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Found 43 papers, 17 papers with code
Efficient Sharpness-aware Minimization for Molecular Graph Transformer Models
Sharpness-aware minimization (SAM) has received increasing attention in computer vision since it can effectively eliminate the sharp local minima from the training trajectory and mitigate generalization degradation.
Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models
In this work, we propose to detect OOD molecules by adopting an auxiliary diffusion model-based framework, which compares similarities between input molecules and reconstructed graphs.
Retrieval-Enhanced Knowledge Editing for Multi-Hop Question Answering in Language Models
Large Language Models (LLMs) have shown proficiency in question-answering tasks but often struggle to integrate real-time knowledge updates, leading to potentially outdated or inaccurate responses.
Privacy-preserving Fine-tuning of Large Language Models through Flatness
The privacy concerns associated with the use of Large Language Models (LLMs) have grown recently with the development of LLMs such as ChatGPT.
PokeMQA: Programmable knowledge editing for Multi-hop Question Answering
Multi-hop question answering (MQA) is one of the challenging tasks to evaluate machine's comprehension and reasoning abilities, where large language models (LLMs) have widely achieved the human-comparable performance.
LETA: Learning Transferable Attribution for Generic Vision Explainer
To address this problem, we develop a pre-trained, DNN-based, generic explainer on large-scale image datasets, and leverage its transferability to explain various vision models for downstream tasks.
Marginal Nodes Matter: Towards Structure Fairness in Graphs
Specifically, we first analyzed multiple graphs and observed that marginal nodes in graphs have a worse performance of downstream tasks than others in graph neural networks.
DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research
The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are used to describe similar research.
Hessian-aware Quantized Node Embeddings for Recommendation
To address the gradient mismatch problem in STE, we further consider the quantized errors and its second-order derivatives for better stability.
ENGAGE: Explanation Guided Data Augmentation for Graph Representation Learning
Then, we design two data augmentation schemes on graphs for perturbing structural and feature information, respectively.
Editable Graph Neural Network for Node Classifications
However, model editing for graph neural networks (GNNs) is rarely explored, despite GNNs' widespread applicability.
Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model
While the model parameters do contribute to memory usage, the primary memory bottleneck during training arises from storing feature maps, also known as activations, as they are crucial for gradient calculation.
Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt
Thus, optimizing this accuracy-efficiency trade-off is crucial for the LLM deployment on commodity hardware.
Context-aware Domain Adaptation for Time Series Anomaly Detection
We formulate context sampling into the Markov decision process and exploit deep reinforcement learning to optimize the time series domain adaptation process via context sampling and design a tailored reward function to generate domain-invariant features that better align two domains for anomaly detection.
A Survey of Graph Prompting Methods: Techniques, Applications, and Challenges
In particular, we introduce the basic concepts of graph prompt learning, organize the existing work of designing graph prompting functions, and describe their applications and future challenges.
MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning
Recently, the training paradigm of "pre-train, fine-tune" has been leveraged to improve the generalization capabilities of GNNs.
TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems
We found that our TinyKG with INT2 quantization aggressively reduces the memory footprint of activation maps with $7 \times$, only with $2\%$ loss in accuracy, allowing us to deploy KGNNs on memory-constrained devices.
Adaptive Risk-Aware Bidding with Budget Constraint in Display Advertising
In this work, we explicitly factor in the uncertainty of estimated ad impression values and model the risk preference of a DSP under a specific state and market environment via a sequential decision process.
QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks
Quantum neural networks (QNNs), an interdisciplinary field of quantum computing and machine learning, have attracted tremendous research interests due to the specific quantum advantages.
RSC: Accelerating Graph Neural Networks Training via Randomized Sparse Computations
To this end, we propose Randomized Sparse Computation, which for the first time demonstrate the potential of training GNNs with approximated operations.
