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Found 11 papers, 1 papers with code
Fast OMP for Exact Recovery and Sparse Approximation
This paper advances OMP in two fronts: it offers a fast algorithm for the orthogonal projection of the input signal at each iteration, and a new selection criterion for making the greedy choice, which reduces the number of iterations it takes to recover the signal.
Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices
Large language models (LLMs) have significantly transformed the landscape of Natural Language Processing (NLP).
Decoding the AI Pen: Techniques and Challenges in Detecting AI-Generated Text
Large Language Models (LLMs) have revolutionized the field of Natural Language Generation (NLG) by demonstrating an impressive ability to generate human-like text.
UAV Swarm Deployment and Trajectory for 3D Area Coverage via Reinforcement Learning
Unmanned aerial vehicles (UAVs) are recognized as promising technologies for area coverage due to the flexibility and adaptability.
Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning
In practice, a set of formulaic alphas is often used together for better modeling precision, so we need to find synergistic formulaic alpha sets that work well together.
Style Miner: Find Significant and Stable Explanatory Factors in Time Series with Constrained Reinforcement Learning
In high-dimensional time-series analysis, it is essential to have a set of key factors (namely, the style factors) that explain the change of the observed variable.
Learn Continuously, Act Discretely: Hybrid Action-Space Reinforcement Learning For Optimal Execution
On the one hand, the continuous action space using percentage changes in prices is preferred for generalization.
Rethinking Pareto Approaches in Constrained Reinforcement Learning
Constrained Reinforcement Learning (CRL) burgeons broad interest in recent years, which pursues both goals of maximizing long-term returns and constraining costs.
Trust the Model When It Is Confident: Masked Model-based Actor-Critic
In complex and noisy settings, model-based RL tends to have trouble using the model if it does not know when to trust the model.
Efficient and Adaptive Kernelization for Nonlinear Max-margin Multi-view Learning
Existing multi-view learning methods based on kernel function either require the user to select and tune a single predefined kernel or have to compute and store many Gram matrices to perform multiple kernel learning.
Learning beyond Predefined Label Space via Bayesian Nonparametric Topic Modelling
In real world machine learning applications, testing data may contain some meaningful new categories that have not been seen in labeled training data.
