Search Results for author:
Found 51 papers, 25 papers with code
Graph Neural Networks Automated Design and Deployment on Device-Edge Co-Inference Systems
GCoDE abstracts the device communication process into an explicit operation and fuses the search of architecture and the operations mapping in a unified space for joint-optimization.
WAL-Net: Weakly supervised auxiliary task learning network for carotid plaques classification
Specifically, the accuracy of mixed-echoic plaques classification increased by approximately 3. 3%, demonstrating the effectiveness of our approach.
FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models
Our work offers a more comprehensive financial knowledge evaluation benchmark, utilizing data of mock exams and covering a wide range of evaluated LLMs.
Fusing Structural and Functional Connectivities using Disentangled VAE for Detecting MCI
In this paper, a novel hierarchical structural-functional connectivity fusing (HSCF) model is proposed to construct brain structural-functional connectivity matrices and predict abnormal brain connections based on functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI).
Self-training with dual uncertainty for semi-supervised medical image segmentation
In the field of semi-supervised medical image segmentation, the shortage of labeled data is the fundamental problem.
HD-GCN:A Hybrid Diffusion Graph Convolutional Network
HD-GCN utilizes hybrid diffusion by combining information diffusion between neighborhood nodes in the feature space and adjacent nodes in the adjacency matrix.
SAViT: Structure-Aware Vision Transformer Pruning via Collaborative Optimization
In this paper, we introduce joint importance, which integrates essential structural-aware interactions between components for the first time, to perform collaborative pruning.
Unified Normalization for Accelerating and Stabilizing Transformers
To tackle these issues, we propose Unified Normalization (UN), which can speed up the inference by being fused with other linear operations and achieve comparable performance on par with LN.
A Safe Semi-supervised Graph Convolution Network
Therefore, we propose a Safe GCN framework (Safe-GCN) to improve the learning performance.
NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning
First, GNNs can learn higher-order structural information by stacking more layers but can not deal with large depth due to the over-smoothing issue.
DFG-NAS: Deep and Flexible Graph Neural Architecture Search
Graph neural networks (GNNs) have been intensively applied to various graph-based applications.
Model Degradation Hinders Deep Graph Neural Networks
Graph Neural Networks (GNNs) have achieved great success in various graph mining tasks. However, drastic performance degradation is always observed when a GNN is stacked with many layers.
Graph Attention Multi-Layer Perceptron
Graph neural networks (GNNs) have achieved great success in many graph-based applications.
Ranked #11 on
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TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning
With the extensive applications of machine learning models, automatic hyperparameter optimization (HPO) has become increasingly important.
Instance-wise Prompt Tuning for Pretrained Language Models
Prompt Learning has recently gained great popularity in bridging the gap between pretraining tasks and various downstream tasks.
AMCAD: Adaptive Mixed-Curvature Representation based Advertisement Retrieval System
Moreover, to deploy AMCAD in Taobao, one of the largest ecommerce platforms with hundreds of million users, we design an efficient two-layer online retrieval framework for the task of graph based advertisement retrieval.
ZOOMER: Boosting Retrieval on Web-scale Graphs by Regions of Interest
We introduce ZOOMER, a system deployed at Taobao, the largest e-commerce platform in China, for training and serving GNN-based recommendations over web-scale graphs.
Artificial Intelligence Enables Real-Time and Intuitive Control of Prostheses via Nerve Interface
Objective: The next generation prosthetic hand that moves and feels like a real hand requires a robust neural interconnection between the human minds and machines.
Information Gain Propagation: a new way to Graph Active Learning with Soft Labels
Graph Neural Networks (GNNs) have achieved great success in various tasks, but their performance highly relies on a large number of labeled nodes, which typically requires considerable human effort.
PaSca: a Graph Neural Architecture Search System under the Scalable Paradigm
Through deconstructing the message passing mechanism, PasCa presents a novel Scalable Graph Neural Architecture Paradigm (SGAP), together with a general architecture design space consisting of 150k different designs.
Content-Noise Complementary Learning for Medical Image Denoising
In this study, we propose a simple yet effective strategy, the content-noise complementary learning (CNCL) strategy, in which two deep learning predictors are used to learn the respective content and noise of the image dataset complementarily.
EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gate
Then it diversifies the experts and continues to train the MoE with a novel Dense-to-Sparse gate (DTS-Gate).
HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework
Embedding models have been an effective learning paradigm for high-dimensional data.
RIM: Reliable Influence-based Active Learning on Graphs
Message passing is the core of most graph models such as Graph Convolutional Network (GCN) and Label Propagation (LP), which usually require a large number of clean labeled data to smooth out the neighborhood over the graph.
Node Dependent Local Smoothing for Scalable Graph Learning
Recent works reveal that feature or label smoothing lies at the core of Graph Neural Networks (GNNs).
Graph Attention MLP with Reliable Label Utilization
Graph neural networks (GNNs) have recently achieved state-of-the-art performance in many graph-based applications.
Cascade Bagging for Accuracy Prediction with Few Training Samples
To alleviate this problem, we propose a novel framework to train an accuracy predictor under few training samples.
Improving Ranking Correlation of Supernet with Candidates Enhancement and Progressive Training
One-shot neural architecture search (NAS) applies weight-sharing supernet to reduce the unaffordable computation overhead of automated architecture designing.
Evaluating Deep Graph Neural Networks
Based on the experimental results, we answer the following two essential questions: (1) what actually leads to the compromised performance of deep GNNs; (2) when we need and how to build deep GNNs.
Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization
Data selection methods, such as active learning and core-set selection, are useful tools for improving the data efficiency of deep learning models on large-scale datasets.
ROD: Reception-aware Online Distillation for Sparse Graphs
Unfortunately, many real-world networks are sparse in terms of both edges and labels, leading to sub-optimal performance of GNNs.
