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1. Integrating Probabilistic Trees and Causal Networks for Clinical and Epidemiological Data

2. Multi-Head Explainer: A General Framework to Improve Explainability in CNNs and Transformers

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3. KG4Diagnosis: A Hierarchical Multi-Agent LLM Framework with Knowledge Graph Enhancement for Medical Diagnosis

4. Dirac-Equation Signal Processing: Physics Boosts Topological Machine Learning

5. Building Confidence in Deep Generative Protein Design

6. Explainable Deep Learning Framework for SERS Bio-quantification

7. Deep Equilibrium Algorithmic Reasoning

8. Predicting time-varying flux and balance in metabolic systems using structured neural-ODE processes

9. Explaining Hypergraph Neural Networks: From Local Explanations to Global Concepts

10. SPHINX: Structural Prediction using Hypergraph Inference Network

11. Bayesian Binary Search

12. Heterogeneous Sheaf Neural Networks

13. Neural Algorithmic Reasoning with Multiple Correct Solutions

14. A Survey for Large Language Models in Biomedicine

15. Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations

16. TourSynbio: A Multi-Modal Large Model and Agent Framework to Bridge Text and Protein Sequences for Protein Engineering

17. GenRec: Generative Sequential Recommendation with Large Language Models

18. Joint Diffusion Processes as an Inductive Bias in Sheaf Neural Networks

19. xAI-Drop: Don't Use What You Cannot Explain

20. AB$\mathbb{C}$MB: Deep Delensing Assisted Likelihood-Free Inference from CMB Polarization Maps

21. Metric Learning for Clifford Group Equivariant Neural Networks

22. G-Adaptivity: optimised graph-based mesh relocation for finite element methods

23. Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease

24. Evaluating representation learning on the protein structure universe

25. RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design

26. Morse Index Stability for the Ginzburg-Landau Approximation

27. Improving Antibody Design with Force-Guided Sampling in Diffusion Models

28. FLUID-LLM: Learning Computational Fluid Dynamics with Spatiotemporal-aware Large Language Models

29. ZeroPur: Succinct Training-Free Adversarial Purification

30. E(n) Equivariant Message Passing Cellular Networks

31. TabMDA: Tabular Manifold Data Augmentation for Any Classifier using Transformers with In-context Subsetting

32. DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised $h$-transform

33. The Explanation Necessity for Healthcare AI

34. Sheaf HyperNetworks for Personalized Federated Learning

35. Contrastive-Adversarial and Diffusion: Exploring pre-training and fine-tuning strategies for sulcal identification

36. How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing

37. Solving the enigma: Deriving optimal explanations of deep networks

38. Artificial Intelligence-powered fossil shark tooth identification: Unleashing the potential of Convolutional Neural Networks

39. SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints

40. DCAE-SR: Design of a Denoising Convolutional Autoencoder for reconstructing Electrocardiograms signals at Super Resolution

41. Sphere Neural-Networks for Rational Reasoning

42. Wet TinyML: Chemical Neural Network Using Gene Regulation and Cell Plasticity

43. Optimizing Polynomial Graph Filters: A Novel Adaptive Krylov Subspace Approach

44. Leveraging graph neural networks for supporting Automatic Triage of Patients

45. Understanding Biology in the Age of Artificial Intelligence

46. Enhancing Node Representations for Real-World Complex Networks with Topological Augmentation

47. An end-to-end attention-based approach for learning on graphs

48. Position: Topological Deep Learning is the New Frontier for Relational Learning

49. HyperBERT: Mixing Hypergraph-Aware Layers with Language Models for Node Classification on Text-Attributed Hypergraphs

50. The Deep Equilibrium Algorithmic Reasoner