Many of the recent advancements in AI are due to exploiting * structured low-rank representations*, from low-rank tensor factorizations (Kolda and Bader 2009; Kossaifi et al. 2019) for scaling large language models (LLMs), e.g., via adapters (Hu et al. 2021) or structured matrices
(Dao et al. 2022); the diffusion of compact polynomial representations as powerful inductive biases for deep learning architectures (Cheng et al. 2024); the emergence of probabilistic circuits to provide tractable probabilistic inference with guarantees (Loconte et al. 2024; Choi et al. 2020) and reliable neuro-symbolic AI (Ahmed et al. 2022); and the wide application of tensor networks to solve and accelerate physics-related problems (Biamonte and Bergholm 2017) and quantum computing (Orus 2019).

*“How are all these representations related to each others? and how can we transfer knowledge across communities?”*

We will try to answer the above questions in our day workshop at the Thirty-Ninth AAAI Conference on Artificial Intelligence (**AAAI-25**). The workshop will be held at the Pennsylvania Convention Center in Philadelphia, Pennsylvania, USA, March 3-4, 2025.

See our call for papers.

✨ Check out also the Tutorial on **tensor factorizations and probabilistic circuits** also at AAAI-25! ✨

# News

**[15th Oct 2024]**Nadav, Guillame, Yannis and Andrew confirmed to be speakers!**[25th Sep 2024]**Openreview is open to receive submissions!**[20th Sep 2024]**Website is on!

# Speakers

*NYU*

# Organizers

*UCLA*

# Recommended reading

- Kolda and Bader 2009 - Tensor decompositions and applications
- Hu et al. 2021 - LoRA: Low-Rank Adaptation of Large Language Models
- Loconte et al. 2024 - What is the Relationship between Tensor Factorizations and Circuits (and How Can We Exploit it)?
- Dao et al. 2022 - Monarch: Expressive Structured Matrices for Efficient and Accurate Training
- Kossaifi et al. 2019 - Tensorly: Tensor learning in python
- Cheng et al. 2024 - Multilinear Operator Networks
- Choi et al. 2020 - Probabilistic Circuits: A Unifying Framework for Tractable Probabilistic Models
- Ahmed et al. 2022 - Semantic Probabilistic Layers for Neuro-Symbolic Learning
- Biamonte and Bergholm 2017 - Tensor Networks in a Nutshell
- Orus 2019 - Tensor networks for complex quantum systems

Last build date: 2024-11-14.