Ling Pan

papers

206

total citations

papers (11)

Softmax Deep Double Deterministic Policy Gradients

NEURIPS 2020arXiv

118

citations

Learning to Scale Logits for Temperature-Conditional GFlowNets

ICML 2024arXiv

citations

Beyond the Destination: A Novel Benchmark for Exploration-Aware Embodied Question Answering

ICCV 2025arXiv

citations

Learning to Sample Effective and Diverse Prompts for Text-to-Image Generation

CVPR 2025arXiv

citations

Neuroplastic Expansion in Deep Reinforcement Learning

ICLR 2025arXiv

citations

E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance

NEURIPS 2022arXiv

citations

Measure gradients, not activations! Enhancing neuronal activity in deep reinforcement learning

NEURIPS 2025arXiv

citations

The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning

ICML 2025arXiv

citations

Ling Pan

papers (11)

Softmax Deep Double Deterministic Policy Gradients

Learning to Scale Logits for Temperature-Conditional GFlowNets

Beyond the Destination: A Novel Benchmark for Exploration-Aware Embodied Question Answering

Learning to Sample Effective and Diverse Prompts for Text-to-Image Generation

Neuroplastic Expansion in Deep Reinforcement Learning

E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance

Measure gradients, not activations! Enhancing neuronal activity in deep reinforcement learning

The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning

Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets

Regularized Softmax Deep Multi-Agent Q-Learning

Flow Factorization for Efficient Generative Flow Networks

papers (11)

Softmax Deep Double Deterministic Policy Gradients

Learning to Scale Logits for Temperature-Conditional GFlowNets

Beyond the Destination: A Novel Benchmark for Exploration-Aware Embodied Question Answering

Learning to Sample Effective and Diverse Prompts for Text-to-Image Generation

Neuroplastic Expansion in Deep Reinforcement Learning

E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance

Measure gradients, not activations! Enhancing neuronal activity in deep reinforcement learning

The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning

Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets

Regularized Softmax Deep Multi-Agent Q-Learning

Flow Factorization for Efficient Generative Flow Networks