3 citations · 4 across the 13 of their papers we have counts for
16 papers
A Survey on Self-Improving Test-Time Intelligence: Feedback-Driven Adapting, Learning, and Scaling at Inference
Shuaicheng Niu, Guohao Chen, Yaofo Chen +14
The ability of AI systems to improve their behavior during deployment is becoming increasingly important. As inference moves beyond the static execution of a fixed trained model, a…
Learning Physical Interaction: A Survey of Tactile- and Force-aware Robot Learning
Shilin Shan, Chuhao Zhou, Ruize Wang +30
Physically grounded robot intelligence requires robots to perceive, reason about, and regulate their interactions with the physical world. This capability is particularly critical…
YARD: Y-Architecture Register Decoding for Efficient Hallucination Mitigation in Large Vision-Language Models
Ting Chen, Geng Li, Guohao Chen +5
Contrastive decoding (CD) seeks to mitigate hallucinations in Large Vision-Language Models (LVLMs) by contrasting the output distributions of a standard model and a visually degrad…
OccamToken: Efficient VLM Inference with Training-Free and Budget-Adaptive Token Pruning
Geng Li, Guohao Chen, Ting Chen +6
Vision-language models (VLMs) rely on long visual token sequences for visual understanding, making the prefill stage expensive in both computation and memory. Most existing pruning…
Guided Trajectory Optimization with Sparse Scaling for Test-Time Diffusion
Gang Dai, Yining Huang, Yiming Xia +2
Test-Time Scaling (TTS) paradigm offers a promising perspective for enhancing the generation performance of diffusion models. However, current solutions largely restrict their sear…
EVA-0: Test-Time Model Evolution with Only Two Forward Passes per Sample
Guohao Chen, Shuaicheng Niu, Geng Li +4
Test-time model evolution offers a promising way for deployed models to improve from unlabeled test-time experience, yet most existing methods depend on backpropagation (BP), which…
Continual Knowledge Adaptation for Reinforcement Learning
Jinwu Hu, Zihao Lian, Zhiquan Wen +5
Reinforcement Learning enables agents to learn optimal behaviors through interactions with environments. However, real-world environments are typically non-stationary, requiring ag…
Adapt in the Wild: Test-Time Entropy Minimization with Sharpness and Feature Regularization
Shuaicheng Niu, Guohao Chen, Deyu Chen +7
Test-time adaptation (TTA) may fail to improve or even harm the model performance when test data have: 1) mixed distribution shifts, 2) small batch sizes, 3) online imbalanced labe…
ZeroSiam: An Efficient Asymmetry for Test-Time Entropy Optimization without Collapse
Guohao Chen, Shuaicheng Niu, Deyu Chen +5
Test-time entropy minimization helps adapt a model to novel environments and incentivize its reasoning capability, unleashing the model's potential during inference by allowing it…
Test-Time Model Adaptation for Quantized Neural Networks
Zeshuai Deng, Guohao Chen, Shuaicheng Niu +6
Quantizing deep models prior to deployment is a widely adopted technique to speed up inference for various real-time applications, such as autonomous driving. However, quantized mo…
When Small Guides Large: Cross-Model Co-Learning for Test-Time Adaptation
Chang'an Yi, Xiaohui Deng, Guohao Chen +3
Test-time Adaptation (TTA) adapts a given model to testing domain data with potential domain shifts through online unsupervised learning, yielding impressive performance. However,…
Test-Time Learning for Large Language Models
Jinwu Hu, Zhitian Zhang, Guohao Chen +6
While Large Language Models (LLMs) have exhibited remarkable emergent capabilities through extensive pre-training, they still face critical limitations in generalizing to specializ…
Self-Bootstrapping for Versatile Test-Time Adaptation
Shuaicheng Niu, Guohao Chen, Peilin Zhao +3
In this paper, we seek to develop a versatile test-time adaptation (TTA) objective for a variety of tasks - classification and regression across image-, object-, and pixel-level pr…
Efficient Dynamic Ensembling for Multiple LLM Experts
Jinwu Hu, Yufeng Wang, Shuhai Zhang +5
LLMs have demonstrated impressive performance across various language tasks. However, the strengths of LLMs can vary due to different architectures, model sizes, areas of training…
Test-Time Model Adaptation with Only Forward Passes
Shuaicheng Niu, Chunyan Miao, Guohao Chen +2
Test-time adaptation has proven effective in adapting a given trained model to unseen test samples with potential distribution shifts. However, in real-world scenarios, models are…
Uncertainty-Calibrated Test-Time Model Adaptation without Forgetting
Mingkui Tan, Guohao Chen, Jiaxiang Wu +4
Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and test data by adapting a given model w.r.t. any test sample. Although recent TTA has sh…