2 citations · 2 across the 6 of their papers we have counts for
6 papers
Neural Weight Compression for Language Models
Jegwang Ryu, Minkyu Kim, Seungjun Shin +3
Efficient compression of language model weights is increasingly critical as model scale and deployment grow. Yet, most existing methods rely on handcrafted transforms and heuristic…
OrthoRank: Token Selection via Sink Token Orthogonality for Efficient LLM inference
Seungjun Shin, Jaehoon Oh, Dokwan Oh
Attention mechanisms are central to the success of large language models (LLMs), enabling them to capture intricate token dependencies and implicitly assign importance to each toke…
Efficient Neural Video Representation with Temporally Coherent Modulation
Seungjun Shin, Suji Kim, Dokwan Oh
Implicit neural representations (INR) has found successful applications across diverse domains. To employ INR in real-life, it is important to speed up training. In the field of IN…
Diversify, Contextualize, and Adapt: Efficient Entropy Modeling for Neural Image Codec
Jun-Hyuk Kim, Seungeon Kim, Won-Hee Lee +1
Designing a fast and effective entropy model is challenging but essential for practical application of neural codecs. Beyond spatial autoregressive entropy models, more efficient b…
House of Cards: Massive Weights in LLMs
Jaehoon Oh, Seungjun Shin, Dokwan Oh
Massive activations, which manifest in specific feature dimensions of hidden states, introduce a significant bias in large language models (LLMs), leading to an overemphasis on the…
Neural Image Compression with Text-guided Encoding for both Pixel-level and Perceptual Fidelity
Hagyeong Lee, Minkyu Kim, Jun-Hyuk Kim +3
Recent advances in text-guided image compression have shown great potential to enhance the perceptual quality of reconstructed images. These methods, however, tend to have signific…