6 citations · 10 across the 20 of their papers we have counts for
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Getting Free Bits Back from Rotational Symmetries in LLMs
Jiajun He, Gergely Flamich, José Miguel Hernández-Lobato
Current methods for compressing neural network weights, such as decomposition, pruning, quantization, and channel simulation, often overlook the inherent symmetries within these ne…
Training Neural Samplers with Reverse Diffusive KL Divergence
Jiajun He, Wenlin Chen, Mingtian Zhang +2
Training generative models to sample from unnormalized density functions is an important and challenging task in machine learning. Traditional training methods often rely on the re…
Accelerating Relative Entropy Coding with Space Partitioning
Jiajun He, Gergely Flamich, José Miguel Hernández-Lobato
Relative entropy coding (REC) algorithms encode a random sample following a target distribution , using a coding distribution shared between the sender and receiver. Sadly,…
Bidirectional Consistency Models
Liangchen Li, Jiajun He
Diffusion models (DMs) are capable of generating remarkably high-quality samples by iteratively denoising a random vector, a process that corresponds to moving along the probabilit…