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Amortized Factor Inference Networks for Posterior Inference
Joohwan Ko, Justin Domke
Amortized inference promises fast test-time Bayesian inference, but existing methods are inherently tied to fixed models. Extending amortization to unseen models typically requires…
Latent Target Score Matching, with an application to Simulation-Based Inference
Joohwan Ko, Tomas Geffner
Denoising score matching (DSM) for training diffusion models may suffer from high variance at low noise levels. Target Score Matching (TSM) mitigates this when clean data scores ar…
Relaxed Sequence Sampling for Diverse Protein Design
Joohwan Ko, Aristofanis Rontogiannis, Yih-En Andrew Ban +2
Protein design using structure prediction models such as AlphaFold2 has shown remarkable success, but existing approaches like relaxed sequence optimization (RSO) rely on single-pa…
Model-Informed Flows for Bayesian Inference
Joohwan Ko, Justin Domke
Variational inference often struggles with the posterior geometry exhibited by complex hierarchical Bayesian models. Recent advances in flow-based variational families and Variatio…
Layer-Adaptive State Pruning for Deep State Space Models
Minseon Gwak, Seongrok Moon, Joohwan Ko +1
Due to the lack of state dimension optimization methods, deep state space models (SSMs) have sacrificed model capacity, training search space, or stability to alleviate computation…
Learning to Scale Logits for Temperature-Conditional GFlowNets
Minsu Kim, Joohwan Ko, Taeyoung Yun +6
GFlowNets are probabilistic models that sequentially generate compositional structures through a stochastic policy. Among GFlowNets, temperature-conditional GFlowNets can introduce…