End To End

By Rick Mullin. 2015

291 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Learning Robust and Multilingual Speech Representations (2020, neutral): “Data augmentation (Park et al., 2019), self-training (Synnaeve et al., 2019; Kahn et al., 2019a) have advanced the state-of-the-art performance on English speech recognition.”
Speech Enhancement in Multiple-Noise Conditions Using Deep Neural Networks (2016, background): “In recent years deep neural network (DNN) based learning architectures have been found to be very successful in related areas such as speech recognition [9][10][11][12].”
Rethinking the Faster R-CNN Architecture for Temporal Action Localization (2018, background): “In addition to the works reviewed above, there exist other classes of approaches, such as those based on single-shot detectors [2, 27] or reinforcement learning [52].”
FINDINGS OF THE IWSLT 2020 EVALUATION CAMPAIGN (2020, methodology): “All the systems were developed using the ESPnet end-to-end speech processing toolkit (Watanabe et al., 2018).”
Scaling Rectified Flow Transformers for High-Resolution Image Synthesis (2024, methodology): “We analyze the convergence behavior of these architectures in Figure 4: Vanilla DiT underperforms UViT.”
Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning (2021, unknown): “The memory-augmented HMM, depicted in Figure 2, can be viewed as a generative variant of memory networks [41, 35] and is closely related to Hidden Topic Markov Models [8].”