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Found 5 papers, 2 papers with code
DrHouse: An LLM-empowered Diagnostic Reasoning System through Harnessing Outcomes from Sensor Data and Expert Knowledge
Large language models (LLMs) have the potential to transform digital healthcare, as evidenced by recent advances in LLM-based virtual doctors.
Dual Adversarial Alignment for Realistic Support-Query Shift Few-shot Learning
The key feature of RSQS is that the individual samples in a meta-task are subjected to multiple distribution shifts in each meta-task.
PGADA: Perturbation-Guided Adversarial Alignment for Few-shot Learning Under the Support-Query Shift
Few-shot learning methods aim to embed the data to a low-dimensional embedding space and then classify the unseen query data to the seen support set.
Revisiting the Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning
QMIX, a popular MARL algorithm based on the monotonicity constraint, has been used as a baseline for the benchmark environments, such as Starcraft Multi-Agent Challenge (SMAC), Predator-Prey (PP).
Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement Learning
Multi-Agent Reinforcement Learning (MARL) has seen revolutionary breakthroughs with its successful application to multi-agent cooperative tasks such as computer games and robot swarms.
