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Found 24 papers, 13 papers with code
Graph Partial Label Learning with Potential Cause Discovering
PLL is a critical weakly supervised learning problem, where each training instance is associated with a set of candidate labels, including both the true label and additional noisy labels.
BayesPrompt: Prompting Large-Scale Pre-Trained Language Models on Few-shot Inference via Debiased Domain Abstraction
From the perspective of distribution analyses, we disclose that the intrinsic issues behind the phenomenon are the over-multitudinous conceptual knowledge contained in PLMs and the abridged knowledge for target downstream domains, which jointly result in that PLMs mis-locate the knowledge distributions corresponding to the target domains in the universal knowledge embedding space.
T2MAC: Targeted and Trusted Multi-Agent Communication through Selective Engagement and Evidence-Driven Integration
This process enables agents to collectively use evidence garnered from multiple perspectives, fostering trusted and cooperative behaviors.
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive Learning
To this end, we propose a novel hierarchical topology isomorphism expertise embedded graph contrastive learning, which introduces knowledge distillations to empower GCL models to learn the hierarchical topology isomorphism expertise, including the graph-tier and subgraph-tier.
Rethinking Dimensional Rationale in Graph Contrastive Learning from Causal Perspective
To this end, with the purpose of exploring the intrinsic rationale of graphs, we accordingly propose to capture the dimensional rationale from graphs, which has not received sufficient attention in the literature.
Rethinking Causal Relationships Learning in Graph Neural Networks
In order to comprehensively analyze various GNN models from a causal learning perspective, we constructed an artificially synthesized dataset with known and controllable causal relationships between data and labels.
Unsupervised Social Event Detection via Hybrid Graph Contrastive Learning and Reinforced Incremental Clustering
To this end, recent works explore learning discriminative information from social messages by leveraging graph contrastive learning (GCL) and embedding clustering in an unsupervised manner.
M2HGCL: Multi-Scale Meta-Path Integrated Heterogeneous Graph Contrastive Learning
Orthogonal to homogeneous graphs, the types of nodes and edges in heterogeneous graphs are diverse so that specialized graph contrastive learning methods are required.
Information Theory-Guided Heuristic Progressive Multi-View Coding
To this end, we rethink the existing multi-view learning paradigm from the perspective of information theory and then propose a novel information theoretical framework for generalized multi-view learning.
Atomic and Subgraph-aware Bilateral Aggregation for Molecular Representation Learning
In this paper, we introduce a new model for molecular representation learning called the Atomic and Subgraph-aware Bilateral Aggregation (ASBA), which addresses the limitations of previous atom-wise and subgraph-wise models by incorporating both types of information.
Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective
By further observing the ramifications of introducing expertise logic into graph representation learning, we conclude that leading the GNNs to learn human expertise can improve the model performance.
MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning
As a successful approach to self-supervised learning, contrastive learning aims to learn invariant information shared among distortions of the input sample.
Modeling Multiple Views via Implicitly Preserving Global Consistency and Local Complementarity
To this end, we propose a methodology, specifically consistency and complementarity network (CoCoNet), which avails of strict global inter-view consistency and local cross-view complementarity preserving regularization to comprehensively learn representations from multiple views.
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language
Although artificial intelligence (AI) has made significant progress in understanding molecules in a wide range of fields, existing models generally acquire the single cognitive ability from the single molecular modality.
Ranked #7 on
Molecule Captioning
on ChEBI-20
Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest
However, the quality of the 1-best dependency tree for medical texts produced by an out-of-domain parser is relatively limited so that the performance of medical relation extraction method may degenerate.
Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal Perspective
To understand the reasons behind this phenomenon, we revisit the learning paradigm of knowledge distillation on the few-shot object detection task from the causal theoretic standpoint, and accordingly, develop a Structural Causal Model.
Robust Causal Graph Representation Learning against Confounding Effects
This observation reveals that there exist confounders in graphs, which may interfere with the model learning semantic information, and current graph representation learning methods have not eliminated their influence.
Interventional Contrastive Learning with Meta Semantic Regularizer
Contrastive learning (CL)-based self-supervised learning models learn visual representations in a pairwise manner.
Supporting Vision-Language Model Inference with Causality-pruning Knowledge Prompt
Vision-language models are pre-trained by aligning image-text pairs in a common space so that the models can deal with open-set visual concepts by learning semantic information from textual labels.
MetAug: Contrastive Learning via Meta Feature Augmentation
We perform a meta learning technique to build the augmentation generator that updates its network parameters by considering the performance of the encoder.
Robust Local Preserving and Global Aligning Network for Adversarial Domain Adaptation
We conduct theoretical analysis on the robustness of the proposed RLPGA and prove that the robust informative-theoretic-based loss and the local preserving module are beneficial to reduce the empirical risk of the target domain.
Bootstrapping Informative Graph Augmentation via A Meta Learning Approach
To this end, we propose a novel approach to learning a graph augmenter that can generate an augmentation with uniformity and informativeness.
Domain-Invariant Representation Learning with Global and Local Consistency
In this paper, we give an analysis of the existing representation learning framework of unsupervised domain adaptation and show that the learned feature representations of the source domain samples are with discriminability, compressibility, and transferability.
Information Theory-Guided Heuristic Progressive Multi-View Coding
To this end, we rethink the existing multi-view learning paradigm from the information theoretical perspective and then propose a novel information theoretical framework for generalized multi-view learning.
