Why is "Seq2seq" trending?

Search Interest Perspective

• A PatchTST-GRU based heterogeneous seq2seq model with numerical weather prediction refinement for multi-step wind power forecasting - Nature
• ATT-enhanced inception-BiGRU-Seq2Seq model for short-term user-level load forecasting during heatwaves - Frontiers
• Climate-adaptive energy forecasting in green buildings via attention-enhanced Seq2Seq transfer learning - Nature
• Neural-enhanced motion-to-EMG: refining simulated muscle activity from musculoskeletal models using a Seq2Seq approach - Frontiers
• Quantum Encoder Supports Smoother Market Trajectories and Outperforms Classical Methods in 14 Tests - Quantum Zeitgeist
• A Comprehensive Guide to Neural Machine Translation using Seq2Sequence Modelling using PyTorch. - Towards Data Science
• Introducing tf-seq2seq: An Open Source Sequence-to-Sequence Framework in TensorF - research.google
• Data-driven battery degradation prediction: Forecasting voltage-capacity curves using one-cycle data - Wiley Online Library
• Seq2Seq modelling for English to French Translations - Towards Data Science
• Seq2Seq-based GRU autoencoder for anomaly detection and failure identification in coal mining hydraulic support systems - Nature
• An improved LSTM-Seq2Seq-based forecasting method for electricity load - Frontiers
• Sequence to sequence architecture based on hybrid LSTM global and local encoders approach for meteorological factors forecasting - Nature
• Neural Machine Translation using a Seq2Seq Architecture and Attention (ENG to POR) - Towards Data Science
• Energy Investment Risk Assessment for Nations Via Seq2seq Model - Frontiers
• A lightweight neural attention-based model for service chatbots - Nature
• [Deep learning] How to build an emotional chatbot (Part 2 the dialogue system) - Towards Data Science
• Application of Seq2Seq Models on Code Correction - Frontiers
• Enhanced matrix inference with Seq2seq models via diagonal sorting - Nature
• Multistep ahead atmospheric optical turbulence forecasting for free-space optical communication using empirical mode decomposition and LSTM-based sequence-to-sequence learning - Frontiers
• Sequence-to-sequence models with attention mechanistically map to the architecture of human memory search - Nature
• Paradigm shift in natural language processing - EurekAlert!
• Beautifully Illustrated: NLP Models from RNN to Transformer - Towards Data Science
• AlexaTM 20B is Amazon’s New Language Super Model Which is Also Capable of Few-Shot Learning - Medium
• Facebook’s Flexible ‘RAG’ Language Model Achieves SOTA Results on Open-Domain QA - syncedreview.com
• An electricity load forecasting model based on multilayer dilated LSTM network and attention mechanism - Frontiers
• Transformer models…How did it all start? - Towards Data Science
• Machine Translation - Towards Data Science
• From Nĭ Hăo to Better Potential Medicines: How Language Translation Technology Is Being Applied to Drug Design - Pfizer
• TensorRT 4 Accelerates Neural Machine Translation, Recommenders, and Speech | NVIDIA Technical Blog - NVIDIA Developer
• New Technique Improves Control of Lower Limb Assistive Devices - ScienceBlog.com
• Building NLP Powered Applications with Hugging Face Transformers - Towards Data Science
• Building Your Own Neural Machine Translation System in TensorFlow - research.google
• Using a Grapheme to Phoneme Model in Cisco’s Webex Assistant - Cisco Blogs
• An end-to-end LSTM-Attention based framework for quasi-steady-state CEST prediction - Frontiers
• Grammatical Error Correction with Machine Learning – Overview and Implementation - Towards Data Science
• Artificial intelligence-driven ensemble deep learning models for smart monitoring of indoor activities in IoT environment for people with disabilities - Nature
• DAMO Academy Proposes One For All, a Task- and Modality-Agnostic Framework for Multimodal and Uni-Modal Understanding and Generation - syncedreview.com
• ARK Invest: AI training costs dropped 100-fold between 2017 and 2019 - VentureBeat
• Identifying cause-and-effect relationships of manufacturing errors using sequence-to-sequence learning - Nature
• Encode, Tag and Realize: A Controllable and Efficient Approach for Text Generati - research.google
• Salesforce AI’s CodeT5+ Open Code LLMs Flexibly Adapt to Diverse Downstream Code Understanding and Generation Tasks - syncedreview.com
• Robust Understanding of Robot-Directed Speech Commands Using Sequence to Sequence With Noise Injection - Frontiers
• Building a Python Code Generator - Towards Data Science
• Deep learning for dense Z-spectra reconstruction from CEST images at sparse frequency offsets - Frontiers
• Neural Machine Translator with Less than 50 lines of Code + Guide - Towards Data Science
• Paper Walkthrough: Attention Is All You Need - Towards Data Science
• Long-Form QA beyond ELI5: an updated dataset and approach - Towards Data Science
• OpenAI begins publicly tracking AI model efficiency - VentureBeat
• The power of constrained language models. - Towards Data Science
• The Transformer Model - Towards Data Science
• Introducing FELIX: Flexible Text Editing Through Tagging and Insertion - research.google
• Dynamic memory networks. Machine learning has made it possible… | by Manish Pawar - DataDrivenInvestor
• Adding an attention mechanism to RNNs - KDnuggets
• What’s the deal with Chatbots?. “Please stay on the line. All of our… | by Abhinit Patil - DataDrivenInvestor
• Top Machine Learning Projects Launched By Google In 2020 (Till Date) - Analytics India Magazine
• Abstractive Text Summarization with Deep Learning - Times Internet