Search Results for author: Zheng Zhao

Found 24 papers, 9 papers with code

Probabilistic Estimation of Chirp Instantaneous Frequency Using Gaussian Processes

1 code implementation12 May 2022 Zheng Zhao, Simo Särkkä, Jens Sjölund, Thomas B. Schön

We present a probabilistic approach for estimating chirp signal and its instantaneous frequency function when the true forms of the chirp and instantaneous frequency are unknown.

Gaussian Processes

A Unified Model for Reverse Dictionary and Definition Modelling

no code implementations9 May 2022 Pinzhen Chen, Zheng Zhao

We train a dual-way neural dictionary to guess words from definitions (reverse dictionary), and produce definitions given words (definition modelling).

Revisiting Shallow Discourse Parsing in the PDTB-3: Handling Intra-sentential Implicits

no code implementations CODI 2021 Zheng Zhao, Bonnie Webber

In the PDTB-3, several thousand implicit discourse relations were newly annotated \textit{within} individual sentences, adding to the over 15, 000 implicit relations annotated \textit{across} adjacent sentences in the PDTB-2.

Discourse Parsing

State-space deep Gaussian processes with applications

1 code implementation24 Nov 2021 Zheng Zhao

Lastly, this thesis features a number of applications of state-space (deep) GPs.

Gaussian Processes

Silicon photonic subspace neural chip for hardware-efficient deep learning

1 code implementation11 Nov 2021 Chenghao Feng, Jiaqi Gu, Hanqing Zhu, Zhoufeng Ying, Zheng Zhao, David Z. Pan, Ray T. Chen

The optical neural network (ONN) is a promising candidate for next-generation neurocomputing due to its high parallelism, low latency, and low energy consumption.

Advance in Reversible Covalent Kinase Inhibitors

no code implementations22 Jun 2021 Zheng Zhao, Philip E. Bourne

Finally, we provide a brief perspective on future design strategies for RCKIs, including those that target proteins other than kinases.

Hierarchical Non-Stationary Temporal Gaussian Processes With $L^1$-Regularization

no code implementations20 May 2021 Zheng Zhao, Rui Gao, Simo Särkkä

This paper is concerned with regularized extensions of hierarchical non-stationary temporal Gaussian processes (NSGPs) in which the parameters (e. g., length-scale) are modeled as GPs.

Gaussian Processes

Using the structural kinome to systematize kinase drug discovery

no code implementations27 Apr 2021 Zheng Zhao, Philip E. Bourne

Thus, obtaining the desired selectivity, given the whole human kinome, is a fundamental task during early-stage drug discovery.

Drug Discovery

Temporal Gaussian Process Regression in Logarithmic Time

1 code implementation19 Feb 2021 Adrien Corenflos, Zheng Zhao, Simo Särkkä

The aim of this article is to present a novel parallelization method for temporal Gaussian process (GP) regression problems.

SqueezeLight: Towards Scalable Optical Neural Networks with Multi-Operand Ring Resonators

1 code implementation IEEE Design, Automation & Test in Europe Conference & Exhibition (DATE) 2021 Jiaqi Gu, Chenghao Feng, Zheng Zhao, Zhoufeng Ying, Mingjie Liu, Ray T. Chen, David Z. Pan

Optical neural networks (ONNs) have demonstrated promising potentials for next-generation artificial intelligence acceleration with ultra-low latency, high bandwidth, and low energy consumption.

Study of charmonium-like and fully-charm tetraquark spectroscopy

no code implementations31 Dec 2020 Zheng Zhao, Kai Xu, Attaphon Kaewsnod, Xuyang Liu, Ayut Limphirat, Yupeng Yan

The masses of tetraquark states of all $qc\bar q \bar c$ and $cc\bar c \bar c$ quark configurations are evaluated in a constituent quark model, where the Cornell-like potential and one-gluon exchange spin-spin coupling are employed.

High Energy Physics - Phenomenology

Efficient On-Chip Learning for Optical Neural Networks Through Power-Aware Sparse Zeroth-Order Optimization

1 code implementation21 Dec 2020 Jiaqi Gu, Chenghao Feng, Zheng Zhao, Zhoufeng Ying, Ray T. Chen, David Z. Pan

Optical neural networks (ONNs) have demonstrated record-breaking potential in high-performance neuromorphic computing due to their ultra-high execution speed and low energy consumption.

Review of Machine-Learning Methods for RNA Secondary Structure Prediction

no code implementations1 Sep 2020 Qi Zhao, Zheng Zhao, Xiaoya Fan, Zhengwei Yuan, Qian Mao, YuDong Yao

Recently, with the increasing availability of RNA structure data, new methods based on machine-learning technologies, especially deep learning, have alleviated the issue.

Deep State-Space Gaussian Processes

1 code implementation11 Aug 2020 Zheng Zhao, Muhammad Emzir, Simo Särkkä

This paper is concerned with a state-space approach to deep Gaussian process (DGP) regression.

Gaussian Processes

Taylor Moment Expansion for Continuous-Discrete Gaussian Filtering and Smoothing

2 code implementations8 Jan 2020 Zheng Zhao, Toni Karvonen, Roland Hostettler, Simo Särkkä

The paper is concerned with non-linear Gaussian filtering and smoothing in continuous-discrete state-space models, where the dynamic model is formulated as an It\^{o} stochastic differential equation (SDE), and the measurements are obtained at discrete time instants.

Kalman-based Spectro-Temporal ECG Analysis using Deep Convolutional Networks for Atrial Fibrillation Detection

no code implementations12 Dec 2018 Zheng Zhao, Simo Särkkä, Ali Bahrami Rad

In this article, we propose a novel ECG classification framework for atrial fibrillation (AF) detection using spectro-temporal representation (i. e., time varying spectrum) and deep convolutional networks.

Atrial Fibrillation Detection ECG Classification

Successive Ray Refinement and Its Application to Coordinate Descent for LASSO

no code implementations17 Dec 2015 Jun Liu, Zheng Zhao, Ruiwen Zhang

Coordinate descent is one of the most popular approaches for solving Lasso and its extensions due to its simplicity and efficiency.

Safe and Efficient Screening For Sparse Support Vector Machine

no code implementations30 Oct 2013 Zheng Zhao, Jun Liu

Screening is an effective technique for speeding up the training process of a sparse learning model by removing the features that are guaranteed to be inactive the process.

Sparse Learning

Safe Screening With Variational Inequalities and Its Application to LASSO

no code implementations29 Jul 2013 Jun Liu, Zheng Zhao, Jie Wang, Jieping Ye

Safe screening is gaining increasing attention since 1) solving sparse learning formulations usually has a high computational cost especially when the number of features is large and 2) one needs to try several regularization parameters to select a suitable model.

Sparse Learning

Discriminative K-means for Clustering

no code implementations NeurIPS 2007 Jieping Ye, Zheng Zhao, Mingrui Wu

The connection between DisKmeans and several other clustering algorithms is also analyzed.

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