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From the 1 of 9 linked papers with an AI index.

activity
20242026
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9 papers

physics.chem-ph2026

Different Environments in Quantum Statistical Mechanics: To or not to

Ellen T. Ekstrøm, Jacob Pedersen, Ida-Marie Høyvik

We piece together textbook material to re-derive the parameter entering reduced density operators from quantum statistical mechanics. By re-deriving , we show that the conte…

cs.CV2026

Understanding Sources of Demographic Predictability in Brain MRI via Disentangling Anatomy and Contrast

Mehmet Yigit Avci, Akshit Achara, Andrew King +1

The paper introduces a disentangled representation learning framework that separates anatomical structure from acquisition-dependent contrast in brain MRI, showing that demographic…

cs.LG2026

Multi-Way Representation Alignment

Akshit Achara, Tatiana Gaintseva, Mateo Mahaut +5

The Platonic Representation Hypothesis suggests that independently trained neural networks converge to increasingly similar latent spaces. However, current strategies for mapping t…

cs.CV2026

EquiSteer: Cross-Attention Steering Towards a Fairer Text-Guided Image Generation

Tatiana Gaintseva, Akshit Achara, Gregory Slabaugh +2

Text-to-image diffusion models power everyday creative tasks, but they still reproduce the demographic biases in their training data. On common prompts such as ``a photo of a nurse…

cs.CV2026

Right Regions, Wrong Labels: Semantic Label Flips in Segmentation under Correlation Shift

Akshit Achara, Yovin Yahathugoda, Nick Byrne +4

The robustness of machine learning models can be compromised by spurious correlations between non-causal features in the input data and target labels. A common way to test for such…

cs.CV2025

Localising Shortcut Learning in Pixel Space via Ordinal Scoring Correlations for Attribution Representations (OSCAR)

Akshit Achara, Peter Triantafillou, Esther Puyol-Antón +2

Deep neural networks often exploit shortcuts. These are spurious cues which are associated with output labels in the training data but are unrelated to task semantics. When the sho…