refine
Approximating the Permanent by Sampling from Adaptive Partitions
Jonathan Kuck, Tri Dao, Hamid Rezatofighi, Ashish Sabharwal, Stefano Ermon
Neural Information Processing Systems http://nips.cc/
- North America > United States > California > Santa Clara County > Palo Alto (0.04)
- North America > Canada (0.04)
- Asia > Middle East > Jordan (0.04)
- Asia > India > West Bengal > Kolkata (0.04)
DeepUSPS: Deep Robust Unsupervised Saliency Prediction via Self-supervision
Tam Nguyen, Maximilian Dax, Chaithanya Kumar Mummadi, Nhung Ngo, Thi Hoai Phuong Nguyen, Zhongyu Lou, Thomas Brox
Neural Information Processing Systems http://nips.cc/
- North America > Canada (0.04)
- Europe > Germany > Baden-Württemberg > Karlsruhe Region > Karlsruhe (0.04)
- Europe > Germany > Baden-Württemberg > Freiburg (0.04)
- Health & Medicine > Pharmaceuticals & Biotechnology (1.00)
- Health & Medicine > Therapeutic Area > Endocrinology > Diabetes (0.68)
AdaPlanner: Adaptive Planning from Feedback with Language Models
Large language models (LLMs) have recently demonstrated the potential in acting as autonomous agents for sequential decision-making tasks. However, most existing methods either take actions greedily without planning or rely on static plans that are not adaptable to environmental feedback. Consequently, the sequential decision-making performance of LLM agents degenerates with problem complexity and plan horizons increase. We propose a closed-loop approach, AdaPlanner, which allows the LLM agent to refine its self-generated plan adaptively in response to environmental feedback.