AdaGCL: Adaptive Subgraph Contrastive Learning to Generalize Large-scale Graph Training
To bridge the gap between large-scale graph training and contrastive learning, we propose adaptive subgraph contrastive learning (AdaGCL).
A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking
Large-scale graph training is a notoriously challenging problem for graph neural networks (GNNs).
Ranked #2 on
Node Property Prediction
on ogbn-products
GPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural Networks
Based on the pre-trained model, we propose the graph prompting function to modify the standalone node into a token pair, and reformulate the downstream node classification looking the same as edge prediction.
Differentially Private Counterfactuals via Functional Mechanism
Counterfactual, serving as one emerging type of model explanation, has attracted tons of attentions recently from both industry and academia.
Towards Similarity-Aware Time-Series Classification
Prior work has approached TSC from two major directions: (1) similarity-based methods that classify time-series based on the nearest neighbors, and (2) deep learning models that directly learn the representations for classification in a data-driven manner.
CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks
In this paper, we investigate GNNs from the lens of weight and feature loss landscapes, i. e., the loss changes with respect to model weights and node features, respectively.
EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression
Based on the implementation, we propose a memory-efficient framework called ``EXACT'', which for the first time demonstrate the potential and evaluate the feasibility of training GNNs with compressed activations.
AutoCoG: A Unified Data-Modal Co-Search Framework for Graph Neural Networks
Despite the preliminary success, we argue that for GNNs, NAS has to be customized further, due to the topological complicacy of GNN input data (graph) as well as the notorious training instability.
An Information Fusion Approach to Learning with Instance-Dependent Label Noise
Instance-dependent label noise (IDN) widely exists in real-world datasets and usually misleads the training of deep neural networks.
Orthogonal Graph Neural Networks
Graph neural networks (GNNs) have received tremendous attention due to their superiority in learning node representations.
Adaptive Label Smoothing To Regularize Large-Scale Graph Training
Graph neural networks (GNNs), which learn the node representations by recursively aggregating information from its neighbors, have become a predominant computational tool in many domains.
Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study
In view of those, we present the first fair and reproducible benchmark dedicated to assessing the "tricks" of training deep GNNs.
Dirichlet Energy Constrained Learning for Deep Graph Neural Networks
To this end, we analyze the bottleneck of deep GNNs by leveraging the Dirichlet energy of node embeddings, and propose a generalizable principle to guide the training of deep GNNs.
Simplifying Deep Reinforcement Learning via Self-Supervision
Supervised regression to demonstrations has been demonstrated to be a stable way to train deep policy networks.
DivAug: Plug-in Automated Data Augmentation with Explicit Diversity Maximization
We validate in experiments that the relative gain from automated data augmentation in test accuracy is highly correlated to Variance Diversity.
Detecting Interactions from Neural Networks via Topological Analysis
Motivated by the observation, in this paper, we propose to investigate the interaction detection problem from a novel topological perspective by analyzing the connectivity in neural networks.
Towards Interaction Detection Using Topological Analysis on Neural Networks
Detecting statistical interactions between input features is a crucial and challenging task.
Policy-GNN: Aggregation Optimization for Graph Neural Networks
It is a challenging task to develop an effective aggregation strategy for each node, given complex graphs and sparse features.
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning
Outlier detection is an important data mining task with numerous practical applications such as intrusion detection, credit card fraud detection, and video surveillance.
Towards Deeper Graph Neural Networks with Differentiable Group Normalization
Graph neural networks (GNNs), which learn the representation of a node by aggregating its neighbors, have become an effective computational tool in downstream applications.
Multi-Channel Graph Convolutional Networks
To further improve the graph representation learning ability, hierarchical GNN has been explored.
Auto-GNN: Neural Architecture Search of Graph Neural Networks
First, the search space of GNN is different from the ones in existing NAS work.
Ranked #38 on
Node Classification
on Cora
Experience Replay Optimization
Experience replay enables reinforcement learning agents to memorize and reuse past experiences, just as humans replay memories for the situation at hand.
