activity
20172025
most citedEfficientDet: Scalable and Efficient Object Detection

532 citations · 2.6k across the 41 of their papers we have counts for

collaborators
Showing 2019Show all

7 papers · 1 filter

eess.AS2019★ 13 cited

A comparison of end-to-end models for long-form speech recognition

Chung-Cheng Chiu, Wei Han, Yu Zhang +11

End-to-end automatic speech recognition (ASR) models, including both attention-based models and the recurrent neural network transducer (RNN-T), have shown superior performance com…

cs.CV2019★ 532 cited

EfficientDet: Scalable and Efficient Object Detection

Mingxing Tan, Ruoming Pang, Quoc V. Le

Model efficiency has become increasingly important in computer vision. In this paper, we systematically study neural network architecture design choices for object detection and pr…

cs.CL2019

Two-Pass End-to-End Speech Recognition

Tara N. Sainath, Ruoming Pang, David Rybach +9

The requirements for many applications of state-of-the-art speech recognition systems include not only low word error rate (WER) but also low latency. Specifically, for many use-ca…

cs.CL2019

Monotonic Infinite Lookback Attention for Simultaneous Machine Translation

Naveen Arivazhagan, Colin Cherry, Wolfgang Macherey +5

Simultaneous machine translation begins to translate each source sentence before the source speaker is finished speaking, with applications to live and streaming scenarios. Simulta…

cs.CV2019

Searching for MobileNetV3

Andrew Howard, Mark Sandler, Grace Chu +9

We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone…

cs.CV2019★ 11 cited

NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection

Golnaz Ghiasi, Tsung-Yi Lin, Ruoming Pang +1

Current state-of-the-art convolutional architectures for object detection are manually designed. Here we aim to learn a better architecture of feature pyramid network for object de…

cs.LG2019★ 184 cited

Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

Jonathan Shen, Patrick Nguyen, Yonghui Wu +88

Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models a…