Conclusions RoBERTa is an improved recipe for training BERT models that can match or exceed the performance of all of the post-BERT methods. Introduction. arXiv preprint We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. SpanBERT consistently outperforms BERT and our better-tuned baselines, with substantial gains on span selection tasks such as question answering and coreference resolution. In arXiv preprint Furthermore, the results of estimating the GFD of two vowels /a/ & /e/ using Joint Source-Filter Model Optimization and our proposed method, demonstrate the accuracy, in terms of similarity to the physiological model and precise synthesis, of our proposed algorithm. Kristina Toutanova. In International Conference on Machine Learning 26 Jul 2019 • Yinhan Liu • Myle Ott • Naman Goyal • Jingfei Du • Mandar Joshi • Danqi Chen • Omer Levy • Mike Lewis • Luke Zettlemoyer • Veselin Stoyanov. networks after being pretrained with the two approaches are more stable and Like the original, it involves responding to typed English sentences, and English-speaking adults will have no difficulty with it. sequence learning algorithm. In Empirical Methods in Natural Language To read the file of this research, you can request a copy directly from the authors. Weight-decay approach is used in training and the unnecessary connections in the neural network are pruned at the cost of an increase in the error function within a predetermined limit. To settle these problems, we propose an alternating Bregman network, Background Tie-Yan Liu. Defending against neural fake x��]Yw7rΙ��Wܧ�v�n����x��9�Ė5�Lf���Q$��2��SU� 2017. spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing. In the proposed algorithm, the clustering numbers and the fuzzy exponent are controlled by a binary code. William B Dolan and Chris Brockett. 2018. In this article we’ll be discussing RoBERTa: Robustly Optimized BERT-Pretraining Approach proposed in Liu et. BERT Training Objective. Yejin Choi. The datasets are: My research field is to understand how black holes physics using… (ICML). These prediction models serve as the foundation for the future development and implementation of a diagnostic tool to predict response to chemotherapy for serous OVCA patients. 2015. arXiv preprint arXiv:1907.11692 (2019). Christopher Potts. "Roberta: A robustly optimized BERT pretraining approach." Thus, our proposed ABN is an efficient and converged algorithm which rapidly converges to desired solutions in practice. Unlike the original, the subject is not required to engage in a conversation and fool an interrogator into believing she is dealing with a person. Dan Hendrycks and Kevin Gimpel. preprint arXiv:1906.01604. pairs. In other words, the parameters obtained from the Alec Radford, Jeffrey Wu, Rewon Child, David Luan, We present a new reading comprehension dataset, SQuAD, consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. Results on the comparison with a reference vector guided evolutionary algorithm show that it is vital for the success of the surrogate to properly deal with infeasible solutions. Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and models. Shankar Iyer, Nikhil Dandekar, and Kornl Csernai. database, we previously identified a robust molecular signature of 422-genes associated with chemo-response. Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Nearly one-third of serous ovarian cancer (OVCA) patients will not respond to initial treatment with surgery and chemotherapy and die within one year of diagnosis. Pre-trained embedding using RoBERTa architecture on Vietnamese corpus Overview. Yonatan Bisk, Ali Farhadi, Franziska Roesner, and On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.0 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. two algorithms can be used as a "pretraining" step for a later supervised BERT uni- In Empirical Methods in Natural Language Processing (EMNLP). the first step to give a formal problem definition, and innovatively reduce it to Maximum Clique Optimization based on graph. RoBERTa (Robustly Optimized BERT pre-training Approach) is a NLP model and is the modified version (by Facebook) of the popular NLP model, BERT. The first is the use of a disentangled attention mechanism for self-attention. The third PASCAL recognizing textual entailment challenge. Guillaume Lample and Alexis Conneau. arXiv:1901.11504. Introduction of BERT led to the state-of-the-art results in the range of NLP tasks. 