Search Results for author:
Found 32 papers, 15 papers with code
面向微博文本的融合字词信息的轻量级命名实体识别(Lightweight Named Entity Recognition for Weibo Based on Word and Character)
中文社交媒体命名实体识别由于其领域特殊性, 一直广受关注。非正式且无结构的微博文本存在以下两个问题:一是词语边界模糊;二是语料规模有限。针对问题一, 本文将同维度的字词进行融合, 获得丰富的文本序列表征;针对问题二, 提出了基于Star-Transformer框架的命名实体识别模型, 借助星型拓扑结构更好地捕获动态特征;同时利用高速网络优化Star-Transformer中的信息桥接, 提升模型的鲁棒性。本文提出的轻量级命名实体识别模型取得了目前Weibo语料上最好的效果。
Distributionally Robust Graph-based Recommendation System
DR-GNN addresses two core challenges: 1) To enable DRO to cater to graph data intertwined with GNN, we reinterpret GNN as a graph smoothing regularizer, thereby facilitating the nuanced application of DRO; 2) Given the typically sparse nature of recommendation data, which might impede robust optimization, we introduce slight perturbations in the training distribution to expand its support.
Knowledge Translation: A New Pathway for Model Compression
Deep learning has witnessed significant advancements in recent years at the cost of increasing training, inference, and model storage overhead.
Fast ODE-based Sampling for Diffusion Models in Around 5 Steps
Sampling from diffusion models can be treated as solving the corresponding ordinary differential equations (ODEs), with the aim of obtaining an accurate solution with as few number of function evaluations (NFE) as possible.
CDR: Conservative Doubly Robust Learning for Debiased Recommendation
In recommendation systems (RS), user behavior data is observational rather than experimental, resulting in widespread bias in the data.
Graph Neural Networks-based Hybrid Framework For Predicting Particle Crushing Strength
Therefore, we firstly generate a dataset with 45, 000 numerical simulations and 900 particle types to facilitate the research progress of machine learning for particle crushing.
OpenGSL: A Comprehensive Benchmark for Graph Structure Learning
Moreover, we observe that the learned graph structure demonstrates a strong generalization ability across different GNN models, despite the high computational and space consumption.
Theoretical foundations of studying criticality in the brain
These problems stem from the non-triviality and immaturity of the physical theories that analytically derive brain criticality and the statistic techniques that estimate brain criticality from empirical data.
A Geometric Perspective on Diffusion Models
Recent years have witnessed significant progress in developing effective training and fast sampling techniques for diffusion models.
Temporal Aggregation and Propagation Graph Neural Networks for Dynamic Representation
The whole temporal neighborhood of nodes reveals the varying preferences of nodes.
Transition Propagation Graph Neural Networks for Temporal Networks
The proposed TIP-GNN focuses on the bilevel graph structure in temporal networks: besides the explicit interaction graph, a node's sequential interactions can also be constructed as a transition graph.
Robust Sequence Networked Submodular Maximization
The approximation ratio of the algorithm depends both on the number of the removed elements and the network topology.
Accelerating Diffusion Sampling with Classifier-based Feature Distillation
Instead of aligning output images, we distill teacher's sharpened feature distribution into the student with a dataset-independent classifier, making the student focus on those important features to improve performance.
Improving Knowledge Graph Embedding via Iterative Self-Semantic Knowledge Distillation
Iteratively incorporating and accumulating iteration-based semantic information enables the low-dimensional model to be more expressive for better link prediction in KGs.
Knowledge Distillation with the Reused Teacher Classifier
Knowledge distillation aims to compress a powerful yet cumbersome teacher model into a lightweight student model without much sacrifice of performance.
Ranked #3 on
Knowledge Distillation
on CIFAR-100
High-contrast, speckle-free, true 3D holography via binary CGH optimization
Here, we propose the practical solution to realize speckle-free, high-contrast, true 3D holography by combining random-phase, temporal multiplexing, binary holography, and binary optimization.
Online Adversarial Distillation for Graph Neural Networks
However, its effect on graph neural networks is less than satisfactory since the graph topology and node attributes are likely to change in a dynamic way and in this case a static teacher model is insufficient in guiding student training.
Learning Dynamic Preference Structure Embedding From Temporal Networks
Our proposed Dynamic Preference Structure (DPS) framework consists of two stages: structure sampling and graph fusion.
Exploring the Connection between Knowledge Distillation and Logits Matching
Knowledge distillation is a generalized logits matching technique for model compression.
Enhancing Entity Boundary Detection for Better Chinese Named Entity Recognition
In comparison with English, due to the lack of explicit word boundary and tenses information, Chinese Named Entity Recognition (NER) is much more challenging.
Cross-Layer Distillation with Semantic Calibration
Knowledge distillation is a technique to enhance the generalization ability of a student model by exploiting outputs from a teacher model.
SamWalker++: recommendation with informative sampling strategy
However, the social network information may not be available in many recommender systems, which hinders application of SamWalker.
CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation
To deal with these problems, we propose an efficient and effective collaborative sampling method CoSam, which consists of: (1) a collaborative sampler model that explicitly leverages user-item interaction information in sampling probability and exhibits good properties of normalization, adaption, interaction information awareness, and sampling efficiency; and (2) an integrated sampler-recommender framework, leveraging the sampler model in prediction to offset the bias caused by uneven sampling.
Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback
A popular and effective approach for implicit recommendation is to treat unobserved data as negative but downweight their confidence.
Online Knowledge Distillation with Diverse Peers
The second-level distillation is performed to transfer the knowledge in the ensemble of auxiliary peers further to the group leader, i. e., the model used for inference.
Relational Multi-Manifold Co-Clustering
In this study, we focus on improving the co-clustering performance via manifold ensemble learning, which is able to maximally approximate the intrinsic manifolds of both the sample and feature spaces.
Scalable Image Retrieval by Sparse Product Quantization
Unlike the existing approaches, in this paper, we propose a novel approach called Sparse Product Quantization (SPQ) to encoding the high-dimensional feature vectors into sparse representation.
Weakly Supervised Multiclass Video Segmentation
In this paper, we present a novel nearest neighbor-based label transfer scheme for weakly supervised video segmentation.
Semi-Supervised Coupled Dictionary Learning for Person Re-identification
In this paper, to bridge the human appearance variations across cameras, two coupled dictionaries that relate to the gallery and probe cameras are jointly learned in the training phase from both labeled and unlabeled images.
Semi-supervised Node Splitting for Random Forest Construction
Node splitting is an important issue in Random Forest but robust splitting requires a large number of training samples.
Probabilistic Graphlet Cut: Exploiting Spatial Structure Cue for Weakly Supervised Image Segmentation
Finally, we propose a novel image segmentation algorithm, called graphlet cut, that leverages the learned graphlet distribution in measuring the homogeneity of a set of spatially structured superpixels.
Spectral Graph Cut from a Filtering Point of View
In this paper, we analyze image segmentation algorithms that are based on spectral graph theory, e. g., normalized cut, and show that there is a natural connection between spectural graph theory based image segmentationand and edge preserving filtering.
