Search Results for author: Zhuo Wang

Found 12 papers, 5 papers with code

Scalable Rule-Based Representation Learning for Interpretable Classification

2 code implementations NeurIPS 2021 Zhuo Wang, Wei zhang, Ning Liu, Jianyong Wang

Rule-based models, e. g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity.

Representation Learning

Depth Quality-Inspired Feature Manipulation for Efficient RGB-D Salient Object Detection

1 code implementation5 Jul 2021 Wenbo Zhang, Ge-Peng Ji, Zhuo Wang, Keren Fu, Qijun Zhao

To tackle this dilemma and also inspired by the fact that depth quality is a key factor influencing the accuracy, we propose a novel depth quality-inspired feature manipulation (DQFM) process, which is efficient itself and can serve as a gating mechanism for filtering depth features to greatly boost the accuracy.

RGB-D Salient Object Detection Salient Object Detection

RRL: A Scalable Classifier for Interpretable Rule-Based Representation Learning

no code implementations1 Jan 2021 Zhuo Wang, Wei zhang, Ning Liu, Jianyong Wang

Rule-based models, e. g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity.

Representation Learning

Learning to drive via Apprenticeship Learning and Deep Reinforcement Learning

no code implementations12 Jan 2020 Wenhui Huang, Francesco Braghin, Zhuo Wang

Therefore, we propose an apprenticeship learning in combination with deep reinforcement learning approach that allows the agent to learn the driving and stopping behaviors with continuous actions.

Robotics

Transparent Classification with Multilayer Logical Perceptrons and Random Binarization

1 code implementation10 Dec 2019 Zhuo Wang, Wei zhang, Ning Liu, Jianyong Wang

In this paper, we propose a new hierarchical rule-based model for classification tasks, named Concept Rule Sets (CRS), which has both a strong expressive ability and a transparent inner structure.

Binarization General Classification

ColluEagle: Collusive review spammer detection using Markov random fields

1 code implementation5 Nov 2019 Zhuo Wang, Runlong Hu, Qian Chen, Pei Gao, Xiaowei Xu

Previous works use review network effects, i. e. the relationships among reviewers, reviews, and products, to detect fake reviews or review spammers, but ignore time effects, which are critical in characterizing group spamming.

PACT: Parameterized Clipping Activation for Quantized Neural Networks

2 code implementations ICLR 2018 Jungwook Choi, Zhuo Wang, Swagath Venkataramani, Pierce I-Jen Chuang, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan

We show, for the first time, that both weights and activations can be quantized to 4-bits of precision while still achieving accuracy comparable to full precision networks across a range of popular models and datasets.

Quantization

Efficient Neural Codes under Metabolic Constraints

no code implementations NeurIPS 2016 Zhuo Wang, Xue-Xin Wei, Alan A. Stocker, Daniel D. Lee

The advantage could be as large as one-fold, substantially larger than the previous estimation.

Optimization and analysis of large scale data sorting algorithm based on Hadoop

no code implementations1 Jun 2015 Zhuo Wang, Longlong Tian, Dianjie Guo, Xiaoming Jiang

If the data is also too big, it will turn back to the first round and keep on.

Distributed, Parallel, and Cluster Computing

Optimal Neural Population Codes for High-dimensional Stimulus Variables

no code implementations NeurIPS 2013 Zhuo Wang, Alan A. Stocker, Daniel D. Lee

We consider solutions for a minimal case where the number of neurons in the population is equal to the number of stimulus dimensions (diffeomorphic).

Optimal Neural Tuning Curves for Arbitrary Stimulus Distributions: Discrimax, Infomax and Minimum L_p Loss

no code implementations NeurIPS 2012 Zhuo Wang, Alan A. Stocker, Daniel D. Lee

In this manner, we show how the optimal tuning curve depends upon the loss function, and the equivalence of maximizing mutual information with minimizing $L_p$ loss in the limit as $p$ goes to zero.

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