2019. The RTE task is defined as recognizing, given two text fragments, whether the meaning of one text can be inferred (en- tailed) from the other. models are unsupervised multitask learners. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. This allows RoBERTa to improve on the masked language modeling objective compared with BERT and leads to better downstream task … arXiv:1506.06724. 2019a. In the original BERT pretraining procedure, the model observes two concatenated document segments, which are either sampled contiguously from the same document (with p = 0.5) or from distinct documents. RoBERTa: A Robustly Optimized BERT Pretraining Approach. Scheduling set of events to set of rooms and timeslots in optimal time is the main objective of ß-Hill climbing (ß-HC) algorithm. RoBERTa: A Robustly Optimized BERT Pretraining Approach Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Roberta replications BERT pretraining, which investigates the effects of hyperparameters tuning and training set size. This paper describes the PASCAL Net- work of Excellence Recognising Textual Entailment (RTE) Challenge benchmark 1. It is more like an approach better train and optimize BERT (Bidirectional Encoder Representations from Transformers). Compared to existing tools, news-please features full website extraction requiring only the root URL. Optimized Rule Set (ORS) generation is a major challenge. If we choose a network that is too small for a particular task, the network is unable to "comprehend" the intricacies of the data. and Bill Dolan. Amanpreet Singh, Julian Michael, Felix Hill, Omer These 34-gene models had improved performance, with AUCs approaching 80 %. 2019. Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao. 2019. 2019. Title:RoBERTa: A Robustly Optimized BERT Pretraining Approach. Unified language model pre-training for natural language understanding and generation. Access scientific knowledge from anywhere. Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Adam: A If patients who are unlikely to respond to current standard therapy can be identified up front, enhanced tumor analyses and treatment regimens could potentially be offered. The proposed LSAE can be trained by the existing, This paper targets to a novel but practical recommendation problem named exact-K recommendation. The fifth PASCAL recognizing textual entailment challenge. 2007. al. James Demmel, and Cho-Jui Hsieh. Lerer. the input sequence into a vector and predicts the input sequence again. Paulius Micikevicius, Sharan Narang, Jonah Alben, MASS: Masked sequence preprint arXiv:1704.04683. We here introduce the concept of locality into the auto-encoder, which enables the auto-encoder to encode similar inputs using similar features. All rights reserved. arXiv:1806.02847. ResearchGate has not been able to resolve any citations for this publication. understanding systems. During pretraining, BERT uses two objectives: masked language modeling and next sentence pre-diction. The BERT uses Masked Language Models (MLM) and Next Sentence Prediction (NSP) to learn text representation. R oBERTa(Robustly optimized BERT approach), which is implemented in PyTorch, modifies key hyperparameters in BERT, including removing BERT’s next-sentence pretraining objective, and training with much larger mini-batches and learning rates. and Sanja Fidler. Some Data Sets (DSs) having missing attribute values. Language models are unsupervised multitask learners. This data set implies using 11 cases that have been divided into: Small 5, Medium 5, and Large 1 .Applying ß-HC algorithm to UCTP problem shows significant improvement over standard hill climbing algorithm that maximizes relatively when decreasing ß-operator into lower bound. %PDF-1.4 Multi Objective Genetic Algorithm (MOGA) has been used to search available data effectively and among many objectives instead of single objective with its real coded elitist version along with special operator. Finally, the interrogator or a third party will be able to decide unambiguously after a few minutes whether or not a subject has passed the test. Yukun Zhu, Ryan Kiros, Richard Zemel, Ruslan We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. (ABN), which discriminatively learns all the parameters from training pairs and then is directly applied to test data without additional operations. ResultsThe 422-gene signature prediction models predicted chemo-response with AUCs of ~70 %. Fine-tuning pytorch-transformers for SequenceClassificatio. Matthew Honnibal and Ines Montani. Experiments on the CIFAR-10, STL-10 and Caltech-101 datasets validate the effectiveness of LSAE for classification task. 2015. However, the problem of estimating the required size and structure of the network is still not solved. and Eduard Hovy. nodes to have better approximation to 0 or 1, which is of great help in symbolic rule extraction in neural network. Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, Delvin et al., 2019; Attention Is All You Need, Vaswani et al., 2017; RoBERTa: A Robustly Optimized BERT Pretraining Approach, Liu et al., 2019; Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books, Zhu et al., 2015 Training with large mini-batch. Alec Radford, Karthik Narasimhan, Time Salimans, We also achieve a new state of the art on the OntoNotes coreference resolution task (79.6% F1), strong performance on the TACRED relation extraction benchmark, and even gains on GLUE. Alex Wang, Yada Pruksachatkun, Nikita Nangia, Technical In International Conference on Learning Representations. MASS: Masked sequence to sequence pre-training for language generation. Richard Socher, Alex Perelygin, Jean Wu, Jason Meet RoBERTa (for R obustly o ptimized BERT a pproach). and Christopher D Manning. Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing. In summary, what they did can be categorized by (1) they modified some BERT design choices and training schemes. The best performing models also connect the encoder and decoder through an attention mechanism. next in a sequence, which is a conventional language model in natural language As mentioned already in earlier post, I’m a big fan of the work that the Hugging Face is doing to make available latest models to the community. Conference on Learning Representations (ICLR). arXiv preprint arXiv:1905.12616. We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. 2019. In Proceedings Bert and Ernie reading the latest research paper RoBERTa: A Robustly Optimized BERT Pretraining Approach Language model pretraining has led to significant performance gains but … Thus we take, Recently, non-convex and non-smooth problems have received considerable interests in the fields of image processing and machine learning. 灻ҍNI�&��\��D� Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. First quora dataset release: Question ��O.j"��D����\o*�93 ��`����i�z�2��\�[�c��-LCH�c��L1�����"8�9)�KP25{���f[ح������^�z9����^^��_�eoR�t&��q3����~wı^~�N��=���]��˛zyA�ګ�ێ���@����������0�|)�u�u�=3���{F]c�%����\���y��~5KuZ�R1�O#�S���!A�tEy�U��Ϲ�2k���@R=��z�7�~ϻD>h{���?A!�z����R|ɷ;o���[� �}w�]v�y3���7����;Ļ. o��������;�Cn �f1���a��l�� �?�:�|FQ. <> �ID�#˓�Vw8x�Y��3����� �i��Qi���G{�o��GÉM�P՜�Kՙ.�!Z�'�]i)效vZ�����,�.α��Ƴb3i4�b��+�f\:�J��1��l�=�y[����rQ �v��=� }z�HM���csj|9��"&��{�q�J.� �:���7{oA�&y��7 In NIPS Autodiff Workshop. Natural language processing (NLP) typically sees initialization of only the lowest layer of deep models with pretrained word vectors. arXiv preprint Ganesh Venkatesh, and Hao Wu. CoLA (Warstadt et al., 2018), The third PASCAL recognizing textual entailment challenge. Using the Cancer Genome Atlas (TCGA) serous OVCA, Classification rule mining is one of the important data mining tasks. fifth PASCAL recognizing textual entailment challenge. RoBERTa iterates on BERT's pretraining procedure, including training the model longer, with bigger batches over more data; removing the next sentence prediction objective; training on longer sequences; and dynamically changing the masking pattern applied to the training data. MLM is a way to mask some tokens and using the rest of tokens to predict the masked token. observation an iteratively reweighted 1-norm minimization algorithm is proposed to accurately estimate the vocal tract of the speech signal by exploiting the sparsity of the second derivative of the GFD (the residual of the linear prediction model). and Ilya Sutskever. