New Framework
Recent research focuses on developing versatile frameworks for various tasks, primarily aiming to improve efficiency, reproducibility, and accessibility within their respective domains. These frameworks leverage diverse techniques, including programmatic data generation for LLMs, deep learning architectures for image and audio processing, and reinforcement learning for optimization and automated testing. The resulting advancements enhance the development and evaluation of AI models, improve the reliability of benchmarking processes, and offer new tools for diverse applications ranging from healthcare diagnostics to autonomous vehicle navigation.
Papers
DualBind: A Dual-Loss Framework for Protein-Ligand Binding Affinity Prediction
Meng Liu, Saee Gopal Paliwal
Watching Swarm Dynamics from Above: A Framework for Advanced Object Tracking in Drone Videos
Duc Pham, Matthew Hansen, Félicie Dhellemmes, Jens Krause, Pia Bideau
A Framework for Efficient Model Evaluation through Stratification, Sampling, and Estimation
Riccardo Fogliato, Pratik Patil, Mathew Monfort, Pietro Perona
CHARME: A chain-based reinforcement learning approach for the minor embedding problem
Hoang M. Ngo, Nguyen H K. Do, Minh N. Vu, Tamer Kahveci, My T. Thai
TopoBenchmarkX: A Framework for Benchmarking Topological Deep Learning
Lev Telyatnikov, Guillermo Bernardez, Marco Montagna, Pavlo Vasylenko, Ghada Zamzmi, Mustafa Hajij, Michael T Schaub, Nina Miolane, Simone Scardapane, Theodore Papamarkou
A Review of Prominent Paradigms for LLM-Based Agents: Tool Use (Including RAG), Planning, and Feedback Learning
Xinzhe Li
TR2MTL: LLM based framework for Metric Temporal Logic Formalization of Traffic Rules
Kumar Manas, Stefan Zwicklbauer, Adrian Paschke
Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent Parametric Partial Differential Equations
Jan Hagnberger, Marimuthu Kalimuthu, Daniel Musekamp, Mathias Niepert
VHDL-Eval: A Framework for Evaluating Large Language Models in VHDL Code Generation
Prashanth Vijayaraghavan, Luyao Shi, Stefano Ambrogio, Charles Mackin, Apoorva Nitsure, David Beymer, Ehsan Degan
A Framework for Spatio-Temporal Graph Analytics In Field Sports
Valerio Antonini, Michael Scriney, Alessandra Mileo, Mark Roantree
ToxVidLM: A Multimodal Framework for Toxicity Detection in Code-Mixed Videos
Krishanu Maity, A. S. Poornash, Sriparna Saha, Pushpak Bhattacharyya
Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases
Geng Sun, Wenwen Xie, Dusit Niyato, Fang Mei, Jiawen Kang, Hongyang Du, Shiwen Mao