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1. SLA Decomposition for Sustainable End-to-End Multi-Domain Multi-Technology Network Slicing

   https://doi.org/10.1109/MWC.008.2300157  

   Esmat, Haitham H; Lorenzo, Beatriz (August 2024, IEEE Wireless Communications)
2. End-to-end Knowledge Retrieval with Multi-modal Queries

   Luo, Man; Fang, Zhiyuan; Gokhale, Tejas; Yang, Yezhou; Baral, Chitta (July 2023, 61st Annual Meeting of the Association for Computational Linguistics)

   We investigate knowledge retrieval with multi-modal queries, i.e. queries containing information split across image and text inputs, a challenging task that differs from previous work on cross-modal retrieval. We curate a new dataset called ReMuQ for benchmarking progress on this task. ReMuQ requires a system to retrieve knowledge from a large corpus by integrating contents from both text and image queries. We introduce a retriever model “ReViz” that can directly process input text and images to retrieve relevant knowledge in an end-to-end fashion without being dependent on intermediate modules such as object detectors or caption generators. We introduce a new pretraining task that is effective for learning knowledge retrieval with multimodal queries and also improves performance on downstream tasks. We demonstrate superior performance in retrieval on two datasets (ReMuQ and OK-VQA) under zero-shot settings as well as further improvements when finetuned on these datasets. 

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3. A Multi-Level Optimization Framework for End-to-End Text Augmentation

   https://doi.org/10.1162/tacl_a_00464  

   Somayajula, Sai Ashish; Song, Linfeng; Xie, Pengtao (January 2022, Transactions of the Association for Computational Linguistics)

   Abstract Text augmentation is an effective technique in alleviating overfitting in NLP tasks. In existing methods, text augmentation and downstream tasks are mostly performed separately. As a result, the augmented texts may not be optimal to train the downstream model. To address this problem, we propose a three-level optimization framework to perform text augmentation and the downstream task end-to- end. The augmentation model is trained in a way tailored to the downstream task. Our framework consists of three learning stages. A text summarization model is trained to perform data augmentation at the first stage. Each summarization example is associated with a weight to account for its domain difference with the text classification data. At the second stage, we use the model trained at the first stage to perform text augmentation and train a text classification model on the augmented texts. At the third stage, we evaluate the text classification model trained at the second stage and update weights of summarization examples by minimizing the validation loss. These three stages are performed end-to-end. We evaluate our method on several text classification datasets where the results demonstrate the effectiveness of our method. Code is available at https://github.com/Sai-Ashish/End-to-End-Text-Augmentation. 

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4. TesseTrack: End-to-End Learnable Multi-Person Articulated 3D Pose Tracking

   Reddy, N Dinesh; Guigues, Laurent; Pischulini, Leonid; Eledath, Jayan; Narasimhan, Srinivasa (June 2021, International Conference on Computer Vision and Pattern Recognition (CVPR))

   We consider the task of 3D pose estimation and tracking of multiple people seen in an arbitrary number of camera feeds. We propose TesseTrack, a novel top-down approach that simultaneously reasons about multiple individuals’ 3D body joint reconstructions and associations in space and time in a single end-to-end learnable framework. At the core of our approach is a novel spatio-temporal formulation that operates in a common voxelized feature space aggregated from single- or multiple camera views. After a person detection step, a 4D CNN produces short-term persons pecific representations which are then linked across time by a differentiable matcher. The linked descriptions are then merged and deconvolved into 3D poses. This joint spatio-temporal formulation contrasts with previous piecewise strategies that treat 2D pose estimation, 2D-to-3D lifting, and 3D pose tracking as independent sub-problems that are error-prone when solved in isolation. Furthermore, unlike previous methods, TesseTrack is robust to changes in the number of camera views and achieves very good results even if a single view is available at inference time. Quantitative evaluation of 3D pose reconstruction accuracy on standard benchmarks shows significant improvements over the state of the art. Evaluation of multi-person articulated 3D pose tracking in our novel evaluation framework demonstrates the superiority of TesseTrack over strong baselines. 

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5. End-to-End Throughput in Multi-Hop Wireless Networks With Random Relay Deployment

   https://doi.org/10.1109/TSIPN.2018.2806846  

   Liang, Yuan; Li, Tongtong (January 2018, IEEE Transactions on Signal and Information Processing over Networks)