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1. On Partial Prototype Collapse in the DINO Family of Self-Supervised Methods

2. Continuous Ensemble Weather Forecasting with Diffusion models

3. Prior Learning in Introspective VAEs

4. Towards Understanding Epoch-wise Double descent in Two-layer Linear Neural Networks

5. Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks

6. cryoSPHERE: Single-particle heterogeneous reconstruction from cryo EM

7. DINO as a von Mises-Fisher mixture model

8. On the connection between Noise-Contrastive Estimation and Contrastive Divergence

9. Microsecond time-resolved X-ray scattering by utilizing MHz repetition rate at second-generation XFELs

10. Discriminator Guidance for Autoregressive Diffusion Models

11. Graph-based Neural Weather Prediction for Limited Area Modeling

12. Temporal Graph Neural Networks for Irregular Data

13. Nonlinear System Identification: Learning while respecting physical models using a sequential Monte Carlo method

14. Calibration tests beyond classification

15. A Variational Perspective on Generative Flow Networks

16. Marginalized particle Gibbs for multiple state-space models coupled through shared parameters

17. Speeding Up Logic-Based Benders Decomposition by Strengthening Cuts with Graph Neural Networks

18. Scalable Deep Gaussian Markov Random Fields for General Graphs

19. Active Learning with Weak Supervision for Gaussian Processes

21. Robustness and Reliability When Training With Noisy Labels

22. Graph-based machine learning beyond stable materials and relaxed crystal structures

23. Markovian Score Climbing: Variational Inference with KL(p||q)

24. A general framework for ensemble distribution distillation

25. Deep Gaussian Markov Random Fields

26. Active Learning with Weak Supervision for Gaussian Processes

27. Parameter elimination in particle Gibbs sampling

28. Calibration tests in multi-class classification: A unifying framework

29. Particle filter with rejection control and unbiased estimator of the marginal likelihood

30. Elements of Sequential Monte Carlo

31. Evaluating model calibration in classification

32. Constructing the Matrix Multilayer Perceptron and its Application to the VAE

33. Graphical model inference: Sequential Monte Carlo meets deterministic approximations

34. Learning dynamical systems with particle stochastic approximation EM

35. Improving the particle filter in high dimensions using conjugate artificial process noise

36. Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations

37. Pseudo-extended Markov chain Monte Carlo

38. Distributed, scalable and gossip-free consensus optimization with application to data analysis

39. Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo

40. Learning of state-space models with highly informative observations: a tempered Sequential Monte Carlo solution

41. Smoothing with Couplings of Conditional Particle Filters

42. High-dimensional Filtering using Nested Sequential Monte Carlo

43. Pseudo-Marginal Hamiltonian Monte Carlo

44. Coupling of Particle Filters

45. Interacting Particle Markov Chain Monte Carlo

46. Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables

47. Blocking Strategies and Stability of Particle Gibbs Samplers

48. Particle ancestor sampling for near-degenerate or intractable state transition models

49. Rao-Blackwellized particle smoothers for conditionally linear Gaussian models

50. Sequential Monte Carlo Methods for System Identification

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