1 citations · 1 across the 5 of their papers we have counts for
5 papers
AdaHOP: Fast and Accurate Low-Precision Training via Outlier-Pattern-Aware Rotation
Seonggon Kim, Alireza Khodamoradi, Pranathi Vasireddy +2
Hadamard transforms have become a key tool for stabilizing low-precision training, but existing methods apply them uniformly across tensors and computation paths. We show that this…
Merge-Friendly Post-Training Quantization for Multi-Target Domain Adaptation
Juncheol Shin, Minsang Seok, Seonggon Kim +1
Model merging has emerged as a powerful technique for combining task-specific weights, achieving superior performance in multi-target domain adaptation. However, when applied to pr…
HOT: Hadamard-based Optimized Training
Seonggon Kim, Juncheol Shin, Seung-taek Woo +1
It has become increasingly important to optimize backpropagation to reduce memory usage and computational overhead. Achieving this goal is highly challenging, as multiple objective…
PTQ4VM: Post-Training Quantization for Visual Mamba
Younghyun Cho, Changhun Lee, Seonggon Kim +1
Visual Mamba is an approach that extends the selective space state model, Mamba, to vision tasks. It processes image tokens sequentially in a fixed order, accumulating information…
HLQ: Fast and Efficient Backpropagation via Hadamard Low-rank Quantization
Seonggon Kim, Eunhyeok Park
With the rapid increase in model size and the growing importance of various fine-tuning applications, lightweight training has become crucial. Since the backward pass is twice as e…