Search Results for author:
Found 28 papers, 9 papers with code
Token2Wave
This paper provides an in-depth analysis of Token2Wave, a novel token representation method derived from the Wave Network, designed to capture both global and local semantics of input text through wave-inspired complex vectors.
Bridging the Gap: Representation Spaces in Neuro-Symbolic AI
Neuro-symbolic AI is an effective method for improving the overall performance of AI models by combining the advantages of neural networks and symbolic learning.
Neuro-Symbolic AI: Explainability, Challenges, and Future Trends
Explainability is an essential reason limiting the application of neural networks in many vital fields.
Wave Network: An Ultra-Small Language Model
We propose an innovative token representation and update method in a new ultra-small language model: the Wave network.
Logic Error Localization in Student Programming Assignments Using Pseudocode and Graph Neural Networks
Additionally, we have devised a method to efficiently gather logic-error-prone programs during the syntax error correction process and compile these into a dataset that includes single and multiple line logic errors, complete with indices of the erroneous lines.
LecPrompt: A Prompt-based Approach for Logical Error Correction with CodeBERT
In this paper, we introduce LecPrompt to localize and repair logical errors, an prompt-based approach that harnesses the capabilities of CodeBERT, a transformer-based large language model trained on code.
Signal Watermark on Large Language Models
As Large Language Models (LLMs) become increasingly sophisticated, they raise significant security concerns, including the creation of fake news and academic misuse.
FreqMark: Frequency-Based Watermark for Sentence-Level Detection of LLM-Generated Text
The increasing use of Large Language Models (LLMs) for generating highly coherent and contextually relevant text introduces new risks, including misuse for unethical purposes such as disinformation or academic dishonesty.
Multi-Task Program Error Repair and Explanatory Diagnosis
And program error diagnosis can often be too abstract or technical for developers to understand, especially for beginners.
Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization
This paper proposes a Parameter-efficient prompt Tuning approach with Adaptive Optimization, i. e., FedPepTAO, to enable efficient and effective FL of LLMs.
Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation
In this paper, Intent contrastive learning with Cross Subsequences for sequential Recommendation (ICSRec) is proposed to model users' latent intentions.
Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation
In this paper, we propose a Meta-optimized Seq2Seq Generator and Contrastive Learning (Meta-SGCL) for sequential recommendation, which applies the meta-optimized two-step training strategy to adaptive generate contrastive views.
Contrastive Enhanced Slide Filter Mixer for Sequential Recommendation
Sequential recommendation (SR) aims to model user preferences by capturing behavior patterns from their item historical interaction data.
Ensemble Modeling with Contrastive Knowledge Distillation for Sequential Recommendation
Our framework adopts multiple parallel networks as an ensemble of sequence encoders and recommends items based on the output distributions of all these networks.
Sequential Recommendation with Probabilistic Logical Reasoning
To better capture the uncertainty and evolution of user tastes, SR-PLR embeds users and items with a probabilistic method and conducts probabilistic logical reasoning on users' interaction patterns.
Frequency Enhanced Hybrid Attention Network for Sequential Recommendation
However, many recent studies represent that current self-attention based models are low-pass filters and are inadequate to capture high-frequency information.
Privacy-Preserving Representation Learning for Text-Attributed Networks with Simplicial Complexes
Finally, I will study a privacy-preserving deterministic differentially private alternating direction method of multiplier to learn secure representation outputs from SNNs that capture multi-scale relationships and facilitate the passage from local structure to global invariant features on text-attributed networks.
Measuring the Privacy Leakage via Graph Reconstruction Attacks on Simplicial Neural Networks (Student Abstract)
In this paper, we measure the privacy leakage via studying whether graph representations can be inverted to recover the graph used to generate them via graph reconstruction attack (GRA).
A Roadmap to Domain Knowledge Integration in Machine Learning
Many machine learning algorithms have been developed in recent years to enhance the performance of a model in different aspects of artificial intelligence.
Class-attention Video Transformer for Engagement Intensity Prediction
In order to deal with variant-length long videos, prior works extract multi-modal features and fuse them to predict students' engagement intensity.
Contrastive Learning with Bidirectional Transformers for Sequential Recommendation
Contrastive learning with Transformer-based sequence encoder has gained predominance for sequential recommendation.
Knowledge Distillation via Weighted Ensemble of Teaching Assistants
In this research, we have shown that using multiple teaching assistant models, the student model (the smaller model) can be further improved.
Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation
To this end, in this paper, we propose a Learnable Model Augmentation self-supervised learning for sequential Recommendation (LMA4Rec).
Quaternion-Based Graph Convolution Network for Recommendation
Graph Convolution Network (GCN) has been widely applied in recommender systems for its representation learning capability on user and item embeddings.
Edge-Enhanced Global Disentangled Graph Neural Network for Sequential Recommendation
In this paper, we propose an Edge-Enhanced Global Disentangled Graph Neural Network (EGD-GNN) model to capture the relation information between items for global item representation and local user intention learning.
Coarse to Fine: Multi-label Image Classification with Global/Local Attention
Since multi-label image classification is very complicated, people seek to use the attention mechanism to guide the classification process.
Deep Cross Networks with Aesthetic Preference for Cross-domain Recommendation
Then, we integrate the aesthetic features into a cross-domain network to transfer users' domain independent aesthetic preferences.
Where to Go Next: A Spatio-temporal LSTM model for Next POI Recommendation
Furthermore, to reduce the number of parameters and improve efficiency, we further integrate coupled input and forget gates with our proposed model.
