Search Results for author:
Found 29 papers, 3 papers with code
Nyonic Technical Report
This report details the development and key achievements of our latest language model designed for custom large language models.
scInterpreter: Training Large Language Models to Interpret scRNA-seq Data for Cell Type Annotation
Despite the inherent limitations of existing Large Language Models in directly reading and interpreting single-cell omics data, they demonstrate significant potential and flexibility as the Foundation Model.
TRLS: A Time Series Representation Learning Framework via Spectrogram for Medical Signal Processing
Representation learning frameworks in unlabeled time series have been proposed for medical signal processing.
Noise-induced stochastic Nash equilibrium
In order to better understand the impact of environmental stochastic fluctuations on the evolution of animal behavior, we introduce the concept of a stochastic Nash equilibrium (SNE) that extends the classical concept of a Nash equilibrium (NE).
Intelligent machines work in unstructured environments by differential neuromorphic computing
By mimicking the intrinsic nature of human low-level perception mechanisms, the electronic memristive neuromorphic circuit-based method, presented here shows the potential for adapting to diverse sensing technologies and helping intelligent machines generate smart high-level decisions in the real world.
SHISRCNet: Super-resolution And Classification Network For Low-resolution Breast Cancer Histopathology Image
CF module extracts and fuses the multi-scale features of SR images for classification.
Histopathological Image Classification
Image Classification
+1
Enhancing Depth Completion with Multi-View Monitored Distillation
Our approach builds upon the state-of-the-art ensemble distillation method, in which we introduce a stereo-based model as a teacher model to improve the accuracy of the student model for depth completion.
NCL: Textual Backdoor Defense Using Noise-augmented Contrastive Learning
At present, backdoor attacks attract attention as they do great harm to deep learning models.
Global RTK Positioning in Graphical State Space
This paper proposes a new method for RTK post-processing.
Evolutionary rationality of risk preference
We highlight the importance of selection intensity and fitness, as well as their equivalents in the human mind, named as attention degree and meta-fitness, in the decision making process.
GNNear: Accelerating Full-Batch Training of Graph Neural Networks with Near-Memory Processing
However, to realize efficient GNN training is challenging, especially on large graphs.
Data Informed Residual Reinforcement Learning for High-Dimensional Robotic Tracking Control
The learning inefficiency of reinforcement learning (RL) from scratch hinders its practical application towards continuous robotic tracking control, especially for high-dimensional robots.
Instantaneous Local Control Barrier Function: An Online Learning Approach for Collision Avoidance
This paper presents an integrated perception and control approach to accomplish safe autonomous navigation in unknown environments.
Model-Free Incremental Adaptive Dynamic Programming Based Approximate Robust Optimal Regulation
This paper presents a new formulation for model-free robust optimal regulation of continuous-time nonlinear systems.
Deep Attributed Network Representation Learning via Attribute Enhanced Neighborhood
In this paper, we propose a deep attributed network representation learning via attribute enhanced neighborhood (DANRL-ANE) model to improve the robustness and effectiveness of node representations.
Origin of the Electronic Structure in Single-Layer FeSe/SrTiO3 Films
These observations not only show the first direct evidence that the electronic structure of single-layer FeSe/SrTiO3 films originates from bulk FeSe through a combined effect of an electronic phase transition and an interfacial charge transfer, but also provide a quantitative basis for theoretical models in describing the electronic structure and understanding the superconducting mechanism in single-layer FeSe/SrTiO3 films.
Band Gap
Superconductivity
Strongly Correlated Electrons
TSAM: Temporal Link Prediction in Directed Networks based on Self-Attention Mechanism
In this paper, we address the problem of temporal link prediction in directed networks and propose a deep learning model based on GCN and self-attention mechanism, namely TSAM.
Off-Policy Risk-Sensitive Reinforcement Learning Based Constrained Robust Optimal Control
By introducing pseudo controls and risk-sensitive input and state penalty terms, the constrained robust stabilization problem of the original system is converted into an equivalent optimal control problem of an auxiliary system.
Dense Fusion Classmate Network for Land Cover Classification
Recently, FCNs based methods have made great progress in semantic segmentation.
Neural Input Search for Large Scale Recommendation Models
During training we use reinforcement learning to find the optimal vocabulary size for each feature and embedding dimension for each value of the feature.
Electronic structure and $H$-$T$ phase diagram of Eu(Fe$_{1-x}$Rh$_x$)$_2$As$_2$
The iron-based superconductors represent a promising platform for high-temperature superconductivity, but the interactions underpinning their pairing present a puzzle.
Superconductivity Strongly Correlated Electrons
Reduced-Rank Local Distance Metric Learning for k-NN Classification
We propose a new method for local distance metric learning based on sample similarity as side information.
R$^2$-CNN: Fast Tiny Object Detection in Large-Scale Remote Sensing Images
To tackle these problems, we propose a unified and self-reinforced network called remote sensing region-based convolutional neural network ($\mathcal{R}^2$-CNN), composing of backbone Tiny-Net, intermediate global attention block, and final classifier and detector.
Multi-Task Learning Using Neighborhood Kernels
As shown by our empirical results, our algorithm consistently outperforms the traditional kernel learning algorithms such as uniform combination solution, convex combinations of base kernels as well as some kernel alignment-based models, which have been proven to give promising results in the past.
Conic Multi-Task Classification
Traditionally, Multi-task Learning (MTL) models optimize the average of task-related objective functions, which is an intuitive approach and which we will be referring to as Average MTL.
Pareto-Path Multi-Task Multiple Kernel Learning
A traditional and intuitively appealing Multi-Task Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) shared kernel function, which allows information sharing amongst tasks.
A Unifying Framework for Typical Multi-Task Multiple Kernel Learning Problems
Over the past few years, Multi-Kernel Learning (MKL) has received significant attention among data-driven feature selection techniques in the context of kernel-based learning.
Multi-Task Classification Hypothesis Space with Improved Generalization Bounds
This paper presents a RKHS, in general, of vector-valued functions intended to be used as hypothesis space for multi-task classification.
Kernel-based Distance Metric Learning in the Output Space
In this paper we present two related, kernel-based Distance Metric Learning (DML) methods.
