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Found 129 papers, 69 papers with code
OCR Hinders RAG: Evaluating the Cascading Impact of OCR on Retrieval-Augmented Generation
In this paper, we introduce OHRBench, the first benchmark for understanding the cascading impact of OCR on RAG systems.
Optical Character Recognition
Optical Character Recognition (OCR)
+2
Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learning
With the increasing computation of training graph neural networks (GNNs) on large-scale graphs, graph condensation (GC) has emerged as a promising solution to synthesize a compact, substitute graph of the large-scale original graph for efficient GNN training.
Can LLMs be Good Graph Judger for Knowledge Graph Construction?
We seek to utilize the capacity of LLMs to function as a graph judger, a capability superior to their role only as a predictor for KG construction problems.
Beyond Sight: Towards Cognitive Alignment in LVLM via Enriched Visual Knowledge
Specifically, the VE's representation of visual information may not fully align with LLM's cognitive framework, leading to a mismatch where visual features exceed the language model's interpretive range.
Towards Precise Scaling Laws for Video Diffusion Transformers
Achieving optimal performance of video diffusion transformers within given data and compute budget is crucial due to their high training costs.
Epidemiology-informed Network for Robust Rumor Detection
Given the variations in topics and social impact of the root node, different source information naturally has distinct outreach capabilities, resulting in different heights of propagation trees.
Toward Personalized Federated Node Classification in One-shot Communication
Specifically, for effective graph learning in a single communication round, our method estimates and aggregates class-wise feature distribution statistics to construct a global pseudo-graph on the server, facilitating the training of a global graph model.
MC-LLaVA: Multi-Concept Personalized Vision-Language Model
Specifically, MC-LLaVA uses a joint training strategy incorporating multiple concepts in a single training step, allowing VLMs to perform accurately in multi-concept personalization.
VersaTune: An Efficient Data Composition Framework for Training Multi-Capability LLMs
Despite their potential, existing work mainly focuses on domain-specific enhancements during fine-tuning, the challenge of which lies in catastrophic forgetting of knowledge across other domains.
EVQAScore: Efficient Video Question Answering Data Evaluation
To address this gap, we introduce EVQAScore, a reference-free method that leverages keyword extraction to assess both video caption and video QA data quality.
RAGraph: A General Retrieval-Augmented Graph Learning Framework
Graph Neural Networks (GNNs) have become essential in interpreting relational data across various domains, yet, they often struggle to generalize to unseen graph data that differs markedly from training instances.
VecCity: A Taxonomy-guided Library for Map Entity Representation Learning
First, existing research is fragmented, with models classified by the type of map entity, limiting the reusability of techniques across different tasks.
Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extraction
Document parsing is essential for converting unstructured and semi-structured documents-such as contracts, academic papers, and invoices-into structured, machine-readable data.
Nova: A Practical and Advanced Alignment
Importantly, Qwen2-Nova-72B and Llama3-PBM-Nova-70B are instruct versions of the Qwen2-72B and Llama-3-70B base models, optimized through Nova.
Facilitating Multi-turn Function Calling for LLMs via Compositional Instruction Tuning
While current research on function calling by LLMs primarily focuses on single-turn interactions, this paper addresses the overlooked necessity for LLMs to engage in multi-turn function calling--critical for handling compositional, real-world queries that require planning with functions but not only use functions.
FB-Bench: A Fine-Grained Multi-Task Benchmark for Evaluating LLMs' Responsiveness to Human Feedback
Human feedback is crucial in the interactions between humans and Large Language Models (LLMs).
Semantic Score Distillation Sampling for Compositional Text-to-3D Generation
To overcome these challenges, we introduce a novel SDS approach, Semantic Score Distillation Sampling (SemanticSDS), designed to effectively improve the expressiveness and accuracy of compositional text-to-3D generation.
Multi-Agent Collaborative Data Selection for Efficient LLM Pretraining
Efficient data selection is crucial to accelerate the pretraining of large language models (LLMs).
Trans4D: Realistic Geometry-Aware Transition for Compositional Text-to-4D Synthesis
Then we propose a geometry-aware 4D transition network to realize a complex scene-level 4D transition based on the plan, which involves expressive geometrical object deformation.
Gradual Learning: Optimizing Fine-Tuning with Partially Mastered Knowledge in Large Language Models
During the pretraining phase, large language models (LLMs) acquire vast amounts of knowledge from extensive text corpora.
