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Found 9 papers, 3 papers with code
PokerBench: Training Large Language Models to become Professional Poker Players
PokerBench thus presents a unique benchmark for a quick and reliable evaluation of the poker-playing ability of LLMs as well as a comprehensive benchmark to study the progress of LLMs in complex game-playing scenarios.
CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching
Anomaly detection in multivariate time series is challenging as heterogeneous subsequence anomalies may occur.
AI-Generated Text Detection and Classification Based on BERT Deep Learning Algorithm
In conclusion, the AI-generated text detection model based on the BERT algorithm proposed in this study shows high accuracy and stability in experiments, providing an effective solution for related fields.
AlphaMapleSAT: An MCTS-based Cube-and-Conquer SAT Solver for Hard Combinatorial Problems
We perform an extensive comparison of AlphaMapleSAT against the March CnC solver on challenging combinatorial problems such as the minimum Kochen-Specker and Ramsey problems.
CTAGE: Curvature-Based Topology-Aware Graph Embedding for Learning Molecular Representations
Therefore, it is necessary to develop a method for predicting molecular properties that effectively combines spatial structural information while maintaining the simplicity and efficiency of graph neural networks.
Multi-scale Geometry-aware Transformer for 3D Point Cloud Classification
At first, the MGT divides point cloud data into patches with multiple scales.
RMMDet: Road-Side Multitype and Multigroup Sensor Detection System for Autonomous Driving
Then we implement muti-type sensor detection and multi-group sensors fusion in this environment, including camera-radar and camera-lidar detection based on result-level fusion.
A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifold
Although Deep Learning (DL) has achieved success in complex Artificial Intelligence (AI) tasks, it suffers from various notorious problems (e. g., feature redundancy, and vanishing or exploding gradients), since updating parameters in Euclidean space cannot fully exploit the geometric structure of the solution space.
