Stefano Soatto
Professor
Researches computer vision and machine learning, including visual representation learning, 3D reconstruction and foundation models.
Stefano Soatto's lab at UCLA works on 3D vision & neural rendering, generative & foundation models and large language models. 51 papers since 2023, 42 at top venues, mostly CVPR and ICLR. Applications close .
At a glance
- papers, 2023–2026
- 13/ year
- at T1 venues
- 82%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom DBLP, 2023–2026
- 4 papers2026 · T1 3 · collecting
- 7 papers2025 · T1 4
- 24 papers2024 · T1 21
- 16 papers2023 · T1 14
T1 venuesOtherVenues: CVPR, ICLR, NeurIPS, ECCV, ICCV
Research focuspaper titles that use each topic's words
- Large language models19 papers
- Generative & foundation models14 papers
- 3D vision & neural rendering6 papers
- representation learning2 papers
- visual representation0 papers
Paper list
From DBLP.
20264 papers
- EntropyAI Agents as Universal Task Solvers (opens in a new tab)
- ICLR (T1)Conjuring Semantic Similarity (opens in a new tab)
- CVPR (T1)Gated KalmaNet: A Fading Memory Layer through Test-time Ridge Regression (opens in a new tab)
- CVPR (T1)Talk2Move: Reinforcement Learning for Text-Instructed Object-Level Geometric Transformation in Scenes (opens in a new tab)
20257 papers
- CDCBoiling Frogs: Generative Models, Synthetic Data, and the Opinions of their Users (opens in a new tab)
- NeuSExpansion Span: Combining Fading Memory and Retrieval in Hybrid State Space Models (opens in a new tab)
- NeuSObservability of Latent States in Generative AI Models (opens in a new tab)
- ICLR (T1)PICASO: Permutation-Invariant Context Composition with State Space Models (opens in a new tab)
- ICRA (T1)Robust Planning for Autonomous Driving via Mixed Adversarial Diffusion Predictions (opens in a new tab)
- CVPR (T1)Scaling up Image Segmentation across Data and Tasks (opens in a new tab)
- NeurIPS (T1)STree: Speculative Tree Decoding for Hybrid State Space Models (opens in a new tab)
202424 papers
- ECCV (T1)AugUndo: Scaling Up Augmentations for Monocular Depth Completion and Estimation (opens in a new tab)
- NeurIPS (T1)B'MOJO: Hybrid State Space Realizations of Foundation Models with Eidetic and Fading Memory (opens in a new tab)
- CVPR (T1)CPR: Retrieval Augmented Generation for Copyright Protection (opens in a new tab)
- ICLR (T1)Critical Learning Periods Emerge Even in Deep Linear Networks (opens in a new tab)
- CVPR (T1)Diffeomorphic Template Registration for Atmospheric Turbulence Mitigation (opens in a new tab)
- ECCV (T1)Diffusion Soup: Model Merging for Text-to-Image Diffusion Models (opens in a new tab)
- EMNLP (T1)DocKD: Knowledge Distillation from LLMs for Open-World Document Understanding Models (opens in a new tab)
- TPAMI (T1)Elodi: Ensemble Logit Difference Inhibition for Positive-Congruent Training (opens in a new tab)
- CVPR (T1)Enhancing Vision-Language Pre-Training with Rich Supervisions (opens in a new tab)
- ICML (T1)Fewer Truncations Improve Language Modeling (opens in a new tab)
- CDCHeat Death of Generative Models in Closed-Loop Learning (opens in a new tab)
- CVPR (T1)Interpretable Measures of Conceptual Similarity by Complexity-Constrained Descriptive Auto-Encoding (opens in a new tab)
202316 papers
- CVPR (T1)A Meta-Learning Approach to Predicting Performance and Data Requirements (opens in a new tab)
- CVPR (T1)À-la-carte Prompt Tuning (APT): Combining Distinct Data Via Composable Prompting (opens in a new tab)
- CVPR (T1)Critical Learning Periods for Multisensory Integration in Deep Networks (opens in a new tab)
- CVPR (T1)Depth Estimation from Camera Image and mmWave Radar Point Cloud (opens in a new tab)
- NeurIPS (T1)Gacs-Korner Common Information Variational Autoencoder (opens in a new tab)
- AISTATS (T1)Graph Spectral Embedding using the Geodesic Betweenness Centrality (opens in a new tab)
- CVPR (T1)Guided Recommendation for Model Fine-Tuning (opens in a new tab)
- WACVHarnessing Unrecognizable Faces for Improving Face Recognition (opens in a new tab)
- Nat. Mac. Intell.Incorporating physics into data-driven computer vision (opens in a new tab)
- NeurIPS (T1)Leveraging sparse and shared feature activations for disentangled representation learning (opens in a new tab)
- ICCV (T1)Linear Spaces of Meanings: Compositional Structures in Vision-Language Models (opens in a new tab)
- ICLR (T1)Masked Vision and Language Modeling for Multi-modal Representation Learning (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to UCLAFall 2027
Computer Science PhD
- Round
- Single round (Dec 15)
- Opens
- —
- GRE
- optional
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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- SGNTU
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Data
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- DBLP
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