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Variational Flow Matching goes Riemannian! 🔮 In this preliminary work, we derive a variational objective for probability flows 🌀 on manifolds with closed-form geodesics. My dream team: Floor Eijkelboom Alison Erik Bekkers 💥 📜 arxiv.org/abs/2502.12981 🧵1/5

1\ Hi, can I get an unsupervised sparse autoencoder for steering, please? I only have unlabeled data varying across multiple unknown concepts. Oh, and make sure it learns the same features each time! Yes! A freshly brewed Sparse Shift Autoencoder (SSAE) coming right up. 🧶

We are going to present our poster "Sum of Squares Circuits" at AAAI in Philadelphia today Hall E 12:30pm-14:00pm poster #840 We trace expressiveness connections of different types of additive and subtractive deep mixture models and tensor networks 📜 arxiv.org/abs/2408.11778

🧵 New paper! We explore sparse coding, superposition, and the Linear Representation Hypothesis (LRH) through identifiability theory, compressed sensing, and interpretability research. If neural representations intrigue you, read on! 🤓 arxiv.org/abs/2503.01824

On my way to Mai Khao 🇹🇭🏝️ for #AISTATS2025, where I will present A Causal Framework for Evaluating Deferring Systems, a joint work with Filippo Palomba, Jose M. Alvarez and Salvatore Ruggieri. 1/5

Now in Thailand to present our paper at #AISTATS2025 🇹🇭🌴 📍Today at 3:00-6:00 pm, poster number 118! More details here: openreview.net/forum?id=XCmIl…

Neural networks implicitly define a latent vector field on the data manifold, via autoencoding iterations🌀 This representation retains properties of the model, revealing memorization and generalization regimes, and characterizing distribution shifts 📜: arxiv.org/abs/2505.22785

🗑️Garbage In, #Reasoning Out? High scores🎢 for reasoning in #LLMs? BUT! What if your fav benchmark is just... BROKEN? We audit popular reasoning benchmarks & studied how robust LLM reasoning is wrt input uncertainty. 📄 Paper: arxiv.org/abs/2506.23864

Is gravity quantum? Hopefully, it will be the BMV experiment to reveal that ⚛️