58 citations · 216 across the 28 of their papers we have counts for
6 papers · 1 filter
Counterfactual Probing for Parallel Unmasking with Hidden Forest Structure
Ryotaro Kawata, Satoshi Hayakawa, Taiji Suzuki
Masked generative models offer parallel token prediction, but accurate parallel sampling must account for dependencies among tokens. When dependencies are unknown, finding safe bat…
Does Uniform Discrete Diffusion Need Time?
Chunsan Hong, Chieh-Hsin Lai, Satoshi Hayakawa +3
Uniform discrete diffusion models (UDMs) commonly use explicit time conditioning, but we find that it can often be unnecessary in practice. In this paper, we first show that the po…
From Truncation to Commitment: Persistent Context in Uniform Discrete Diffusion
Satoshi Hayakawa
Uniform-state discrete diffusion models update all tokens in parallel while keeping every position revisable. Even when the commonly used top- rule leaves only one candidate at…
Simplex Relaxation for Discrete Diffusion
Jinya Sakurai, Patrick Pynadath, Satoshi Hayakawa +4
Discrete diffusion models for categorical generation are defined by a corruption kernel, which determines the intermediate state space and the associated reverse prediction problem…
Convex-Geometric Error Bounds for Positive-Weight Kernel Quadrature
Satoshi Hayakawa
Kernel quadrature can exploit RKHS spectral structure and outperform Monte Carlo on smooth integrands, but optimized quadrature weights are generally signed and may be numerically…
Understanding and Accelerating the Training of Masked Diffusion Language Models
Chunsan Hong, Sanghyun Lee, Chieh-Hsin Lai +5
Masked diffusion models (MDMs) have emerged as a promising alternative to autoregressive models (ARMs) for language modeling. However, MDMs are known to learn substantially more sl…