Neural Potts Model
We propose the Neural Potts Model objective as an amortized optimization problem. The objective enables training a single model with shared parameters to explicitly model energy landscapes across multiple protein families. Given a protein sequence as input, the model is trained to predict a pairwise coupling matrix for a Potts model energy function describing the local evolutionary landscape of the sequence. Plausible couplings are predicted for train and validation sequences. A controlled ablation experiment assessing unsupervised contact prediction on sets of related protein families finds a gain from amortization for low-depth MSAs; the result is confirmed on a larger database with broad coverage of protein sequences.
Code (0)
등록된 구현이 없습니다.
Tasks
modelSimilar Papers 제목 키워드 기반
The Potts-Ising model for discrete multivariate data
Modeling dependencies in multivariate discrete data is a challenging problem, especially in high dimensions. The Potts model is a versatile such model, suitable when each coordinate is a categorical variable. However, th…
Multi-Channel Potts-Based Reconstruction for Multi-Spectral Computed Tomography
We consider reconstructing multi-channel images from measurements performed by photon-counting and energy-discriminating detectors in the setting of multi-spectral X-ray computed tomography (CT). Our aim is to exploit th…
Computed Tomography (CT)PottsMGNet: A Mathematical Explanation of Encoder-Decoder Based Neural Networks
For problems in image processing and many other fields, a large class of effective neural networks has encoder-decoder-based architectures. Although these networks have made impressive performances, mathematical explanat…
continuous-controlContinuous ControlDecoderImage Segmentation+2Bayesian image segmentations by Potts prior and loopy belief propagation
This paper presents a Bayesian image segmentation model based on Potts prior and loopy belief propagation. The proposed Bayesian model involves several terms, including the pairwise interactions of Potts models, and the …
Image SegmentationLearning TheorySemantic SegmentationMapping of attention mechanisms to a generalized Potts model
Transformers are neural networks that revolutionized natural language processing and machine learning. They process sequences of inputs, like words, using a mechanism called self-attention, which is trained via masked la…
Language ModelingLanguage ModellingMasked Language Modeling