End to End

"End-to-end" systems aim to streamline complex processes by integrating multiple stages into a single, unified model, eliminating the need for intermediate steps and potentially improving efficiency and performance. Current research focuses on applying this approach across diverse fields, utilizing architectures like transformers, reinforcement learning, and spiking neural networks to tackle challenges in autonomous driving, robotics, speech processing, and natural language processing. This approach offers significant potential for improving the accuracy, speed, and robustness of various applications, while also simplifying development and deployment.

Papers

March 4, 2022

Detecting Offensive Language on Social Networks: An End-to-end Detection Method based on Graph Attention Networks
Zhenxiong Miao, Xingshu Chen, Haizhou Wang, Rui Tang, Zhou Yang, Wenyi Tang
End to End Graph Attention Network Social Network Graph Attention Text Embeddings Textual Feature Offensive Language Detection Sentiment Feature

March 3, 2022

March 2, 2022

Providing Insights for Open-Response Surveys via End-to-End Context-Aware Clustering
Soheil Esmaeilzadeh, Brian Williams, Davood Shamsi, Onar Vikingstad
End to End DCU Insight AQ Research Survey Semantic Vector Survey Response

February 26, 2022

An End-to-End Transformer Model for Crowd Localization
Dingkang Liang, Wei Xu, Xiang Bai
End to End Accurate Localization Localization Performance Crowd Localization

February 24, 2022

Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image Segmentation
Pooya Ashtari, Diana M. Sima, Lieven De Lathauwer, Dominique Sappey-Marinier, Frederik Maes, Sabine Van Huffel
Convolutional Neural Network Semantic Segmentation Medical Image Segmentation End to End Transformer Based Model Brain Tumor Segmentation Context Modeling Scalable Interpretability

February 23, 2022

An End-to-End Cascaded Image Deraining and Object Detection Neural Network
Kaige Wang, Tianming Wang, Jianchuang Qu, Huatao Jiang, Qing Li, Lin Chang
End to End Object Detection Image Deraining Deep Learning Based Object Low Level Vision Task High Level Vision Task

February 21, 2022

Guided Visual Attention Model Based on Interactions Between Top-down and Bottom-up Information for Robot Pose Prediction
Hyogo Hiruma, Hiroki Mori, Hiroshi Ito, Tetsuya Ogata
End to End Interaction Strategy Robot Pose

February 18, 2022

An end-to-end predict-then-optimize clustering method for intelligent assignment problems in express systems
Jinlei Zhang, Ergang Shan, Lixia Wu, Lixing Yang, Ziyou Gao, Haoyuan Hu
End to End Clustering Method Predict Then Optimize Predictive Clustering Optimal Assignment Express System

February 17, 2022

Mitigating Closed-model Adversarial Examples with Bayesian Neural Modeling for Enhanced End-to-End Speech Recognition
Chao-Han Huck Yang, Zeeshan Ahmed, Yile Gu, Joseph Szurley, Roger Ren, Linda Liu, Andreas Stolcke, Ivan Bulyko
Adversarial Example End to End Adversarial Noise Textual Adversarial Example Bayesian Neural

February 14, 2022

DermX: an end-to-end framework for explainable automated dermatological diagnosis
Raluca Jalaboi, Frederik Faye, Mauricio Orbes-Arteaga, Dan Jørgensen, Ole Winther, Alfiia Galimzianova
End to End Dermatological Disease Diagnosis

February 12, 2022

February 3, 2022

Practical Imitation Learning in the Real World via Task Consistency Loss
Mohi Khansari, Daniel Ho, Yuqing Du, Armando Fuentes, Matthew Bennice, Nicolas Sievers, Sean Kirmani, Yunfei Bai, Eric Jang
End to End Robotics Domain Real World Action Prediction Task Consistency

January 31, 2022

An end-to-end deep learning approach for extracting stochastic dynamical systems with $\alpha$-stable L\'evy noise
Cheng Fang, Yubin Lu, Ting Gao, Jinqiao Duan
End to End Deep Learning Approach Stochastic Dynamical System Interacting System Multiplicative Noise Alpha Stable

January 30, 2022

Improving End-to-End Contextual Speech Recognition with Fine-Grained Contextual Knowledge Selection
Minglun Han, Linhao Dong, Zhenlin Liang, Meng Cai, Shiyu Zhou, Zejun Ma, Bo Xu
Fine Grained End to End Contextual Biasing Contextual Automatic Speech Recognition Phrase Representation

January 28, 2022

January 27, 2022

Excavation Reinforcement Learning Using Geometric Representation
Qingkai Lu, Yifan Zhu, Liangjun Zhang
Reinforcement Learning End to End Geometric Representation Learning Excavation

End to End

Papers

Detecting Offensive Language on Social Networks: An End-to-end Detection Method based on Graph Attention Networks

Deep Latent-Variable Models for Text Generation

Implicit Kinematic Policies: Unifying Joint and Cartesian Action Spaces in End-to-End Robot Learning

Providing Insights for Open-Response Surveys via End-to-End Context-Aware Clustering

An End-to-End Transformer Model for Crowd Localization

Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image Segmentation

An End-to-End Cascaded Image Deraining and Object Detection Neural Network

Guided Visual Attention Model Based on Interactions Between Top-down and Bottom-up Information for Robot Pose Prediction

An end-to-end predict-then-optimize clustering method for intelligent assignment problems in express systems

Mitigating Closed-model Adversarial Examples with Bayesian Neural Modeling for Enhanced End-to-End Speech Recognition

DermX: an end-to-end framework for explainable automated dermatological diagnosis

USTED: Improving ASR with a Unified Speech and Text Encoder-Decoder

End-to-end Reinforcement Learning of Robotic Manipulation with Robust Keypoints Representation

Towards Best Practice of Interpreting Deep Learning Models for EEG-based Brain Computer Interfaces

Practical Imitation Learning in the Real World via Task Consistency Loss

An end-to-end deep learning approach for extracting stochastic dynamical systems with $\alpha$-stable L\'evy noise

Improving End-to-End Contextual Speech Recognition with Fine-Grained Contextual Knowledge Selection

Reducing language context confusion for end-to-end code-switching automatic speech recognition

Neural-FST Class Language Model for End-to-End Speech Recognition

Excavation Reinforcement Learning Using Geometric Representation