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition
End-to-end AutoML has attracted intensive interests from both academia and industry, which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and hyper-parameter tuning.
OpenBox: A Generalized Black-box Optimization Service
Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, engineering, physics, and experimental design.
GMLP: Building Scalable and Flexible Graph Neural Networks with Feature-Message Passing
In recent studies, neural message passing has proved to be an effective way to design graph neural networks (GNNs), which have achieved state-of-the-art performance in many graph-based tasks.
A Portable, Self-Contained Neuroprosthetic Hand with Deep Learning-Based Finger Control
This enables the implementation of the neuroprosthetic hand as a portable and self-contained unit with real-time control of individual finger movements.
Measurement of the absolute branching fractions for purely leptonic $D_s^+$ decays
Constraining our measurement to the Standard Model expectation of lepton universality ($R=9. 75$), we find the more precise results $\cal B(D_s^+\to \tau^+\nu_\tau) = (5. 22\pm0. 10\pm 0. 14)\times10^{-2}$ and $A_{\it CP}(\tau^\pm\nu_\tau) = (-0. 1\pm1. 9\pm1. 0)\%$.
High Energy Physics - Experiment
Cross section measurement of $e^+e^- \to p\bar{p}η$ and $e^+e^- \to p\bar{p}ω$ at center-of-mass energies between 3.773 GeV and 4.6 GeV
Based on $14. 7~\textrm{fb}^{-1}$ of $e^+e^-$ annihilation data collected with the BESIII detector at the BEPCII collider at 17 different center-of-mass energies between $3. 7730~\textrm{GeV}$ and $4. 5995~\textrm{GeV}$, Born cross sections of the two processes $e^+e^- \to p\bar{p}\eta$ and $e^+e^- \to p\bar{p}\omega$ are measured for the first time.
High Energy Physics - Experiment
Measurements of the center-of-mass energies of $e^{+}e^{-}$ collisions at BESIII
During the 2016-17 and 2018-19 running periods, the BESIII experiment collected 7. 5~fb$^{-1}$ of $e^+e^-$ collision data at center-of-mass energies ranging from 4. 13 to 4. 44 GeV.
High Energy Physics - Experiment
Search for the reaction $e^{+}e^{-} \rightarrow π^{+}π^{-} χ_{cJ}$ and a charmonium-like structure decaying to $χ_{cJ}π^{\pm}$ between 4.18 and 4.60 GeV
We search for the process $e^{+}e^{-}\rightarrow \pi ^{+}\pi ^{-} \chi_{cJ}$ ($J=0, 1, 2$) and for a charged charmonium-like state in the $\pi ^{\pm} \chi_{cJ}$ subsystem.
High Energy Physics - Experiment
Fine-Grained Image Captioning with Global-Local Discriminative Objective
This is primarily due to (i) the conservative characteristic of traditional training objectives that drives the model to generate correct but hardly discriminative captions for similar images and (ii) the uneven word distribution of the ground-truth captions, which encourages generating highly frequent words/phrases while suppressing the less frequent but more concrete ones.
Model independent determination of the spin of the $Ω^{-}$ and its polarization alignment in $ψ(3686)\rightarrowΩ^{-}\barΩ^{+}$
We present an analysis of the process $\psi(3686) \to \Omega^- \bar{\Omega}^+$ ($\Omega^-\to K^-\Lambda$, $\bar{\Omega}^+\to K^+\bar{\Lambda}$, $\Lambda\to p\pi^-$, $\bar{\Lambda}\to \bar{p}\pi^+$) based on a data set of $448\times 10^6$ $\psi(3686)$ decays collected with the BESIII detector at the BEPCII electron-positron collider.
High Energy Physics - Experiment
MSD: Multi-Self-Distillation Learning via Multi-classifiers within Deep Neural Networks
In this paper, we propose a general training framework named multi-self-distillation learning (MSD), which mining knowledge of different classifiers within the same network and increase every classifier accuracy.
Representation Learning of EHR Data via Graph-Based Medical Entity Embedding
Automatic representation learning of key entities in electronic health record (EHR) data is a critical step for healthcare informatics that turns heterogeneous medical records into structured and actionable information.
Push and Pull Search Embedded in an M2M Framework for Solving Constrained Multi-objective Optimization Problems
In dealing with constrained multi-objective optimization problems (CMOPs), a key issue of multi-objective evolutionary algorithms (MOEAs) is to balance the convergence and diversity of working populations.
Embedding Push and Pull Search in the Framework of Differential Evolution for Solving Constrained Single-objective Optimization Problems
In the top sub-population, the search process is divided into two different stages --- push and pull stages. An adaptive DE variant with three trial vector generation strategies is employed in the proposed PPS-DE.
Towards Efficient Large-Scale Graph Neural Network Computing
This evolution has led to large graph-based irregular and sparse models that go beyond what existing deep learning frameworks are designed for.
Deep Compressive Autoencoder for Action Potential Compression in Large-Scale Neural Recording
The proposed model is built upon a deep compressive autoencoder (CAE) with discrete latent embeddings.
A Bio-inspired Redundant Sensing Architecture
However, this upper bound is hard to achieve in many man-made systems.
Advancing System Performance with Redundancy: From Biological to Artificial Designs
We envision that our theory would provide a framework for the future development of bio-inspired redundant artificial systems as well as assist the studies of the fundamental mechanisms governing various biological processes.
Unsupervised Spike Sorting Based on Discriminative Subspace Learning
The algorithms are tested on synthetic and in-vivo data, and are compared against two widely used spike sorting methods.
Noise Characterization, Modeling, and Reduction for In Vivo Neural Recording
Multiple noise sources have been studied through analytical models as well as empirical measurements.
