Yann LeCun
Jacob T. Schwartz Chaired Professor in Computer Science
Works on deep learning and self-supervised learning, including convolutional networks, energy-based models and joint-embedding world models.
Yann LeCun's lab at NYU works on generative & foundation models. 66 papers since 2023, 51 at top venues, mostly ICLR and ICML. Applications close .
At a glance
- papers, 2023–2026
- 17/ year
- at T1 venues
- 77%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom DBLP, 2023–2026
- 11 papers2026 · T1 7 · collecting
- 20 papers2025 · T1 19
- 16 papers2024 · T1 11
- 19 papers2023 · T1 14
T1 venuesOtherVenues: ICLR, ICML, NeurIPS, CVPR, ICCV
Research focuspaper titles that use each topic's words
- self-supervised14 papers
- Generative & foundation models13 papers
- self-supervised learning12 papers
- world5 papers
- world models4 papers
Paper list
From DBLP.
202611 papers
- Commun. ACMAnnouncing the Shaw Prize in Computer Science (opens in a new tab)
- ICLR (T1)Attention Sinks and Compression Valleys in LLMs are Two Sides of the Same Coin (opens in a new tab)
- ICLR (T1)Cambrian-S: Towards Spatial Supersensing in Video (opens in a new tab)
- ICLR (T1)From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning (opens in a new tab)
- ICLR (T1)LLM-JEPA: Large Language Models Meet Joint Embedding Predictive Architectures (opens in a new tab)
- IEEE Signal Process. Mag.Spectral Graph Theory: The mathematics of self-supervised learning [Special Issue on the Mathematics of Deep Learning] (opens in a new tab)
- ACL (T1)SpidR-Adapt: A Universal Speech Representation Model for Few-Shot Adaptation (opens in a new tab)
- Commun. ACMUnpacking Open Source Artificial Intelligence: Toward a Framework for Openness in Foundation Models (opens in a new tab)
- ECCV (T1)V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning (opens in a new tab)
- ICLR (T1)VL-JEPA: Joint Embedding Predictive Architecture for Vision-language (opens in a new tab)
- Trans. Mach. Learn. Res.What Drives Success in Physical Planning with Joint-Embedding Predictive World Models? (opens in a new tab)
202520 papers
- ICML (T1)DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning (opens in a new tab)
- Findings of ACLForgotten Polygons: Multimodal Large Language Models are Shape-Blind (opens in a new tab)
- ICLR (T1)Hierarchical World Models as Visual Whole-Body Humanoid Controllers (opens in a new tab)
- AISTATS (T1)Improving Pre-trained Self-Supervised Embeddings Through Effective Entropy Maximization (opens in a new tab)
- ICML (T1)Layer by Layer: Uncovering Hidden Representations in Language Models (opens in a new tab)
- NeurIPS (T1)Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models (opens in a new tab)
- ICLR (T1)LiveBench: A Challenging, Contamination-Limited LLM Benchmark (opens in a new tab)
- ICCV (T1)MetaMorph: Multimodal Understanding and Generation via Instruction Tuning (opens in a new tab)
- CVPR (T1)Navigation World Models (opens in a new tab)
- NeurIPS (T1)OSVI-WM: One-Shot Visual Imitation for Unseen Tasks using World-Model-Guided Trajectory Generation (opens in a new tab)
- ICLR (T1)PooDLe🐩: Pooled and dense self-supervised learning from naturalistic videos (opens in a new tab)
- CVPR (T1)Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation (opens in a new tab)
202416 papers
- Trans. Mach. Learn. Res.Blockwise Self-Supervised Learning at Scale (opens in a new tab)
- NeurIPS (T1)Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs (opens in a new tab)
- TPAMI (T1)Editorial: Learning With Fewer Labels in Computer Vision (opens in a new tab)
- ECCV (T1)EgoPet: Egomotion and Interaction Data from an Animal's Perspective (opens in a new tab)
- CVPR (T1)Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs (opens in a new tab)
- NeurIPS (T1)Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning (opens in a new tab)
- NeurIPS (T1)G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering (opens in a new tab)
- ICLR (T1)GAIA: a benchmark for General AI Assistants (opens in a new tab)
- ICLR (T1)Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning (opens in a new tab)
- ICML (T1)How Learning by Reconstruction Produces Uninformative Features For Perception (opens in a new tab)
- RLJLight-weight Probing of Unsupervised Representations for Reinforcement Learning (opens in a new tab)
- Trans. Mach. Learn. Res.Revisiting Feature Prediction for Learning Visual Representations from Video (opens in a new tab)
202319 papers
- ICML (T1)A Generalization of ViT/MLP-Mixer to Graphs (opens in a new tab)
- ICCV (T1)Active Self-Supervised Learning: A Few Low-Cost Relationships Are All You Need (opens in a new tab)
- CVPR WorkshopAdapting Grounded Visual Question Answering Models to Low Resource Languages (opens in a new tab)
- NeurIPS (T1)An Information Theory Perspective on Variance-Invariance-Covariance Regularization (opens in a new tab)
- Trans. Mach. Learn. Res.Augmented Language Models: a Survey (opens in a new tab)
- Trans. Mach. Learn. Res.Bag of Image Patch Embedding Behind the Success of Self-Supervised Learning (opens in a new tab)
- WACVCompact and Optimal Deep Learning with Recurrent Parameter Generators (opens in a new tab)
- ICASSP (T1)Fast and Exact Enumeration of Deep Networks Partitions Regions (opens in a new tab)
- Artificial Intelligence (T1)Language, common sense, and the Winograd schema challenge (opens in a new tab)
- ICLR (T1)Minimalistic Unsupervised Representation Learning with the Sparse Manifold Transform (opens in a new tab)
- ICLR (T1)On the duality between contrastive and non-contrastive self-supervised learning (opens in a new tab)
- ICASSP (T1)Police: Provably Optimal Linear Constraint Enforcement For Deep Neural Networks (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to NYUFall 2027
Computer Science PhD (Courant / Tandon)
- Round
- Single round (December)
- Opens
- —
- GRE
- See program page
- English
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- Fee
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Not yet confirmed for this intake.
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