2 citations · 3 across the 7 of their papers we have counts for
7 papers
Speculative Macro Commit for Faster Tool-Using Agents
Zeyu Liu, Souvik Kundu, Peter A. Beerel
Tool-using LLM agents spend wall-clock time not only on model inference but also in serial action--observation turns, where each tool call, environment transition, and observation…
Linearizing Vision Transformer with Test-Time Training
Yining Li, Dongchen Han, Zeyu Liu +3
While linear-complexity attention mechanisms offer a promising alternative to Softmax attention for overcoming the quadratic bottleneck, training such models from scratch remains p…
LAWCAT: Efficient Distillation from Quadratic to Linear Attention with Convolution across Tokens for Long Context Modeling
Zeyu Liu, Souvik Kundu, Lianghao Jiang +5
Although transformer architectures have achieved state-of-the-art performance across diverse domains, their quadratic computational complexity with respect to sequence length remai…
AFLoRA: Adaptive Freezing of Low Rank Adaptation in Parameter Efficient Fine-Tuning of Large Models
Zeyu Liu, Souvik Kundu, Anni Li +3
We present a novel Parameter-Efficient Fine-Tuning (PEFT) method, dubbed as Adaptive Freezing of Low Rank Adaptation (AFLoRA). Specifically, for each pre-trained frozen weight tens…
LMUFormer: Low Complexity Yet Powerful Spiking Model With Legendre Memory Units
Zeyu Liu, Gourav Datta, Anni Li +1
Transformer models have demonstrated high accuracy in numerous applications but have high complexity and lack sequential processing capability making them ill-suited for many strea…
When Bio-Inspired Computing meets Deep Learning: Low-Latency, Accurate, & Energy-Efficient Spiking Neural Networks from Artificial Neural Networks
Gourav Datta, Zeyu Liu, James Diffenderfer +2
Bio-inspired Spiking Neural Networks (SNN) are now demonstrating comparable accuracy to intricate convolutional neural networks (CNN), all while delivering remarkable energy and la…
Spiking Neural Networks with Dynamic Time Steps for Vision Transformers
Gourav Datta, Zeyu Liu, Anni Li +1
Spiking Neural Networks (SNNs) have emerged as a popular spatio-temporal computing paradigm for complex vision tasks. Recently proposed SNN training algorithms have significantly r…