Discrete EBM Sampling with Quasi-Rejection Quality Trade-Offs
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Solution Overview
Problem
Existing energy-based models (EBMs) face challenges in efficiently generating high-quality samples due to the need for a global upper-bound β that is often unknown or impractical, leading to inefficient sampling and limited applicability.
Innovation Solution
The quasi-rejection sampling (QRS) technique decouples EBM definition from proposal distribution tuning, allowing for explicit convergence bounds and diagnostics, enabling quality/efficiency trade-offs by adjusting the upper boundary value β based on KL divergence and acceptance rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional rejection sampling is used with EBM, then sample quality can be maintained, but sampling efficiency deteriorates due to unknown global upper-bound β
Solution Approach 1:
The patent changes the parameter β from a fixed global upper-bound to a local, adaptive bound that is computed based on the proposal distribution and energy function. This allows the acceptance probability to be calculated without knowing the global upper-bound, improving sampling efficiency while maintaining sample quality through local energy ratio comparisons.
Solution Approach 2:
The patent introduces an intermediary proposal distribution q(x) that mediates between the target EBM distribution and the sampling process. This proposal distribution serves as a bridge, allowing samples to be generated and evaluated through local energy ratios rather than requiring direct comparison with the unknown global upper-bound of the EBM.
2Reliability
If global upper-bound β is used for acceptance probability, then theoretical correctness is maintained, but practical applicability deteriorates due to computational intractability
Solution Approach 1:
The patent segments the global upper-bound requirement into local energy evaluations. Instead of computing one global β that bounds all possible states, the method evaluates energy ratios locally for each sampled state against the proposal distribution, making the computation tractable while preserving the rejection sampling theoretical framework.
Solution Approach 2:
The patent performs preliminary computation of the proposal distribution q(x) and its relationship to the EBM energy function before sampling. This preliminary setup establishes local bounds and acceptance criteria that can be efficiently evaluated during sampling, avoiding the need to compute the intractable global upper-bound at sampling time.
3Adaptability or versatility
If Markov Chain Monte-Carlo techniques are used, then sampling from EBM is enabled, but convergence speed deteriorates and diagnostic capability is limited
Solution Approach 1:
The patent incorporates feedback mechanisms through explicit convergence bounds and diagnostics that monitor sampling progress. The method computes measurable quantities that provide feedback on convergence status, allowing the sampling process to be adjusted or terminated based on actual convergence behavior rather than relying on theoretical mixing times of MCMC methods.
Data Source
AI summary
A sampling system includes: an energy-based model (EBM) configured to generate non-negative scores of an input having discrete classifications, respectively; and a sampling module configured to: generate a sample from a probability distribution of the EBM using a proposal distribution; set a probability of acceptance of the sample based on a minimum of (a) 1 and (b) an acceptance value determined based on the sample, a score of the sample from the EBM, the proposal distribution, and an upper boundary value; determine a distribution value between 0 and 1 using a uniform distribution; and discard the sample when the distribution value is greater than the probability of acceptance of the sample.