GLUE- A MULTI-TASK BENCHMARK AND ANALYSIS PLATFORM FOR NATURAL LANGUAGE UNDERSTAND- ING_1648543746532.pdf
Representing and Recommending Shopping Baskets with Complementarity, Compatibility, and Loyalty_1648543746623.pdf
[Airbnb Embedding] Real-time Personalization using Embeddings for Search Ranking at Airbnb (Airbnb 2018)_1648543746938.pdf
[Airbnb]Real-time Personalization using Embeddings for Search Ranking at Airbnb_1648543747527.pdf
[Alibaba Embedding] Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba (Alibaba 2018)_1648543747945.pdf
[BERT]BERT- Pre-training of Deep Bidirectional Transformers for Language Understanding_1648543748129.pdf
[Bert-flow]On the Sentence Embeddings from Pre-trained Language Models_1648543748310.pdf
[Bert]Well Read Students Learn Better On the Importance of Pretraining Compact Models_1648543748358.pdf
[CNN-LSTM]Learning Generic Sentence Representations Using Convolutional Neural Networks_1648543748374.pdf
[ColBERT]- E_icient and E_ective Passage Search via Contextualized Late Interaction over BERT_1648543748461.pdf
[Cove]Learned in Translation- Contextualized Word Vectors_1648543748503.pdf
[Doc2vec-A]Distributed Representations of Sentences and Documents_1648543748532.pdf
[Doc2vec-B]Document Embedding with Paragraph Vectors_1648543748667.pdf
[Doc2vec]An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation_1648543748711.pdf
[ELMo]Deep contextualized word representations_1648543748773.pdf
[ESIM]Enhanced LSTM for Natural Language Inference_1648543748816.pdf
[Fasttext]Bag of Tricks for Efficient Text Classification_1648543748871.pdf
[Fasttext]Enriching Word Vectors with Subword Information_1648543748904.pdf
[GPT]Improving Language Understanding by Generative Pre-Training_1648543749053.pdf
[Glove] Global Vectors for Word Representation_1648543749159.pdf
[Graph Embedding] DeepWalk- Online Learning of Social Representations (SBU 2014)_1648543749275.pdf
[HNSW]Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs_1648543749410.pdf
[InferSent]Supervised Learning of Universal Sentence Representations from Natural Language Inference Data_1648543749451.pdf
[Item2Vec] Item2Vec-Neural Item Embedding for Collaborative Filtering (Microsoft 2016)_1648543749517.pdf
[LINE] LINE - Large-scale Information Network Embedding (MSRA 2015)_1648543749629.pdf
[LSH] Locality-Sensitive Hashing for Finding Nearest Neighbors (IEEE 2008)_1648543749694.pdf
[MTL]LEARNING GENERAL PURPOSE DISTRIBUTED SENTENCE REPRESENTATIONS VIA LARGE SCALE MULTITASK LEARNING_1648543749784.pdf
[Node2vec] Node2vec - Scalable Feature Learning for Networks (Stanford 2016)_1648543749818.pdf
[Poly-encoders]architectures and pre-training strategies for fast and accurate multi-sentence scoring_1648543749855.pdf
[Representation]Assessing Composition in Sentence Vector Representations_1648543749896.pdf
[Representation]FINE-GRAINED ANALYSIS OF SENTENCE EMBEDDINGS USING AUXILIARY PREDICTION TASKS_1648543749940.pdf
[SDNE] Structural Deep Network Embedding (THU 2016)_1648543750084.pdf
[Sampling]Notes on Noise Contrastive Estimation and Negative Sampling_1648543750094.pdf
[Sentence-BERT]- Sentence Embeddings using Siamese BERT-Networks_1648543750107.pdf
[Siamese]Learning Text Similarity with Siamese Recurrent Networks_1648543750221.pdf
[Transformer]Attention is All you need_1648543750314.pdf
[ULMFit]Universal Language Model Fine-tuning for Text Classification_1648543750429.pdf
[USE]Universal Sentence Encoder_1648543750505.pdf
[User2vec]Author2Vec- Learning Author Representations by Combining Content and Link Information_1648543750565.pdf
[User2vec]Customer2Vec_ Representation learning for customer analytics and personalization_1648543751153.pdf
[User2vec]I Know You’ll Be Back- Interpretable New User Clustering and Churn Prediction on a Mobile Social Application_1648543751738.pdf
[User2vec]client2vec- Towards Systematic Baselines for Banking Applications_1648543751802.pdf
[Word2Vec] Distributed Representations of Words and Phrases and their Compositionality (Google 2013)_1648543751883.pdf
[Word2Vec] Efficient Estimation of Word Representations in Vector Space (Google 2013)_1648543751932.pdf
[Word2Vec] Word2vec Explained Negative-Sampling Word-Embedding Method (2014)_1648543751964.pdf
[Word2Vec] Word2vec Parameter Learning Explained (UMich 2016)_1648543752038.pdf
[Word2Vec]Analogies Explained Towards Understanding Word Embeddings_1648543752086.pdf
[Word2vec]Understanding Word2Vec and Paragraph2Vec_1648543752116.pdf
[XLNet]Generalized Autoregressive Pretraining for Language Understanding_1648543752165.pdf
[doc2vec-BOW]A SIMPLE BUT TOUGH-TO-BEAT BASELINE FOR SENTENCE EMBEDDINGS_1648543752297.pdf
[doc2vec-BOW]Improving a tf-idf weighted document vector embedding_1648543752327.pdf
[item2vec]Factorization Meets the Item Embedding- Regularizing Matrix Factorization with Item Co-occurrence_1648543752421.pdf
[item2vec]unbiased-Learning item embeddings using biased feedbackembeddings_1648543752487.pdf
[quickthought] QuickThought_AN EFFICIENT FRAMEWORK FOR LEARNING SENTENCE REPRESENTATIONS_1648543752544.pdf
[skip-thought] skip thoughts_1648543752597.pdf
[skip-thought]Rethinking Skip-thought- A Neighborhood based Approach_1648543752677.pdf
[skip-thought]Trimming and Improving Skip-thought Vectors_1648543752710.pdf