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Found 24 papers, 9 papers with code
Probabilistic Estimation of Chirp Instantaneous Frequency Using Gaussian Processes
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.
A Unified Model for Reverse Dictionary and Definition Modelling
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
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.
State-space deep Gaussian processes with applications
Lastly, this thesis features a number of applications of state-space (deep) GPs.
Silicon photonic subspace neural chip for hardware-efficient deep learning
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
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
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.
Using the structural kinome to systematize kinase drug discovery
Thus, obtaining the desired selectivity, given the whole human kinome, is a fundamental task during early-stage drug discovery.
Temporal Gaussian Process Regression in Logarithmic Time
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
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
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
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.
Logic Synthesis Meets Machine Learning: Trading Exactness for Generalization
If the function is incompletely-specified, the implementation has to be true only on the care set.
Extending Implicit Discourse Relation Recognition to the PDTB-3
The PDTB-3 contains many more Implicit discourse relations than the previous PDTB-2.
Reducing Quantity Hallucinations in Abstractive Summarization
It is well-known that abstractive summaries are subject to hallucination---including material that is not supported by the original text.
Review of Machine-Learning Methods for RNA Secondary Structure Prediction
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
This paper is concerned with a state-space approach to deep Gaussian process (DGP) regression.
Taylor Moment Expansion for Continuous-Discrete Gaussian Filtering and Smoothing
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.
Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network
Results: The proposed algorithm is validated on the 2018 PhysioNet challenge dataset.
Kalman-based Spectro-Temporal ECG Analysis using Deep Convolutional Networks for Atrial Fibrillation Detection
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.
Successive Ray Refinement and Its Application to Coordinate Descent for LASSO
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
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.
Safe Screening With Variational Inequalities and Its Application to LASSO
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.
Discriminative K-means for Clustering
The connection between DisKmeans and several other clustering algorithms is also analyzed.
