Xuefei Ning
Assistant Professor
Works on efficient deep learning, including model compression and efficient generative model inference.
Xuefei Ning's lab at Tsinghua works on efficient training & inference and generative & foundation models. 27 papers since 2023, 21 at top venues, mostly ICLR and AAAI. 3 PhD students and 4 master's students. 4 alumni. Applications close .
At a glance
- papers, 2023–2026
- 7/ year
- at T1 venues
- 78%
- PhD students
- 3
- MS students
- 4
- alumni listed
- 4
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 5 papers2025 · T1 5
- 13 papers2024 · T1 9
- 9 papers2023 · T1 7
T1 venuesOtherVenues: ICLR, AAAI, NeurIPS, ICML, CVPR
Research focuspaper titles that use each topic's words
- Efficient training & inference13 papers
- Generative & foundation models9 papers
- efficient9 papers
- model4 papers
- model compression1 paper
Paper list
From the lab homepage.
20255 papers
- ICLR (T1)Accelerating Auto-regressive Text-to-Image Generation with Training-free Speculative Jacobi Decoding
- ICLR (T1)Distilling Auto-regressive Models into Few Steps 1: Image Generation
- ICLR (T1)Linear Combination of Saved Checkpoints Makes Consistency and Diffusion Models Better
- AAAI (T1)Training-Free and Hardware-Friendly Acceleration for Diffusion Models via Similarity-based Token Pruning
- ICLR (T1)ViDiT-Q: Efficient and Accurate Quantization of Diffusion Transformers for Image and Video Generation
202413 papers
- Publishing House of Electronics Industry(Chinese Book) Efficient Deep Learning: Model Compression and Design
- ICLR (T1)A Unified Sampling Framework for Solver Searching of Diffusion Probabilistic Models
- NeurIPS (T1)Can LLMs Learn by Teaching for Better Reasoning?
- NeurIPS (T1)DiTFastAttn: Attention Compression for Diffusion Transformer Models
- DATEDyPIM: Dynamic-inference-enabled Processing-In-Memory Accelerator
- ICML (T1)Evaluating Quantized Large Language Models
- CVPR (T1)FlashEval: Towards Fast and Accurate Evaluation of Text-to-image Diffusion Generative Models
- FPGAFlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs
- ECCV (T1)MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization
- NeurIPS (T1)Rad-NeRF: Ray-decoupled Training of Neural Radiance Field
- ICLR (T1)Skeleton-of-Thought: Prompting Large Language Models for Efficient Parallel Generation
- WACVTCP: Triplet Contrastive-relationship Preserving for Class-Incremental Learning
20239 papers
- TPAMI (T1)A Generic Graph-based Neural Architecture Encoding Scheme with Multifaceted Information
- ICCV (T1)Ada3D: Exploiting the Spatial Redundancy with Adaptive Inference for Efficient 3D Object Detection
- AAAI (T1)Cold-Start
- AAAI (T1)Ensemble-in-One: Ensemble Learning within Random Gated Networks for Enhanced Adversarial Robustness
- DATE & TCADGibbon: Efficient Co-Exploration of NN Model and Processing-In-Memory Architecture
- NeurIPS (T1)Jaccard Metric Losses: Optimizing the Jaccard Index with Soft Labels
- NeurIPS WorkshopLLM-MQ: Mixed-precision Quantization for Efficient LLM Deployment
- AAAI (T1)Memory-Oriented Structural Pruning for Efficient Image Restoration
- ICML (T1)OMS-DPM: Deciding The Optimal Model Schedule for Diffusion Probabilistic Model
Where alumni went4 listed
Apply to TsinghuaFall 2027
Computer Science PhD (international applicants)
- Round
- Main
- Opens
- GRE
- See program page
- English
- Per school catalog
- Fee
- RMB 800
Not yet confirmed for this intake.
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