Topic: Machine Learning

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1. Diffusion Alignment as Variational Expectation-Maximization

   Paper • Feb 13, 2026 • arXiv • Zijing Ou, Jacob Si, Junyi Zhu, Yingzhen Li

   Diffusion alignment aims to optimize diffusion models for downstream objectives. While existing methods based on RL achieve success, they often suffer from reward over-optimization and mode collaps...

2. A hyperparameter benchmark of VAE-based methods for scRNA-seq

   Paper • Feb 10, 2026 • bioRxiv • Eduardo da Veiga Beltrame

   This paper presents a systematic benchmark of architectural hyperparameters for variational autoencoder (VAE) methods in single-cell RNA-seq batch integration. The study compares scVI, MrVI, and LD...

3. Automatic pain face analysis in mice: Applied to a varied dataset

   Paper • Feb 16, 2026 • bioRxiv • Anonymous Team

   The paper introduces an automated deep learning tool for the real-time assessment of pain in mice using the Mouse Grimace Scale (MGS). The model was trained on a large and diverse dataset with non-...

4. CellAwareGNN: Single-Cell Knowledge Graph Foundation Model for Drug Indication

   Paper • Feb 23, 2026 • bioRxiv • Zhang, X., Jeong, E., Yan, C., Feng, Y., Lyu, L., Guo, X., Chen, Y.

   CellAwareGNN is a new graph neural network (GNN) foundation model that integrates single-cell enhanced knowledge graphs for drug indication prediction. By modeling the complex relationships between...

5. High-accuracy sampling for diffusion models and log-concave distributions

   Paper • Feb 1, 2026 • arXiv • Fan Chen, Sinho Chewi, Constantinos Daskalakis, Alexander Rakhlin

   We present algorithms for diffusion model sampling which obtain δ-error in polylog(1/δ) steps, given access to eO(δ)-accurate score estimates in L2. This is an exponential improvement over all prev...

6. Identifying Intervenable and Interpretable Features via Orthogonality Regularization

   Paper • Feb 17, 2026 • arXiv • Moritz Miller, Florent Draye, Bernhard Schölkopf

   This paper addresses the fundamental challenge of 'feature disentanglement' in modern deep learning. We propose an Orthogonality Regularization technique to identify features that are both interpre...

7. Everyday Machine Learning in 2026: The Benefits You Actually Feel

   Blog • Feb 14, 2026 • Medium • Shane Collins

   In 2026, ML has moved from the cloud to our pockets, handling the 'normal messiness' of real life. This post explores the small, invisible ways AI has cleaned up our lives, from transcripts that ha...