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Found 5 papers, 0 papers with code
Continuous Tensor Relaxation for Finding Diverse Solutions in Combinatorial Optimization Problems
However, a single solution may not be suitable in practical scenarios, as the objective functions and constraints are only approximations of original real-world situations.
Learning Dynamics in Linear VAE: Posterior Collapse Threshold, Superfluous Latent Space Pitfalls, and Speedup with KL Annealing
To mitigate this problem, an adjustable hyperparameter $\beta$ and a strategy for annealing this parameter, called KL annealing, are proposed.
Controlling Continuous Relaxation for Combinatorial Optimization
In addition, these solvers employ a continuous relaxation strategy; thus, post-learning rounding from the continuous space back to the original discrete space is required, undermining the robustness of the results.
Dataset Size Dependence of Rate-Distortion Curve and Threshold of Posterior Collapse in Linear VAE
This paper presents a closed-form expression to assess the relationship between the beta in VAE, the dataset size, the posterior collapse, and the rate-distortion curve by analyzing a minimal VAE in a high-dimensional limit.
Toward Unlimited Self-Learning MCMC with Parallel Adaptive Annealing
However, SLMC methods are difficult to directly apply to multimodal distributions for which training data are difficult to obtain.