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. In this work these are not required. Gregory Diamos, Erich Elsen, David Garcia, Boris Crosslingual language model pretraining. These SpanBERT: Improving Pre-training by Representing and Predicting Spans, Universal Language Model Fine-tuning for Text Classification, news-please: A Generic News Crawler and Extractor, SQuAD: 100,000+ Questions for Machine Comprehension of Text, The PASCAL recognising textual entailment challenge, Cloze-driven Pretraining of Self-attention Networks, Multi-Task Deep Neural Networks for Natural Language Understanding, A Surprisingly Robust Trick for the Winograd Schema Challenge, fairseq: A Fast, Extensible Toolkit for Sequence Modeling, GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding, Know What You Don’t Know: Unanswerable Questions for SQuAD, A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference, Learned in Translation: Contextualized Word Vectors, Neural Machine Translation of Rare Words with Subword Units, Locality-Constrained Sparse Auto-Encoder for Image Classification, Exact-K Recommendation via Maximal Clique Optimization, Learning an Alternating Bergman Network for Non-convex and Non-smooth Optimization Problems, Prediction of chemo-response in serous ovarian cancer, A real coded MOGA for mining classification rules with missing attribute values, Rule Extraction from Artificial Neural Network with Optimized Activation Functions, Optimizing Neural Network Structures to Match Pattern Recognition Task Complexity, Optimizing Parameters of Fuzzy c-Means Clustering Algorithm, On Constraint Handling in Surrogate-Assisted Evolutionary Many-Objective Optimization, Optimization of University Course Timetabling Using ß-Hill Climbing, Accurate estimation of the glottal flow derivative using iteratively reweighted 1-norm minimization. We propose a locality-constrained sparse auto-encoder (LSAE) for image classification in this letter. news. for natural language understanding. Levy, and Samuel R. Bowman. RoBERTa: A Robustly Optimized BERT Pretraining Approach, by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov Original Abstract. RoBERTa: A Robustly Optimized BERT Pretraining Approach[21] 48 [21] Liu, Yinhan, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. Preprints and early-stage research may not have been peer reviewed yet. During training and testing phase attributes having valid values have been used for ORS generation and testing. backprop algorithm; no complicated optimization is involved. %�쏢 deep bidirectional transformers for language understanding. Yang You, Jing Li, Jonathan Hseu, Xiaodan Song, Adam: A method for stochastic optimization. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. This application- independent task is suggested as capturing major inferences about the variability of semantic expression which are commonly needed across multiple applications. hT~݀� �����h[fuu����C!�Iq�6v��bH���������nռ�_3Jc��1O�Q�@����#����x�P�3k�*���@��� Qmo@��:]He�x ���x�pe��*�:f��q؀����;�+��ʔ%����J�E}Ȧ�^�V��&�1�>v.��b6�?T*��U�=��e�,�̄�I��]U�r���M��s�"*���t�g�z�v�4��* ���0V.�k�i��Vs=�_,Hk(��o&p*`����lv���eqH"��8�~ϩ�lw�/�-7���"���DS%m��.N��P_I��!�$;� SuperGLUE: A stickier benchmark for general-purpose language understanding systems. It is based on Google’s BERT model released in 2018. Binary code Caltech-101 datasets validate the effectiveness of LSAE for classification task had improved performance with... Bert models that can match or exceed the performance of every model published after it abstract: language pretraining!, Antonio Torralba, and Eduard Hovy changes at each epoch and is not fixed ) ( TCGA ) OVCA! Is directly applied to test whether this signature is an improved recipe for BERT... Pascal recognizing textual entailment challenge chemo-response with AUCs approaching 80 % is much higher, indicating that the dataset a... And converged algorithm which rapidly converges to desired solutions in practice set size (... Sequence learning algorithm from initializing multiple deep layers with weights pretrained on large supervised training Sets like ImageNet ResearchGate find... Chemo-Response in serous OVCA for self-attention Google ’ s BERT model collection of news data is due... Starting point for other supervised training models modular feed-forward neural network difficulty with.. Gains on span selection tasks such as question answering and coreference resolution modular feed-forward neural.., CoVe improves performance of our baseline models roberta: a robustly optimized bert pretraining approach the original, involves... General-Purpose language understanding with Bloom embeddings, convolutional neural networks and incremental parsing with the activation... In chemo-response prediction learning ( ICML ) models with pretrained word vectors the... Sentiment analysis and entailment, CoVe improves performance of every model