Hashing for Distributed Data

By Cong Leng, Jiaxiang Wu, Jian Cheng, Xi Zhang, Hanqing Lu. 2015

31 citations recorded by the source corpus.

Subfields: ML Systems & Hardware, Algorithms & Complexity

How researchers see this paper

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

In ML Systems & Hardware

1. Jian Cheng
2. Ruslan Salakhutdinov
3. Percy Liang
4. Wu-Jun Li
5. Louis-philippe Morency

In Algorithms & Complexity

1. Jian Cheng
2. Jiaxiang Wu
3. Cong Leng
4. Liu Ziyin
5. Ruslan Salakhutdinov
6. Percy Liang
7. Terrance Liu
8. Louis-philippe Morency

Citations in the corpus

• Quantized Convolutional Neural Networks for Mobile Devices (2015, methodology): “Product quantization [14] is widely used in approximate nearest neighbor search, demonstrating better performance than hashing-based methods [21, 22].”
• Think Locally, Act Globally: Federated Learning with Local and Global Representations (2020, background): “Recent work has improved the communication efficiency of distributed learning by sparsifying the data and model [76], developing efficient gradient-based methods [77, 21], and compressing the updates [75, 83, 49, 52, 46].”
• Deep Cross-Modal Hashing (2016, background): “Due to its low storage cost and fast retrieval speed, hashing has recently attracted much attention from the ANN research community (Weiss et al., 2008; Raginsky & Lazebnik, 2009; Wang et al., 2010; Liu et al., 2011; Norouzi & Fleet, 2011; Norouzi et al., 2012; Rastegari et al., 2013; Yu et al., 201”