8 citations · 11 across the 16 of their papers we have counts for
22 papers
MobiAgent: Dual-Loop Recursive Policy Self-Improvement for Long-Horizon Mobile Manipulation
Chenzhi Liu, Yue Zhang, Jiehong Lin +4
Long-horizon mobile manipulation presents significant challenges due to compounding execution errors and capacity interference between locomotion and arm control. While recent Visi…
AnyStep-WAM: Budget-Aligned Distillation and Adaptive Inference for World Action Models
Rui Wang, Xiangyu Wang, Donglin Yang +4
World-action models (WAMs) couple predictive visual modeling with action generation, typically relying on iterative denoising with a fixed denoising steps. However, manipulation ta…
LiFR v2: Completion-Augmented Event Propagation for High-Rate Dense Prediction
Tao Wan, Xiaoshan Wu, Yifei Yu +6
High-rate dense perception in dynamic environments is limited by the low update rate of RGB cameras, as rapid scene changes can occur between frames. Event cameras offer temporally…
RRAM-DP: Device-Calibrated Differential Privacy for In-Memory Edge Learning
Kwunhang Wong, Jichang Yang, Karl M. H. Lai +7
Edge Artificial Intelligence of Things (AIoT) systems often collect sensitive data in situ, raising serious privacy concerns. Resistive-switching random-access memory (RRAM) is an…
PhysEditBench: A Protocol-Conditioned Benchmark for Dense Physical-Map Prediction with Image Editors
Jiaxin Yang, Yu Hou, Muxin Liu +5
Can general-purpose image editors predict physical maps from a single RGB image? General-purpose image editors differ from standard task-specific dense-prediction models: they do n…
CoRe-Gen: Robust Spectrum-to-Structure Generation under Imperfect Fingerprint Conditions
Tianbo Liu, Chixiang Lu, Jing Hao +4
Molecular structure elucidation from tandem mass spectra (MS/MS) remains challenging, particularly for de novo generation beyond database coverage. A common approach decomposes the…
When to Trust Imagination: Adaptive Action Execution for World Action Models
Rui Wang, Yue Zhang, Jiehong Lin +5
World Action Models (WAMs) jointly predict future visual observations and actions, but typically execute a fixed number of actions before replanning, regardless of whether the imag…
Parameter Efficient Machine Unlearning on Hybrid Resistive Memory based Compute-in-Memory Accelerators
Ning Lin, Jichang Yang, Yangu He +17
Resistive memory compute-in-memory accelerators provide energy efficient analogue matrix vector multiplication for neural network inference, but frequent reprogramming of analogue…
VideoSSM: Autoregressive Long Video Generation with Hybrid State-Space Memory
Yifei Yu, Xiaoshan Wu, Xinting Hu +8
Autoregressive (AR) diffusion enables streaming, interactive long-video generation by producing frames causally, yet maintaining coherence over minute-scale horizons remains challe…
EAG3R: Event-Augmented 3D Geometry Estimation for Dynamic and Extreme-Lighting Scenes
Xiaoshan Wu, Yifei Yu, Xiaoyang Lyu +5
Robust 3D geometry estimation from videos is critical for applications such as autonomous navigation, SLAM, and 3D scene reconstruction. Recent methods like DUSt3R demonstrate that…
MG-Nav: Dual-Scale Visual Navigation via Sparse Spatial Memory
Bo Wang, Jiehong Lin, Chenzhi Liu +5
We present MG-Nav (Memory-Guided Navigation), a dual-scale framework for zero-shot visual navigation that unifies global memory-guided planning with local geometry-enhanced control…
Efficient lattice field theory simulation using adaptive normalizing flow on a resistive memory-based neural differential equation solver
Meng Xu, Jichang Yang, Ning Lin +6
Lattice field theory (LFT) simulations underpin advances in classical statistical mechanics and quantum field theory, providing a unified computational framework across particle, n…
Trustworthy Tree-based Machine Learning by Flash-based Analog CAM with Inherent Soft Boundaries
Bo Wen, Guoyun Gao, Zhicheng Xu +5
The rapid advancement of artificial intelligence has raised concerns regarding its trustworthiness, especially in terms of interpretability and robustness. Tree-based models like R…
Reconfigurable Digital RRAM Logic Enables In-Situ Pruning and Learning for Edge AI
Songqi Wang, Yue Zhang, Jia Chen +9
The human brain simultaneously optimizes synaptic weights and topology by growing, pruning, and strengthening synapses while performing all computation entirely in memory. In contr…
Topology Optimization of Random Memristors for Input-Aware Dynamic SNN
Bo Wang, Shaocong Wang, Ning Lin +12
There is unprecedented development in machine learning, exemplified by recent large language models and world simulators, which are artificial neural networks running on digital co…
Dynamic neural network with memristive CIM and CAM for 2D and 3D vision
Yue Zhang, Woyu Zhang, Shaocong Wang +14
The brain is dynamic, associative and efficient. It reconfigures by associating the inputs with past experiences, with fused memory and processing. In contrast, AI models are stati…
Efficient and accurate neural field reconstruction using resistive memory
Yifei Yu, Shaocong Wang, Woyu Zhang +16
Human beings construct perception of space by integrating sparse observations into massively interconnected synapses and neurons, offering a superior parallelism and efficiency. Re…
Resistive Memory-based Neural Differential Equation Solver for Score-based Diffusion Model
Jichang Yang, Hegan Chen, Jia Chen +19
Human brains image complicated scenes when reading a novel. Replicating this imagination is one of the ultimate goals of AI-Generated Content (AIGC). However, current AIGC methods,…
Random resistive memory-based deep extreme point learning machine for unified visual processing
Shaocong Wang, Yizhao Gao, Yi Li +21
Visual sensors, including 3D LiDAR, neuromorphic DVS sensors, and conventional frame cameras, are increasingly integrated into edge-side intelligent machines. Realizing intensive m…
Pruning random resistive memory for optimizing analogue AI
Yi Li, Songqi Wang, Yaping Zhao +17
The rapid advancement of artificial intelligence (AI) has been marked by the large language models exhibiting human-like intelligence. However, these models also present unpreceden…
SpikeMOT: Event-based Multi-Object Tracking with Sparse Motion Features
Song Wang, Zhu Wang, Can Li +2
In comparison to conventional RGB cameras, the superior temporal resolution of event cameras allows them to capture rich information between frames, making them prime candidates fo…
Resistive memory-based zero-shot liquid state machine for multimodal event data learning
Ning Lin, Shaocong Wang, Yi Li +19
The human brain is a complex spiking neural network (SNN), capable of learning multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains…