Search Results for author:
Found 8 papers, 2 papers with code
Efficient and Scalable Fine-Tune of Language Models for Genome Understanding
Impressively, applying these adapters on natural language foundation models matched or even exceeded the performance of DNA foundation models.
ProPath: Disease-Specific Protein Language Model for Variant Pathogenicity
Clinical variant classification of pathogenic versus benign genetic variants remains a pivotal challenge in clinical genetics.
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).
Human-guided Robot Behavior Learning: A GAN-assisted Preference-based Reinforcement Learning Approach
To reduce and minimize the need for human queries, we propose a new GAN-assisted human preference-based reinforcement learning approach that uses a generative adversarial network (GAN) to actively learn human preferences and then replace the role of human in assigning preferences.
Deep Model Compression Via Two-Stage Deep Reinforcement Learning
Besides accuracy, the model size of convolutional neural networks (CNN) models is another important factor considering limited hardware resources in practical applications.
Relationship Explainable Multi-objective Optimization Via Vector Value Function Based Reinforcement Learning
Solving multi-objective optimization problems is important in various applications where users are interested in obtaining optimal policies subject to multiple, yet often conflicting objectives.
Relationship Explainable Multi-objective Reinforcement Learning with Semantic Explainability Generation
We demonstrate the effectiveness of the proposed approach via a MuJoCo based robotics case study.
Multi-Objective Reinforcement Learning
reinforcement-learning
