From the 1 of 6 linked papers with an AI index.
6 papers
Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning
Xin Qiu, Yulu Gan, Conor F. Hayes +6
The paper shows that evolution strategies can successfully fine‑tune billion‑parameter large language models without backpropagation, outperforming reinforcement learning in stabil…
Neural Thickets: Diverse Task Experts Are Dense Around Pretrained Weights
Yulu Gan, Phillip Isola
Pretraining produces a learned parameter vector that is typically treated as a starting point for further iterative adaptation. In this work, we instead view the outcome of pretrai…
FoundationMotion: Auto-Labeling and Reasoning about Spatial Movement in Videos
Yulu Gan, Ligeng Zhu, Dandan Shan +8
Motion understanding is fundamental to physical reasoning, enabling models to infer dynamics and predict future states. However, state-of-the-art models still struggle on recent mo…
SAN: Hypothesizing Long-Term Synaptic Development and Neural Engram Mechanism in Scalable Model's Parameter-Efficient Fine-Tuning
Gaole Dai, Chun-Kai Fan, Yiming Tang +7
Advances in Parameter-Efficient Fine-Tuning (PEFT) bridged the performance gap with Full Fine-Tuning (FFT) through sophisticated analysis of pre-trained parameter spaces. Starting…
Split-Ensemble: Efficient OOD-aware Ensemble via Task and Model Splitting
Anthony Chen, Huanrui Yang, Yulu Gan +7
Uncertainty estimation is crucial for machine learning models to detect out-of-distribution (OOD) inputs. However, the conventional discriminative deep learning classifiers produce…
Exploring Sparse Visual Prompt for Domain Adaptive Dense Prediction
Senqiao Yang, Jiarui Wu, Jiaming Liu +6
The visual prompts have provided an efficient manner in addressing visual cross-domain problems. In previous works, Visual Domain Prompt (VDP) first introduces domain prompts to ta…