QAEncoder: Towards Aligned Representation Learning in Question Answering System
Modern QA systems entail retrieval-augmented generation (RAG) for accurate and trustworthy responses.
Universal Medical Image Representation Learning with Compositional Decoders
To address these challenges, we have developed a decomposed-composed universal medical imaging paradigm (UniMed) that supports tasks at all levels.
Data Proportion Detection for Optimized Data Management for Large Language Models
Large language models (LLMs) have demonstrated exceptional performance across a wide range of tasks and domains, with data preparation playing a critical role in achieving these results.
BEATS: Optimizing LLM Mathematical Capabilities with BackVerify and Adaptive Disambiguate based Efficient Tree Search
Large Language Models (LLMs) have exhibited exceptional performance across a broad range of tasks and domains.
Enhancing Unsupervised Sentence Embeddings via Knowledge-Driven Data Augmentation and Gaussian-Decayed Contrastive Learning
Recently, using large language models (LLMs) for data augmentation has led to considerable improvements in unsupervised sentence embedding models.
Retrofitting Temporal Graph Neural Networks with Transformer
We also adapt Transformer codebase to train TF-TGN efficiently with multiple GPUs.
DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective Partitioning
Through extensive experimental analysis, we identified three key challenges in designing effective data management strategies that enable the model to achieve long-context capability without sacrificing performance in other tasks: (1) a shortage of long documents across multiple domains, (2) effective construction of context windows, and (3) efficient organization of large-scale datasets.
OpenFGL: A Comprehensive Benchmarks for Federated Graph Learning
Federated graph learning (FGL) has emerged as a promising distributed training paradigm for graph neural networks across multiple local systems without direct data sharing.
BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and Deduplication by Introducing a Competitive Large Language Model Baseline
The general capabilities of Large Language Models (LLM) highly rely on the composition and selection on extensive pretraining datasets, treated as commercial secrets by several institutions.
Fire-Flyer AI-HPC: A Cost-Effective Software-Hardware Co-Design for Deep Learning
For DL training, we deployed the Fire-Flyer 2 with 10, 000 PCIe A100 GPUs, achieved performance approximating the DGX-A100 while reducing costs by half and energy consumption by 40%.
SEA: Supervised Embedding Alignment for Token-Level Visual-Textual Integration in MLLMs
Multimodal Large Language Models (MLLMs) have recently demonstrated remarkable perceptual and reasoning abilities, typically comprising a Vision Encoder, an Adapter, and a Large Language Model (LLM).
Ranked #51 on
Visual Question Answering
on MM-Vet
RAG-Optimized Tibetan Tourism LLMs: Enhancing Accuracy and Personalization
With the development of the modern social economy, tourism has become an important way to meet people's spiritual needs, bringing development opportunities to the tourism industry.
SysBench: Can Large Language Models Follow System Messages?
Finally, we conduct extensive evaluation across various existing LLMs, measuring their ability to follow specified constraints given in system messages.
TC-RAG:Turing-Complete RAG's Case study on Medical LLM Systems
In the pursuit of enhancing domain-specific Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) emerges as a promising solution to mitigate issues such as hallucinations, outdated knowledge, and limited expertise in highly specialized queries.
MathScape: Evaluating MLLMs in multimodal Math Scenarios through a Hierarchical Benchmark
To address this gap, we proposed MathScape, a new benchmark that emphasizes the understanding and application of combined visual and textual information.
CFBench: A Comprehensive Constraints-Following Benchmark for LLMs
The adeptness of Large Language Models (LLMs) in comprehending and following natural language instructions is critical for their deployment in sophisticated real-world applications.
Are Bigger Encoders Always Better in Vision Large Models?
Moreover, we analyzed the effects of LLM backbone parameter size and data quality on the pretraining outcomes.
Synth-Empathy: Towards High-Quality Synthetic Empathy Data
In recent years, with the rapid advancements in large language models (LLMs), achieving excellent empathetic response capabilities has become a crucial prerequisite.
SynthVLM: High-Efficiency and High-Quality Synthetic Data for Vision Language Models
Crucially, our method's reliance on purely generated data ensures the preservation of privacy, achieving SoTA performance with just 100k data points (only 18% of the official dataset size).
Research on Tibetan Tourism Viewpoints information generation system based on LLM
Tibet, ensconced within China's territorial expanse, is distinguished by its labyrinthine and heterogeneous topography, a testament to its profound historical heritage, and the cradle of a unique religious ethos.
