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20052026
most citedUnsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

6.9k citations

Showing 2019Show all

143 papers · 1 filter

cs.CL2019★ 461 cited

Libri-Light: A Benchmark for ASR with Limited or No Supervision

Jacob Kahn, Morgane Rivière, Weiyi Zheng +12

We introduce a new collection of spoken English audio suitable for training speech recognition systems under limited or no supervision. It is derived from open-source audio books f…

physics.optics2019★ 53 cited

A topological encoding method for data-driven photonics inverse design

Zhaocheng Liu, Zhaoming Zhu, Wenshan Cai

Data-driven approaches have been proposed as effective strategies for the inverse design and optimization of photonic structures in recent years. In order to assist data-driven met…

cs.SI2019★ 1 cited

Keyword Aware Influential Community Search in Large Attributed Graphs

Md. Saiful Islam, Mohammed Eunus Ali, Yong-Bin Kang +2

We introduce a novel keyword-aware influential community query KICQ that finds the most influential communities from an attributed graph, where an influential community is defined…

cs.CV2019★ 7 cited

SoundSpaces: Audio-Visual Navigation in 3D Environments

Changan Chen, Unnat Jain, Carl Schissler +5

Moving around in the world is naturally a multisensory experience, but today's embodied agents are deaf---restricted to solely their visual perception of the environment. We introd…

stat.ML2019

Statistical Testing on ASR Performance via Blockwise Bootstrap

Zhe Liu, Fuchun Peng

A common question being raised in automatic speech recognition (ASR) evaluations is how reliable is an observed word error rate (WER) improvement comparing two ASR systems, where s…

cs.CV2019★ 1 cited

Deep Poisoning: Towards Robust Image Data Sharing against Visual Disclosure

Hao Guo, Brian Dolhansky, Eric Hsin +3

Due to respectively limited training data, different entities addressing the same vision task based on certain sensitive images may not train a robust deep network. This paper intr…