6 papers
SoloQ: Calibration-Free Quantization for Diffusion Language Models
Donghyun Lee, Arkapravo Ghosh, Varun Manjunath +5
Diffusion large language models dLLMs) have emerged as a promising alternative to autoregressive language models through bidirectional diffusion-based token generation. However, th…
OpenWorldSAM: Extending SAM2 for Universal Image Segmentation with Language Prompts
Shiting Xiao, Rishabh Kabra, Yuhang Li +3
The ability to segment objects based on open-ended language prompts remains a critical challenge, requiring models to ground textual semantics into precise spatial masks while hand…
Memba: Membrane-driven Parameter-Efficient Fine-Tuning for Mamba
Donghyun Lee, Yuhang Li, Ruokai Yin +2
State Space Models (SSMs) have emerged as powerful alternatives to attention-based Transformers, with Mamba demonstrating impressive efficiency and scalability. As these models gro…
GPTAQ: Efficient Finetuning-Free Quantization for Asymmetric Calibration
Yuhang Li, Ruokai Yin, Donghyun Lee +2
We introduce GPTAQ, a novel finetuning-free quantization method for compressing large-scale transformer architectures. Unlike the previous GPTQ method, which independently calibrat…
ReSpike: Residual Frames-based Hybrid Spiking Neural Networks for Efficient Action Recognition
Shiting Xiao, Yuhang Li, Youngeun Kim +2
Spiking Neural Networks (SNNs) have emerged as a compelling, energy-efficient alternative to traditional Artificial Neural Networks (ANNs) for static image tasks such as image clas…
Spiking Transformer with Spatial-Temporal Attention
Donghyun Lee, Yuhang Li, Youngeun Kim +2
Spike-based Transformer presents a compelling and energy-efficient alternative to traditional Artificial Neural Network (ANN)-based Transformers, achieving impressive results throu…