Physics-guided Active Sample Reweighting for Urban Flow Prediction
Moreover, due to the interval-based predictions and intermittent nature of data filing in many transportation services, the instantaneous dynamics of urban flows can hardly be captured, rendering differential equation-based continuous modeling a loose fit for this setting.
Video In-context Learning
In-context learning for vision data has been underexplored compared with that in natural language.
AnyTaskTune: Advanced Domain-Specific Solutions through Task-Fine-Tuning
Our findings demonstrate that models fine-tuned using the \textbf{Task-Fine-Tune} methodology not only achieve superior performance on these specific tasks but also significantly outperform models with higher general capabilities in their respective domains.
PAS: Data-Efficient Plug-and-Play Prompt Augmentation System
This combination of high performance, efficiency, and flexibility makes PAS a valuable system for enhancing the usability and effectiveness of LLMs through improved prompt engineering.
KeyVideoLLM: Towards Large-scale Video Keyframe Selection
Recently, with the rise of web videos, managing and understanding large-scale video datasets has become increasingly important.
Efficient-Empathy: Towards Efficient and Effective Selection of Empathy Data
By integrating sensibility and rationality data with a MoE structure, we achieve even higher performance, demonstrating the effectiveness of our Efficient-Empathy algorithm.
Consistency Flow Matching: Defining Straight Flows with Velocity Consistency
Additionally, we propose a multi-segment training approach for Consistency-FM to enhance expressiveness, achieving a better trade-off between sampling quality and speed.
RobGC: Towards Robust Graph Condensation
Graph neural networks (GNNs) have attracted widespread attention for their impressive capability of graph representation learning.
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
We introduce Buffer of Thoughts (BoT), a novel and versatile thought-augmented reasoning approach for enhancing accuracy, efficiency and robustness of large language models (LLMs).
Graph Condensation for Open-World Graph Learning
Consequently, due to the limited generalization capacity of condensed graphs, applications that employ GC for efficient GNN training end up with sub-optimal GNNs when confronted with evolving graph structures and distributions in dynamic real-world situations.
A Survey of Multimodal Large Language Model from A Data-centric Perspective
Multimodal large language models (MLLMs) enhance the capabilities of standard large language models by integrating and processing data from multiple modalities, including text, vision, audio, video, and 3D environments.
GlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning
Also, we evaluate how taxonomy prediction can boost other three function prediction tasks by MTL.
EditWorld: Simulating World Dynamics for Instruction-Following Image Editing
Therefore, this work, EditWorld, introduces a new editing task, namely world-instructed image editing, which defines and categorizes the instructions grounded by various world scenarios.
Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition
The increasing prevalence of large-scale graphs poses a significant challenge for graph neural network training, attributed to their substantial computational requirements.
Resilient control of networked switched systems subject to deception attack and DoS attack
In this paper, the resilient control for switched systems in the presence of deception attack and denial-of-service (DoS) attack is addressed.
Acceleration Algorithms in GNNs: A Survey
To address the critical challenges, a range of algorithms have been proposed to accelerate training and inference of GNNs, attracting increasing attention from the research community.
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation.
When Emotional Stimuli meet Prompt Designing: An Auto-Prompt Graphical Paradigm
With the development of Large Language Models (LLM), numerous prompts have been proposed, each with a rich set of features and their own merits.
Distribution-Aware Data Expansion with Diffusion Models
In this paper, we propose DistDiff, a training-free data expansion framework based on the distribution-aware diffusion model.
Cradle: Empowering Foundation Agents Towards General Computer Control
To handle this issue, we propose the General Computer Control (GCC) setting to restrict foundation agents to interact with software through the most unified and standardized interface, i. e., using screenshots as input and keyboard and mouse actions as output.
Retrieval-Augmented Generation for AI-Generated Content: A Survey
We first classify RAG foundations according to how the retriever augments the generator, distilling the fundamental abstractions of the augmentation methodologies for various retrievers and generators.
A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist
Notably, FinAgent is the first advanced multimodal foundation agent designed for financial trading tasks.
ProtLLM: An Interleaved Protein-Language LLM with Protein-as-Word Pre-Training
We propose ProtLLM, a versatile cross-modal large language model (LLM) for both protein-centric and protein-language tasks.