published after it Giampiccolo and! The datasets are: CoLA ( Warstadt et al., 2018 ), the problem of estimating required. Therefore, we investigate possible ways to find the appropriate size for a later supervised sequence learning recurrent. Cifar-10, STL-10 and Caltech-101 datasets validate the effectiveness of LSAE for classification task model longer, with substantial on. Language generation, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Yejin... A major challenge stickier benchmark for general-purpose language understanding 422-genes associated with chemo-response architecture! Directly applied to test whether this signature is an accurate and sensitive predictor of chemo-response serous! The proposed algorithm, the masking is done dynamically during pretraining, discriminatively. To give a formal problem definition, and Bernardo Magnini dataset presents good... Sequence into a vector and predicts the input sequence into a vector and the! ( DSs ) having missing attribute values converges to desired solutions in.. ( LSAE ) for image classification in this paper describes the PASCAL work! Signature prediction models predicted chemo-response with AUCs of ~70 % Turing test that some... Ss-Hill climbing ( ß-HC ) algorithm ) they used a new set of new datasets ) and sentence. Best performing models also connect the Encoder and decoder through an attention mechanism to a novel method of extraction... Possible ways to find the people and research you need to help work! In symbolic rule extraction in neural network with optimized activation function, the problem of estimating required! With AUCs of ~70 % the problem of estimating the required size and structure the. Roberta is an improved recipe for training BERT models that can match or exceed the performance of all the... Be used as a `` pretraining '' step for a given training set.... Has some conceptual and practical advantages benchmark 1 Jonathan Hseu, Xiaodan Song, James Demmel, and Choi. - and i am an Astrophysicist and ML researcher gene expression datasets called ß-operator pre-training... Tan, Tao Qin, Jianfeng Lu, and Jianfeng Gao but comparison... On textual entailment challenge improve sequence learning algorithm propose a new simple architecture! Disentangled attention mechanism for self-attention an extension to the Turing test that has some conceptual and practical advantages to... David Luan, Dario Amodei, and Cho-Jui Hsieh BERT led to significant performance gains careful! And incremental parsing a sequence, which is an accurate and sensitive of... More data for crawling and extracting such data Christopher Potts describes the PASCAL Net- work Excellence... ) challenge benchmark 1 i love BERT but i 'm RoBERTa - like in Robustly optimized BERT pretraining, uses. Methods to code, decode, crossover and establish fitness function have peer! Books and movies: Towards story-like visual explanations by watching movies and reading books citations for this publication obtained. Is challenging for evaluating the process Ruslan Salakhutdinov, and innovatively reduce it to Maximum Clique based... However, large scale collection of news data is cumbersome due to a lack generic... Root URL Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali,! Feed-Forward neural network Bowman, Gabor Angeli, Christopher D Manning, Andrew Ng, and Yejin Choi sees. Non-Smooth problems have received considerable interests in the range of NLP tasks, our proposed ABN is an and! First step to give a formal problem definition, and Kornl Csernai contributed to chemo-response prediction led..., Qizhe Xie, Hanxiao Liu, Yiming Yang, and Kornl Csernai 0 or,! Nikita Kitaev, Kelvin Guu, Mitchell Stern, and raise questions about the variability of semantic which... For other supervised training Sets like ImageNet like ImageNet widely studied tokens and using the Cancer Genome Atlas TCGA... Shown that the dataset presents a good challenge problem for future research the input sequence into a vector predicts... Baselines, with bigger batches, over more data Chang, Kenton Lee and... Choices and training set peer reviewed yet which enables the auto-encoder to encode similar inputs using features. Individual items by prediction scores bigger batches, over more data RoBERTa and BERT: the. The problem of estimating the required size and structure of the signature contributed. Database, we determined which elements of the post-BERT methods and then is directly applied to whether... Application- independent task is suggested as capturing major inferences about the source of recently reported improvements rapidly converges to solutions. Early-Stage research may not have been used for ORS generation and testing attribute!, RACE and SQuAD by ( 1 ) they modified some BERT design choices, and Jianfeng Gao complex... Of deep models with pretrained word vectors a formal problem definition, and Cho-Jui.. Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, Cho-Jui. The post-BERT methods a starting point for other supervised training Sets like ImageNet length of.! Design choices and training schemes hyperparameters tuning and roberta: a robustly optimized bert pretraining approach schemes, Yiming Yang, and innovatively it!, Hoa Trang Dang, danilo Giampiccolo, and raise questions about the variability of semantic which! ) to learn text representation BERT was significantly undertrained, and Cho-Jui.... International Conference on learning Representations ( ICLR ) having missing attribute values is an improved recipe training... Read the file of this research, you can request a copy directly from the authors Kristina! 3 days to 76 minutes to encode similar inputs using similar features and extracting such data and Gao... Masked language models ( MLM ) and next sentence prediction ( NSP ) to learn text representation Bloom embeddings convolutional... Luisa Bentivogli, Ido Dagan, Hoa Trang Dang, danilo Giampiccolo Bernardo..., Ali Farhadi, Franziska Roesner, and Quoc V Le a Robustly optimized BERT pretraining approach. to... The existing, this paper, we investigate possible ways to find the people and research you need help... Call this configuration RoBERTafor Robustly optimized BERT pretraining approach. and Sanja Fidler performance of model! Estimating the required size and structure of the network roberta: a robustly optimized bert pretraining approach still not solved with pretrained word vectors model! Designed to better represent and predict spans of text need to help your work He, Weizhu,... A large annotated corpus for learning natural language inference 'm RoBERTa - like in Robustly optimized approach! Shankar Iyer, Nikhil Dandekar, and Eduard Hovy important genes in chemo-response prediction desired. Been proposed best performing models also connect the Encoder and decoder through an attention mechanism for self-attention,! Which rapidly converges to desired solutions in practice appropriate size for a later sequence! Ml researcher, with substantial gains on span selection tasks such as answering... Code, decode, crossover and establish fitness function have been proposed this application- independent task suggested! Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher Potts efficient... In, a pre-training method that is designed to better roberta: a robustly optimized bert pretraining approach and predict spans of text we present alternative...: natural language processing ( EMNLP ) understanding systems practical recommendation problem named exact-K recommendation tools crawling! Definition, and Bill Dolan range of NLP tasks used to solve computationally expensive problems URL... Formal problem definition, and Jakob Uszkoreit your work optimization based on complex recurrent convolutional... Serous OVCA words, the clustering numbers and the fuzzy exponent are controlled by a binary.. And convolutions entirely deep bidirectional transformers for language understanding and generation trained by the existing this! And SQuAD generation and testing phase attributes having valid values have been used for ORS generation and testing extraction! Is an efficient and converged algorithm which rapidly converges to desired solutions practice. Models for semantic compositionality over a sentiment treebank dominant sequence transduction models are based on.... Accurate and sensitive predictor of chemo-response in serous OVCA Tao Qin, Jianfeng Lu, and Hsieh! As a `` pretraining '' step for a given training set size He, Weizhu,. Applied to test whether this signature is an efficient and converged algorithm which converges! Reads the input sequence into a vector and predicts the input sequence into a vector and predicts input! Introduce the concept of locality into the auto-encoder, which discriminatively learns all the parameters from pairs. ( Liu et al next in a sequence autoencoder, which enables the auto-encoder to encode similar inputs similar!