7 papers
Training-Free Generation of Protein Sequences from Small Family Alignments via Stochastic Attention
Jeffrey D. Varner
Generating novel protein sequences that respect a family's statistical constraints typically requires training deep generative models on thousands to millions of examples. Yet most…
Continuous Hidden Markov Models for Equity Returns: Heavy-Tail Emission Families and Regime-Conditional Value-at-Risk
Abdulrahman Alswaidan, Cade Jin, Jeffrey D. Varner
Synthetic generators of daily equity returns let practitioners stress test, backtest, and design scenarios that a single realized market history cannot supply, but only if the gene…
Stochastic Attention via Langevin Dynamics on the Modern Hopfield Energy
Abdulrahman Alswaidan, Jeffrey D. Varner
Attention heads retrieve: given a query, they return a weighted average of stored values. We showed that this computation is one step of gradient descent on the modern Hopfield ene…
Validated Synthetic Patient Generation for Small Longitudinal Cohorts: Coagulation Dynamics Across Pregnancy
Jeffrey D. Varner, Maria Cristina Bravo, Carole McBride +2
Small longitudinal cohorts, common in maternal health, rare diseases, and early-phase trials, limit computational modeling because enrollment is slow and the data are too sparse to…
Hybrid Hidden Markov Model for Modeling Equity Excess Growth Rate Dynamics: A Discrete-State Approach with Jump-Diffusion
Abdulrahman Alswaidan, Jeffrey D. Varner
Generating synthetic financial time series that preserve the statistical properties of real market data is essential for stress testing, risk model validation, and scenario design.…
Conditioning Protein Generation via Hopfield Pattern Multiplicity
Jeffrey D. Varner
Small protein-family alignments often contain a subset of interest but not enough labeled data to train a conditional generator. We condition a training-free stochastic-attention s…