Kuaiji: the First Chinese Accounting Large Language Model
Large Language Models (LLMs) like ChatGPT and GPT-4 have demonstrated impressive proficiency in comprehending and generating natural language.
Rethinking Node-wise Propagation for Large-scale Graph Learning
However, (i) Most scalable GNNs tend to treat all nodes in graphs with the same propagation rules, neglecting their topological uniqueness; (ii) Existing node-wise propagation optimization strategies are insufficient on web-scale graphs with intricate topology, where a full portrayal of nodes' local properties is required.
Time Series Supplier Allocation via Deep Black-Litterman Model
Time Series Supplier Allocation (TSSA) poses a complex NP-hard challenge, aimed at refining future order dispatching strategies to satisfy order demands with maximum supply efficiency fully.
DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence
The rapid development of large language models has revolutionized code intelligence in software development.
Ranked #3 on
Code Generation
on APPS
True Knowledge Comes from Practice: Aligning LLMs with Embodied Environments via Reinforcement Learning
Despite the impressive performance across numerous tasks, large language models (LLMs) often fail in solving simple decision-making tasks due to the misalignment of the knowledge in LLMs with environments.
Graph Condensation: A Survey
We also empirically compare and analyze representative GC methods with diverse optimization strategies based on the five proposed GC evaluation criteria.
LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation Learning
Most existing graph neural networks (GNNs) are limited to undirected graphs, whose restricted scope of the captured relational information hinders their expressive capabilities and deployments in real-world scenarios.
FedGTA: Topology-aware Averaging for Federated Graph Learning
Existing FGL studies fall into two categories: (i) FGL Optimization, which improves multi-client training in existing machine learning models; (ii) FGL Model, which enhances performance with complex local models and multi-client interactions.
AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity
Then, each client conducts personalized training based on the local subgraph and the federated knowledge extractor.
Towards Effective and General Graph Unlearning via Mutual Evolution
With the rapid advancement of AI applications, the growing needs for data privacy and model robustness have highlighted the importance of machine unlearning, especially in thriving graph-based scenarios.
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
The rapid development of open-source large language models (LLMs) has been truly remarkable.
Improving Diffusion-Based Image Synthesis with Context Prediction
In this way, each point can better reconstruct itself by preserving its semantic connections with neighborhood context.
Ranked #1 on
Image Inpainting
on CelebA
(LPIPS metric)
Self-Supervised Deconfounding Against Spatio-Temporal Shifts: Theory and Modeling
It first encodes traffic data into two disentangled representations for associating invariant and variant ST contexts.
Reinforcement Learning with Maskable Stock Representation for Portfolio Management in Customizable Stock Pools
Specifically, the target stock pool of different investors varies dramatically due to their discrepancy on market states and individual investors may temporally adjust stocks they desire to trade (e. g., adding one popular stocks), which lead to customizable stock pools (CSPs).
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning
For graph self-supervised learning (GSSL), masked autoencoder (MAE) follows the generative paradigm and learns to reconstruct masked graph edges or node features.
Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagation
To further accelerate Scalable GNNs inference in this inductive setting, we propose an online propagation framework and two novel node-adaptive propagation methods that can customize the optimal propagation depth for each node based on its topological information and thereby avoid redundant feature propagation.
Learning Multiplex Representations on Text-Attributed Graphs with One Language Model Encoder
Mainstream text representation learning methods use pretrained language models (PLMs) to generate one embedding for each text unit, expecting that all types of relations between texts can be captured by these single-view embeddings.
IPDreamer: Appearance-Controllable 3D Object Generation with Complex Image Prompts
Recent advances in 3D generation have been remarkable, with methods such as DreamFusion leveraging large-scale text-to-image diffusion-based models to guide 3D object generation.
To better understand realized ecosystem services: An integrated analysis framework of supply, demand, flow and use
Realized ecosystem services (ES) are the actual use of ES by societies, which is more directly linked to human well-being than potential ES.
EarnHFT: Efficient Hierarchical Reinforcement Learning for High Frequency Trading
In stage II, we construct a pool of diverse RL agents for different market trends, distinguished by return rates, where hundreds of RL agents are trained with different preferences of return rates and only a tiny fraction of them will be selected into the pool based on their profitability.
Agents: An Open-source Framework for Autonomous Language Agents
Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with environments, humans, and other agents using natural language interfaces.
Towards General and Efficient Online Tuning for Spark
The distributed data analytic system -- Spark is a common choice for processing massive volumes of heterogeneous data, while it is challenging to tune its parameters to achieve high performance.
VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs
To address this issue, we propose to learn a new powerful graph representation space by directly labeling nodes' diverse local structures for GNN-to-MLP distillation.
Graph Condensation for Inductive Node Representation Learning
To overcome this issue, we propose mapping-aware graph condensation (MCond), explicitly learning the one-to-many node mapping from original nodes to synthetic nodes to seamlessly integrate new nodes into the synthetic graph for inductive representation learning.
Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalization
To remove class spurious feature caused by distribution shifts, we propose Individual Graph Information Bottleneck (I-GIB) which discards irrelevant information by minimizing the mutual information between the input graph and its embeddings.
Patton: Language Model Pretraining on Text-Rich Networks
A real-world text corpus sometimes comprises not only text documents but also semantic links between them (e. g., academic papers in a bibliographic network are linked by citations and co-authorships).
OpenBox: A Python Toolkit for Generalized Black-box Optimization
Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, experimental design, and database knob tuning.
Adapter Learning in Pretrained Feature Extractor for Continual Learning of Diseases
In particular, updating an intelligent diagnosis system with training data of new diseases would cause catastrophic forgetting of old disease knowledge.
GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretation
The generated queries naturally serve as interpretable representations of user interests and can be searched to recommend cold-start items.
Semantic-aware Node Synthesis for Imbalanced Heterogeneous Information Networks
To tackle the imbalance of minority classes and supplement their inadequate semantics, we present the first method for the semantic imbalance problem in imbalanced HINs named Semantic-aware Node Synthesis (SNS).
Transfer Learning for Bayesian Optimization: A Survey
A wide spectrum of design and decision problems, including parameter tuning, A/B testing and drug design, intrinsically are instances of black-box optimization.
Rover: An online Spark SQL tuning service via generalized transfer learning
When applying transfer learning to accelerate the tuning process, we notice two domain-specific challenges: 1) most previous work focus on transferring tuning history, while expert knowledge from Spark engineers is of great potential to improve the tuning performance but is not well studied so far; 2) history tasks should be carefully utilized, where using dissimilar ones lead to a deteriorated performance in production.
DivBO: Diversity-aware CASH for Ensemble Learning
To tackle this issue and further enhance the ensemble performance, we propose DivBO, a diversity-aware framework to inject explicit search of diversity into the CASH problems.
PAMI: partition input and aggregate outputs for model interpretation
There is an increasing demand for interpretation of model predictions especially in high-risk applications.
Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-Training
Generating images from graph-structured inputs, such as scene graphs, is uniquely challenging due to the difficulty of aligning nodes and connections in graphs with objects and their relations in images.
Efficient Graph Neural Network Inference at Large Scale
Graph neural networks (GNNs) have demonstrated excellent performance in a wide range of applications.
Distributed Graph Neural Network Training: A Survey
In recent years, many efforts have been made on distributed GNN training, and an array of training algorithms and systems have been proposed.
Diffusion Models: A Comprehensive Survey of Methods and Applications
This survey aims to provide a contextualized, in-depth look at the state of diffusion models, identifying the key areas of focus and pointing to potential areas for further exploration.
Efficient 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.
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 #12 on
Node Property Prediction
on ogbn-mag
TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning
With the extensive applications of machine learning models, automatic hyperparameter optimization (HPO) has become increasingly important.
Transfer Learning based Search Space Design for Hyperparameter Tuning
The extensive experiments show that our approach considerably boosts BO by designing a promising and compact search space instead of using the entire space, and outperforms the state-of-the-arts on a wide range of benchmarks, including machine learning and deep learning tuning tasks, and neural architecture search.
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.
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.
Two-Level Supervised Contrastive Learning for Response Selection in Multi-Turn Dialogue
We further develop a new method for supervised contrastive learning, referred to as two-level supervised contrastive learning, and employ the method in response selection in multi-turn dialogue.
Ranked #3 on
Conversational Response Selection
on E-commerce
Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale
The ever-growing demand and complexity of machine learning are putting pressure on hyper-parameter tuning systems: while the evaluation cost of models continues to increase, the scalability of state-of-the-arts starts to become a crucial bottleneck.
K-Core Decomposition on Super Large Graphs with Limited Resources
For example, the distributed K-core decomposition algorithm can scale to a large graph with 136 billion edges without losing correctness with our divide-and-conquer technique.
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.