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Found 56 papers, 25 papers with code
Graph-based Retrieval Augmented Generation for Dynamic Few-shot Text Classification
While neural network-based models, such as CNN and BERT, have demonstrated remarkable performance in text classification, their effectiveness heavily relies on abundant labeled training data.
Revisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements
Graph-level tasks, which predict properties or classes for the entire graph, are critical for applications, such as molecular property prediction and subgraph counting.
A Survey on Large Language Model Acceleration based on KV Cache Management
This survey provides a comprehensive overview of KV cache management strategies for LLM acceleration, categorizing them into token-level, model-level, and system-level optimizations.
An Investigation on the Potential of KAN in Speech Enhancement
High-fidelity speech enhancement often requires sophisticated modeling to capture intricate, multiscale patterns.
Cross-Attention Graph Neural Networks for Inferring Gene Regulatory Networks with Skewed Degree Distribution
However, most of these studies have not considered the skewed degree distribution of genes.
Demystifying Workload Imbalances in Large Transformer Model Training over Variable-length Sequences
To optimize large Transformer model training, efficient parallel computing and advanced data management are essential.
Learning Symmetry-Independent Jet Representations via Jet-Based Joint Embedding Predictive Architecture
In high energy physics, self-supervised learning (SSL) methods have the potential to aid in the creation of machine learning models without the need for labeled datasets for a variety of tasks, including those related to jets -- narrow sprays of particles produced by quarks and gluons in high energy particle collisions.
Reconstruction of boosted and resolved multi-Higgs-boson events with symmetry-preserving attention networks
However, the complexity of jet assignment increases when simultaneously considering both $H\rightarrow b\bar{b}$ reconstruction possibilities, i. e., two "resolved" small-radius jets each containing a shower initiated by a $b$-quark or one "boosted" large-radius jet containing a merged shower initiated by a $b\bar{b}$ pair.
Noro: A Noise-Robust One-shot Voice Conversion System with Hidden Speaker Representation Capabilities
Experimental results demonstrate that Noro outperforms our baseline system in both clean and noisy scenarios, highlighting its efficacy for real-world applications.
Subgraph Aggregation for Out-of-Distribution Generalization on Graphs
To address these challenges, we propose a novel framework, SubGraph Aggregation (SuGAr), designed to learn a diverse set of subgraphs that are crucial for OOD generalization on graphs.
Molecular Property Prediction
Out-of-Distribution Generalization
+1
MixPolyp: Integrating Mask, Box and Scribble Supervision for Enhanced Polyp Segmentation
Limited by the expensive labeling, polyp segmentation models are plagued by data shortages.
LlamaPartialSpoof: An LLM-Driven Fake Speech Dataset Simulating Disinformation Generation
Previous fake speech datasets were constructed from a defender's perspective to develop countermeasure (CM) systems without considering diverse motivations of attackers.
Impacts of Darwinian Evolution on Pre-trained Deep Neural Networks
The empirical results show that the proposed framework has positive impacts on the network, with reduced over-fitting and an order of magnitude lower time complexity compared to BP.
Why Are My Prompts Leaked? Unraveling Prompt Extraction Threats in Customized Large Language Models
In this paper, we analyze the underlying mechanism of prompt leakage, which we refer to as prompt memorization, and develop corresponding defending strategies.
Is Large Language Model Good at Database Knob Tuning? A Comprehensive Experimental Evaluation
Knob tuning plays a crucial role in optimizing databases by adjusting knobs to enhance database performance.
ScholarChemQA: Unveiling the Power of Language Models in Chemical Research Question Answering
We hope our benchmark and model can facilitate and promote more research on chemical QA.
Text-based Talking Video Editing with Cascaded Conditional Diffusion
The cascaded conditional diffusion model decomposes the complex talking editing task into two flexible generation tasks, which provides a generalizable talking-face representation, seamless audio-visual transitions, and identity-preserved faces on a small dataset.
