Yu Cheng
Nanyang Associate Professor (NRF)
Works on efficient and sparse model architectures, model compression and multimodal learning.
Yu Cheng's lab at NTU works on efficient training & inference, vision-language & multimodal and accelerators, chips & architecture. 118 papers since 2023, 105 at top venues, mostly ACL and ICML. 42 PhD students.
At a glance
- papers, 2023–2026
- 31/ year
- at T1 venues
- 89%
- PhD students
- 42
- MS students
- 0
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 41 papers2026 · T1 37 · collecting
- 38 papers2025 · T1 36
- 27 papers2024 · T1 23
- 12 papers2023 · T1 9
T1 venuesOtherVenues: ACL, ICML, ICLR, EMNLP, NeurIPS
Research focuspaper titles that use each topic's words
- Vision-language & multimodal37 papers
- Efficient training & inference17 papers
- model3 papers
- Accelerators, chips & architecture0 papers
- model architectures0 papers
Paper list
From the lab homepage.
202641 papers
- COLMA Probabilistic Perspective on the Emergence of Reasoning in RLVR
- ICLR (T1)AdaReasoner: Dynamic Tool Orchestration for Iterative Visual Reasoning
- WACVBREEN: Bridge Data-Efficient Encoder-Free Multimodal Learning with Learnable Queries
- ICML (T1)Characterizing, Evaluating, and Optimizing Complex Reasoning
- ICLR (T1)Conditional Advantage Estimation for Reinforcement Learning in Large Reasoning Models
- ACL (T1)CoTEvol: Self-Evolving Chain-of-Thoughts for Data Synthesis in Mathematical Reasoning
- EMNLP (T1)CPSD: Counterfactual Patches Self-Distillation in RLVR
- BMVCCross-Task Generalization Between Understanding and Generation in Unified Vision-Language Models: A Controlled Study
- ICML (T1)DiffThinker: Towards Generative Multimodal Reasoning with Diffusion Models
- ICLR (T1)DiffuGuard: How Intrinsic Safety is Lost and Found in Diffusion Large Language Models
- ICLR (T1)Diversity-Incentivized Exploration for Versatile Reasoning
- EMNLP (T1)Draft-OPD: On-Policy Distillation for Speculative Draft Models Download
202538 papers
- NeurIPS (T1)$\pi$-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows
- EMNLP (T1)Bit-Flip Error Resilience in LLMs: A Comprehensive Analysis and Defense Framework
- EMNLP (T1)CLIP-MoE: Towards Building Mixture of Experts for CLIP with Diversified Multiplet Upcycling
- NAACL (T1)Continuous Speech Tokenizer in Text-to-speech
- ICML (T1)Divide and Conquer: Grounding LLMs as Efficient Decision-Making Agents via Offline Hierarchical Reinforcement Learning
- ICML (T1)Diving into Self-Evolve Training for Multimodal Reasoning
- EMNLP (T1)Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs
- NAACL (T1)Dynamic Data Mixing Maximizes Instruction Tuning for Mixture-of-Experts
- ICML (T1)EMMA: An Enhanced MultiModal ReAsoning Benchmark
- CVPR (T1)Extrapolating and Decoupling Image-to-Video Generation Models: Motion Modeling is Easier Than You Think
- CVPR (T1)From Head to Tail: Towards Balanced Representation in Large Vision-Language Models through Adaptive Data Calibration
- EMNLP (T1)Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models
202427 papers
- ACL (T1)Confidence is not Timeless: Modeling Temporal Validity for Rule-based Temporal Knowledge Graph Forecasting
- NeurIPS (T1)Conflictbank: A Benchmark for Evaluating the Influence of Knowledge Conflicts in LLM
- AAAI (T1)Enhancing Low-Resource Relation Representations through Multi-View Decoupling
- NAACL (T1)GRILL: Grounded Vision-language Pre-training via Aligning Text and Image Regions
- ECCV (T1)Learning the Unlearned: Mitigating Feature Suppression in Contrastive Learning
- ICML (T1)LIDAO: Towards Limited Interventions for Debiasing (Large) Language Models
- ACL (T1)Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning?
- EMNLP (T1)LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-Training
- NeurIPS (T1)MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution
- ICLR (T1)Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy
- ACL (T1)Mitigating Boundry Ambiguity and Inherent Bias for Text Classification in the Era of Large Language Models
- EMNLP (T1)MoE-I^2: Compressing Mixture of Experts Models through Inter-Expert Pruning and Intra-Expert Low-Rank Decomposition
202312 papers
- ICLR (T1)Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning
- EMNLP (T1)Annotations Are Not All You Need: A Cross-modal Knowledge Transfer Network for Unsupervised Temporal Sentence Grounding
- ACL (T1)DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models
- ACMMMFilling the Information Gap between Video and Query for Language-Driven Moment Retrieval
- AAAI (T1)Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesis
- AAAI (T1)Hypotheses Tree Building for One-Shot Temporal Sentence Localization
- ACL (T1)Local Byte Fusion for Neural Machine Translation
- ICML (T1)Low-Switching Policy Gradient with Exploration via Online Sensitivity Sampling
- —Outstanding Paper Award
- CVPR (T1)Transform-Equivariant Consistency Learning for Temporal Sentence Grounding
- EACLWhat do Compressed Large Language Models Forget?
- CVPR (T1)You Are Catching My Attention: Are Vision Transformers Bad Learners Under Backdoor Attacks?
Where alumni went
Alumni are not listed on the lab homepage.
Apply to NTUFall 2027
Not announced yet
Computing and Data Science PhD
- Round
- Main
- Opens
- —
- GRE
- not required
- English
- TOEFL/IELTS
- Fee
- See program page
Not yet confirmed for this intake.
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