News

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Teaser for Rethinking Sparse Autoencoders: Select-and-Project for Fairness and Control from Encoder Features Alone
Academic Natural Language Processing

Rethinking Sparse Autoencoders: Select-and-Project for Fairness and Control from Encoder Features Alone

Antonio Bărbălau, Cristian Daniel Păduraru, Teodor Poncu, Alexandru Tifrea, Elena Burceanu
Accepted at NeurIPS 2025 Workshops: Mechanistic Interpretability; Reliable ML from Unreliable Data (Poster) Dec 2025
Links: arXiv Abstract Sparse Autoencoders (SAEs) have proven valuable due to their ability to provide interpretable and steerable representations. …
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Teaser for Learning (Approximately) Equivariant Networks via Constrained Optimization
Academic Generalization & Interpretability

Learning (Approximately) Equivariant Networks via Constrained Optimization

Andrei Manolache, Luiz F.O. Chamon, Mathias Niepert
Accepted at NeurIPS 2025 (Oral, top 0.4%) Dec 2025
Links: arXiv · GitHub Abstract Equivariant neural networks are designed to respect symmetries through their architecture, boosting generalization and …
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Patent

Systems and Methods of Detecting Bias in AI Training Data

rfilipescu, ext-abarbalau, eburceanu
Nov 2025
Docket: BTD-2414-C1 · US application: 19/378,395 · PTO filed: Nov 4, 2025 · Status: Filed · Technologies: Machine learning Continuation patent …
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Patent

Systems and Methods of Identifying Spurious Correlations in AI Training Data

rfilipescu, ext-abarbalau, eburceanu
Nov 2025
Docket: BTD-2414 · US application: 19/378,391 · PTO filed: Nov 4, 2025 · Status: Filed · Technologies: Machine learning Patent application on …
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Patent

Systems and Methods of Training Deepfake Detectors

eoneata, ssmeu
Sep 2025
Docket: BTD-2511 · PTO filed: Sep 30, 2025 · Status: Filed · Technologies: Machine learning US patent application on training deepfake detectors.
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Teaser for Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining
Academic Natural Language Processing

Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining

Boshra Ariguib, Mathias Niepert, Andrei Manolache
Under review Sep 2025
Links: arXiv GitHub Abstract High-quality molecular representations are essential for property prediction and molecular design, yet large labeled …
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News

NeurIPS Associate Program Chair

Sep 2025
A team member served as NeurIPS Associate Program Chair (Associate PC), helping shape the conference’s review process and paper selection.
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News

CVPR Daily interview

Jun 2025
Our group was featured in a CVPR Daily interview during CVPR 2025. The interview can be found here
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Teaser for Circumventing shortcuts in audio-visual deepfake detection datasets with unsupervised learning
Academic Deepfake Detection

Circumventing shortcuts in audio-visual deepfake detection datasets with unsupervised learning

Stefan Smeu, Dragos-Alexandru Boldisor, Dan Oneata, Elisabeta Oneata
Accepted at CVPR 2025 (Highlight, top 3%) Jun 2025
Links: arXiv · GitHub Abstract Good datasets are essential for developing and benchmarking any machine learning system. Their importance is even more …
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Teaser for Do We Always Need the Simplicity Bias? Looking for Optimal Inductive Biases in the Wild
Academic Generalization & Interpretability

Do We Always Need the Simplicity Bias? Looking for Optimal Inductive Biases in the Wild

Damien Teney, Lianze Jiang, Florin Gogianu, Ehsan Abbasnejad
Accepted at CVPR 2025 (Oral, top 0.8%) Jun 2025
Links: arXiv · CVF Open Access Abstract Common choices of architecture give neural networks a preference for fitting data with simple functions. This …
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