FacLens: Transferable Probe for Foreseeing Non-Factuality in Large Language Models
Despite advancements in large language models (LLMs), non-factual responses remain prevalent.
Non-destructive Degradation Pattern Decoupling for Ultra-early Battery Prototype Verification Using Physics-informed Machine Learning
Manufacturing complexities and uncertainties have impeded the transition from material prototypes to commercial batteries, making prototype verification critical to quality assessment.
PixelsDB: Serverless and NL-Aided Data Analytics with Flexible Service Levels and Prices
The queries are then executed by a serverless query engine that offers varying prices for different performance service levels (SLAs).
Cardinality Estimation on Hyper-relational Knowledge Graphs
However, existing CE methods over KGs achieve unsatisfying performance on HKGs due to the complexity of qualifiers in HKGs.
UniCL: A Universal Contrastive Learning Framework for Large Time Series Models
Time-series analysis plays a pivotal role across a range of critical applications, from finance to healthcare, which involves various tasks, such as forecasting and classification.
LLMTune: Accelerate Database Knob Tuning with Large Language Models
Database knob tuning is a critical challenge in the database community, aiming to optimize knob values to enhance database performance for specific workloads.
HDLdebugger: Streamlining HDL debugging with Large Language Models
In the domain of chip design, Hardware Description Languages (HDLs) play a pivotal role.
Developing and Deploying Industry Standards for Artificial Intelligence in Education (AIED): Challenges, Strategies, and Future Directions
Finally, we outline a strategic roadmap for stakeholders to implement these standards, fostering a cohesive and ethical AIED ecosystem.
Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts
In this paper, we discover that there exist cases with distribution shifts unobservable in the time domain while observable in the spectral domain, and propose to study distribution shifts on dynamic graphs in the spectral domain for the first time.
Intent-aware Recommendation via Disentangled Graph Contrastive Learning
The other is different behaviors have different intent distributions, so how to establish their relations for a more explainable recommender system.
CodeS: Towards Building Open-source Language Models for Text-to-SQL
To address the limitations, we introduce CodeS, a series of pre-trained language models with parameters ranging from 1B to 15B, specifically designed for the text-to-SQL task.
Bringing Generative AI to Adaptive Learning in Education
The recent surge in generative AI technologies, such as large language models and diffusion models, has boosted the development of AI applications in various domains, including science, finance, and education.
Cross-Space Adaptive Filter: Integrating Graph Topology and Node Attributes for Alleviating the Over-smoothing Problem
To this end, various methods have been proposed to create an adaptive filter by incorporating an extra filter (e. g., a high-pass filter) extracted from the graph topology.
Graph Invariant Learning with Subgraph Co-mixup for Out-Of-Distribution Generalization
In this paper, we propose a novel graph invariant learning method based on invariant and variant patterns co-mixup strategy, which is capable of jointly generating mixed multiple environments and capturing invariant patterns from the mixed graph data.
Graph Representation Learning
Out-of-Distribution Generalization
Out-of-Distribution Generalized Dynamic Graph Neural Network with Disentangled Intervention and Invariance Promotion
In this paper, we propose Disentangled Intervention-based Dynamic graph Attention networks with Invariance Promotion (I-DIDA) to handle spatio-temporal distribution shifts in dynamic graphs by discovering and utilizing invariant patterns, i. e., structures and features whose predictive abilities are stable across distribution shifts.
LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on Dynamic Graphs?
Our main observations are: 1) LLMs have preliminary spatial-temporal understanding abilities on dynamic graphs, 2) Dynamic graph tasks show increasing difficulties for LLMs as the graph size and density increase, while not sensitive to the time span and data generation mechanism, 3) the proposed DST2 prompting method can help to improve LLMs' spatial-temporal understanding abilities on dynamic graphs for most tasks.
Automated Bioinformatics Analysis via AutoBA
With the fast-growing and evolving omics data, the demand for streamlined and adaptable tools to handle the analysis continues to grow.
Graph Meets LLMs: Towards Large Graph Models
In order to promote applying large models for graphs forward, we present a perspective paper to discuss the challenges and opportunities associated with developing large graph models.
