1 citations · 1 across the 7 of their papers we have counts for
7 papers
A Note on Stability for Orthogonalized Matrix Momentum with Client Sampling
Da Chang, Qiankun Shi, Lvgang Zhang +2
We study finite-sample generalization for a client-sampled distributed optimization scheme with matrix-valued parameters and orthogonalized momentum updates. The central quantity i…
When Does Value-Aware KV Eviction Help? A Fixed-Contract Diagnostic for Non-Monotone Cache Compression
Ruijie Zhang, Haozhe Liang, Da Chang +4
Long-context LLM inference is bottlenecked by the memory and bandwidth cost of reading large KV caches during decoding. KV compression reduces this cost by keeping only part of the…
MuonEq: Balancing Before Orthogonalization with Lightweight Equilibration
Da Chang, Qiankun Shi, Lvgang Zhang +5
Orthogonalized-update optimizers such as Muon improve training of matrix-valued parameters, but existing extensions typically either rescale updates after orthogonalization or use…
Calibrating and Rotating: A Unified Framework for Weight Conditioning in PEFT
Da Chang, Peng Xue, Yu Li +3
Parameter-Efficient Fine-Tuning (PEFT) methods are crucial for adapting large pre-trained models. Among these, LoRA is considered a foundational approach. Building on this, the inf…
Encoding Structural Constraints into Segment Anything Models via Probabilistic Graphical Models
Yu Li, Da Chang, Xi Xiao
While the Segment Anything Model (SAM) has achieved remarkable success in image segmentation, its direct application to medical imaging remains hindered by fundamental challenges,…
AlphaAdam:Asynchronous Masked Optimization with Dynamic Alpha for Selective Updates
Da Chang, Yu Li, Ganzhao Yuan
In the training of large language models (LLMs), updating parameters more efficiently and stably has always been an important challenge. To achieve efficient parameter updates, exi…
Mixed Text Recognition with Efficient Parameter Fine-Tuning and Transformer
Da Chang, Yu Li
With the rapid development of OCR technology, mixed-scene text recognition has become a key technical challenge. Although deep learning models have achieved significant results in…