithconsistency

EDUCATION COVID19 Knowledge Graph: Accelerating Information

EDUCATION Contrastive Document Representation Learning with Graph

EDUCATION A simple transferlearning extension of Hyperband Amazon

EDUCATION DISENTANGLEMENT FOR AUDIOVISUAL EMOTION

EDUCATION ReStGAN: A step towards visually guided shopper experience

EDUCATION Detecting and Correcting for Label Shift with Black Box

EDUCATION fewshotqaaframeworkforfewshotlearningofquestion

EDUCATION dualreaderparseronhybridtextualandtabularevidencefor

EDUCATION Learning Deep Fair Graph Neural Networks Amazon Science

EDUCATION A Systematic Study of Model Distillation Methods in Natural

EDUCATION Regularized Graph Convolutional Networks for Short Text

EDUCATION Towards Good Practices in Selfsupervised Representation

EDUCATION using vaes and normalizing flows for oneshot texttospeech

EDUCATION META QLEARNING Amazon Science

EDUCATION Tabular Data Concept Type Detection Using StarTransformers

EDUCATION Towards Good Practices in Selfsupervised Representation

EDUCATION Collaborative Deep Denoising Autoencoder Framework for

EDUCATION Collaborative Deep Denoising Autoencoder Framework for

EDUCATION Discriminative Representation Loss (DRL) for Continual

EDUCATION Short Text Classification using Graph Convolutional Network

EDUCATION Tabular Data Concept Type Detection Using StarTransformers

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