Accelerating Text-to-Image Editing via Cache-Enabled Sparse Diffusion Inference
The key intuition behind our approach is to utilize the semantic mapping between the minor modifications on the input text and the affected regions on the output image.
Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI
However, there are few open-source frameworks for federated heterogeneous medical image analysis with personalization and privacy protection simultaneously without the demand to modify the existing model structures or to share any private data.
Audit to Forget: A Unified Method to Revoke Patients' Private Data in Intelligent Healthcare
Revoking personal private data is one of the basic human rights, which has already been sheltered by several privacy-preserving laws in many countries.
RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL
Due to the structural property of the SQL queries, the seq2seq model takes the responsibility of parsing both the schema items (i. e., tables and columns) and the skeleton (i. e., SQL keywords).
Ranked #1 on
Semantic Parsing
on spider
Curriculum Graph Machine Learning: A Survey
To the best of our knowledge, this paper is the first survey for curriculum graph machine learning.
FAIR AI Models in High Energy Physics
The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery.
Unsupervised Visual Defect Detection with Score-Based Generative Model
Thus, we focus on the unsupervised visual defect detection and localization tasks and propose a novel framework based on the recent score-based generative models, which synthesize the real image by iterative denoising through stochastic differential equations (SDEs).
Use Classifier as Generator
Image recognition/classification is a widely studied problem, but its reverse problem, image generation, has drawn much less attention until recently.
Tower Bridge Net (TB-Net): Bidirectional Knowledge Graph Aware Embedding Propagation for Explainable Recommender Systems
Recently, neural networks based models have been widely used for recommender systems (RS).
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
Out-Of-Distribution Generalization on Graphs: A Survey
This paper is the first systematic and comprehensive review of OOD generalization on graphs, to the best of our knowledge.
Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions
However, as the literature on graph learning booms with a vast number of emerging methods and techniques, it becomes increasingly difficult to manually design the optimal machine learning algorithm for different graph-related tasks.
OOD-GNN: Out-of-Distribution Generalized Graph Neural Network
Our proposed OOD-GNN employs a novel nonlinear graph representation decorrelation method utilizing random Fourier features, which encourages the model to eliminate the statistical dependence between relevant and irrelevant graph representations through iteratively optimizing the sample graph weights and graph encoder.
Disentangled Contrastive Learning on Graphs
Then we propose a novel factor-wise discrimination objective in a contrastive learning manner, which can force the factorized representations to independently reflect the expressive information from different latent factors.
An Experimental Study of Quantitative Evaluations on Saliency Methods
It has been long debated that eXplainable AI (XAI) is an important technology for model and data exploration, validation, and debugging.
AutoGL: A Library for Automated Graph Learning
To fill this gap, we present Automated Graph Learning (AutoGL), the first dedicated library for automated machine learning on graphs.
Noise Modulation: Let Your Model Interpret Itself
Given the great success of Deep Neural Networks(DNNs) and the black-box nature of it, the interpretability of these models becomes an important issue. The majority of previous research works on the post-hoc interpretation of a trained model. But recently, adversarial training shows that it is possible for a model to have an interpretable input-gradient through training. However, adversarial training lacks efficiency for interpretability. To resolve this problem, we construct an approximation of the adversarial perturbations and discover a connection between adversarial training and amplitude modulation.
Quantitative Evaluations on Saliency Methods: An Experimental Study
It has been long debated that eXplainable AI (XAI) is an important topic, but it lacks rigorous definition and fair metrics.
Efficient Reservoir Management through Deep Reinforcement Learning
Dams impact downstream river dynamics through flow regulation and disruption of upstream-downstream linkages.
Masked Gradient-Based Causal Structure Learning
This paper studies the problem of learning causal structures from observational data.
Billion-scale Network Embedding with Iterative Random Projection
Network embedding, which learns low-dimensional vector representation for nodes in the network, has attracted considerable research attention recently.
