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Gen Li

Topic trends: 31,945 papers · similarity ≥ 0.4 · year ≥ 2024 · Data sourced from Semantic Scholar

34,180 papers | Abstracts: 30,725 (89.9%) | Citations: 34,180 (100.0%) | arXiv: 25,721 (75.3%)

Built: Feb 4, 2026, 7:47 AM AMS

23

papers

86

total citations

papers (23)

Phantom: Subject-Consistent Video Generation via Cross-Modal Alignment

EgoGen: An Egocentric Synthetic Data Generator

EgoM2P: Egocentric Multimodal Multitask Pretraining

VolumetricSMPL: A Neural Volumetric Body Model for Efficient Interactions, Contacts, and Collisions

Removing Interference and Recovering Content Imaginatively for Visible Watermark Removal

One-Shot Open Affordance Learning with Foundation Models

Advancing Dynamic Sparse Training by Exploring Optimization Opportunities

Accelerating Convergence of Score-Based Diffusion Models, Provably

Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity

Adaptive Prototype Learning and Allocation for Few-Shot Segmentation

Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting

LOCATE: Localize and Transfer Object Parts for Weakly Supervised Affordance Grounding

OSRT: Omnidirectional Image Super-Resolution With Distortion-Aware Transformer

VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder

Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction

NeurIPS 2020arXiv

Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model

NeurIPS 2020arXiv

Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting

NeurIPS 2021arXiv

Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning

NeurIPS 2021arXiv

Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model

NeurIPS 2022arXiv

Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning

NeurIPS 2023arXiv

Dynamic Sparsity Is Channel-Level Sparsity Learner

NeurIPS 2023arXiv

The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model

NeurIPS 2023arXiv

Regret-Optimal Model-Free Reinforcement Learning for Discounted MDPs with Short Burn-In Time

NeurIPS 2023arXiv