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Found 18 papers, 5 papers with code
Efficient Bilingual Generalization from Neural Transduction Grammar Induction
We introduce (1) a novel neural network structure for bilingual modeling of sentence pairs that allows efficient capturing of bilingual relationship via biconstituent composition, (2) the concept of neural network biparsing, which applies to not only machine translation (MT) but also to a variety of other bilingual research areas, and (3) the concept of a biparsing-backpropagation training loop, which we hypothesize that can efficiently learn complex biparse tree patterns.
COMAE: COMprehensive Attribute Exploration for Zero-shot Hashing
Zero-shot hashing (ZSH) has shown excellent success owing to its efficiency and generalization in large-scale retrieval scenarios.
Triad: A Framework Leveraging a Multi-Role LLM-based Agent to Solve Knowledge Base Question Answering
We evaluated the performance of our framework using three benchmark datasets, and the results show that our framework outperforms state-of-the-art systems on the LC-QuAD and YAGO-QA benchmarks, yielding F1 scores of 11. 8% and 20. 7%, respectively.
Inductive Graph Alignment Prompt: Bridging the Gap between Graph Pre-training and Inductive Fine-tuning From Spectral Perspective
Based on the insight of graph pre-training, we propose to bridge the graph signal gap and the graph structure gap with learnable prompts in the spectral space.
Invariant Graph Transformer
Based on the self-attention module, our proposed invariant graph Transformer (IGT) can achieve fine-grained, more specifically, node-level and virtual node-level intervention.
A Corpus for Named Entity Recognition in Chinese Novels with Multi-genres
Entities like person, location, organization are important for literary text analysis.
Multi-View Spectrogram Transformer for Respiratory Sound Classification
Deep neural networks have been applied to audio spectrograms for respiratory sound classification.
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems
Graph Neural Networks (GNNs) have emerged as promising solutions for collaborative filtering (CF) through the modeling of user-item interaction graphs.
A Matrix Ensemble Kalman Filter-based Multi-arm Neural Network to Adequately Approximate Deep Neural Networks
Deep Learners (DLs) are the state-of-art predictive mechanism with applications in many fields requiring complex high dimensional data processing.
Noisy Positive-Unlabeled Learning with Self-Training for Speculative Knowledge Graph Reasoning
State-of-the-art methods fall short in the speculative reasoning ability, as they assume the correctness of a fact is solely determined by its presence in KG, making them vulnerable to false negative/positive issues.
Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders
First, we define the problem of imputation over NTS which contains missing values in both node time series features and graph structures.
Continual Learning on Dynamic Graphs via Parameter Isolation
Dynamic graph learning methods commonly suffer from the catastrophic forgetting problem, where knowledge learned for previous graphs is overwritten by updates for new graphs.
Neural Exploitation and Exploration of Contextual Bandits
In recent literature, a series of neural bandit algorithms have been proposed to adapt to the non-linear reward function, combined with TS or UCB strategies for exploration.
STERLING: Synergistic Representation Learning on Bipartite Graphs
Most recent bipartite graph SSL methods are based on contrastive learning which learns embeddings by discriminating positive and negative node pairs.
COIN: Co-Cluster Infomax for Bipartite Graphs
We theoretically prove that COIN is able to effectively increase the mutual information of node embeddings and COIN is upper-bounded by the prior distributions of nodes.
EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits
To overcome this challenge, a series of neural bandit algorithms have been proposed, where a neural network is used to learn the underlying reward function and TS or UCB are adapted for exploration.
Unsupervised Belief Representation Learning with Information-Theoretic Variational Graph Auto-Encoders
Inspired by total correlation in information theory, we propose the Information-Theoretic Variational Graph Auto-Encoder (InfoVGAE) that learns to project both users and content items (e. g., posts that represent user views) into an appropriate disentangled latent space.
AceKG: A Large-scale Knowledge Graph for Academic Data Mining
Most existing knowledge graphs (KGs) in academic domains suffer from problems of insufficient multi-relational information, name ambiguity and improper data format for large-scale machine processing.
