Approximate Bayesian Inference via Bitstring Representations
The machine learning community has recently put effort into quantized or low-precision arithmetics to scale large models. This paper proposes performing probabilistic inference in the quantized, discrete parameter space created by these representations, effectively enabling us to learn a continuous distribution using discrete parameters. We consider both 2D densities and quantized neural networks, where we introduce a tractable learning approach using probabilistic circuits. This method offers a scalable solution to manage complex distributions and provides clear insights into model behavior. We validate our approach with various models, demonstrating inference efficiency without sacrificing accuracy. This work advances scalable, interpretable machine learning by utilizing discrete approximations for probabilistic computations.
Code (0)
등록된 구현이 없습니다.
Tasks
Interpretable Machine LearningBayesian InferenceSimilar Papers 제목 키워드 기반
Performance Evaluation of Bitstring Representations in a Linear Genetic Programming Framework
Different bitstring representations can yield varying computational performance. This work compares three bitstring implementations in C++: std::bitset, boost::dynamic_bitset, and a custom direct implementation. Their pe…
Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference
We formalize the problem of learning interdomain correspondences in the absence of paired data as Bayesian inference in a latent variable model (LVM), where one seeks the underlying hidden representations of entities fro…
Bayesian InferenceVariational InferenceDeepMorph: A System for Hiding Bitstrings in Morphable Vector Drawings
We introduce DeepMorph, an information embedding technique for vector drawings. Provided a vector drawing, such as a Scalable Vector Graphics (SVG) file, our method embeds bitstrings in the image by perturbing the drawin…
Camera Pose EstimationDecoderobject-detectionObject Detection+2Efficient Attack Graph Analysis through Approximate Inference
Attack graphs provide compact representations of the attack paths that an attacker can follow to compromise network resources by analysing network vulnerabilities and topology. These representations are a powerful tool f…
Bayesian InferenceClusteringAmortized Bayesian inference for clustering models
We develop methods for efficient amortized approximate Bayesian inference over posterior distributions of probabilistic clustering models, such as Dirichlet process mixture models. The approach is based on mapping distri…
Bayesian InferenceClustering