1 code implementation • 24 Apr 2024 • Sathwik Chadaga, Xinyu Wu, Eytan Modiano
We consider the problem of predicting power failure cascades due to branch failures.
no code implementations • 28 Feb 2024 • Reza Reihanisaransari, Chalapathi Charan Gajjela, Xinyu Wu, Ragib Ishrak, Sara Corvigno, Yanping Zhong, Jinsong Liu, Anil K. Sood, David Mayerich, Sebastian Berisha, Rohith Reddy
We effectively minimize data collection requirements by leveraging sparse data acquisition and employing curvelet-based reconstruction algorithms.
no code implementations • 19 Nov 2023 • Emerson Sie, Xinyu Wu, Heyu Guo, Deepak Vasisht
Millimeter-wave (mmWave) radar is increasingly being considered as an alternative to optical sensors for robotic primitives like simultaneous localization and mapping (SLAM).
1 code implementation • 26 Sep 2023 • Chenyang Miao, Yunduan Cui, Huiyun Li, Xinyu Wu
It alleviates the inconsistency of multiple agents' policy updates by introducing the relative entropy regularization to the Centralized Training with Decentralized Execution (CTDE) framework with the Actor-Critic (AC) structure.
1 code implementation • 20 Sep 2023 • Wenjun Huang, Yunduan Cui, Huiyun Li, Xinyu Wu
Its loss function is designed to correct the fitting error of neural networks for more accurate prediction of probabilistic models.
no code implementations • 28 Aug 2023 • Yuansheng Ni, Sichao Jiang, Xinyu Wu, Hui Shen, Yuli Zhou
The focus is on the robustness of instruction-tuned LLMs to seen and unseen tasks.
no code implementations • 26 Jun 2023 • Chengliang Liu, Binhua Huang, YiWen Liu, Yuanzhe Su, Ke Mai, Yupo Zhang, Zhengkun Yi, Xinyu Wu
In this paper, we investigate the effectiveness of contrastive learning methods for predicting grasp outcomes in an unsupervised manner.
no code implementations • 16 Feb 2022 • Zhenhua Xu, Yuxuan Liu, Lu Gan, Yuxiang Sun, Xinyu Wu, Ming Liu, Lujia Wang
To provide a solution to these problems, we propose a novel approach based on transformer and imitation learning in this paper.
1 code implementation • 28 Mar 2021 • Zhuang Wang, Xinyu Wu, T. S. Eugene Ng
It can even achieve a scaling factor of distributed training up to 99% over high-speed networks.
no code implementations • ICCV 2019 • Namdar Homayounfar, Wei-Chiu Ma, Justin Liang, Xinyu Wu, Jack Fan, Raquel Urtasun
One of the fundamental challenges to scale self-driving is being able to create accurate high definition maps (HD maps) with low cost.
no code implementations • ECCV 2020 • Abbas Sadat, Sergio Casas, Mengye Ren, Xinyu Wu, Pranaab Dhawan, Raquel Urtasun
In this paper we propose a novel end-to-end learnable network that performs joint perception, prediction and motion planning for self-driving vehicles and produces interpretable intermediate representations.
no code implementations • 7 Feb 2018 • Vishal Saxena, Xinyu Wu, Kehan Zhu
Emerging non-volatile memory (NVM), or memristive, devices promise energy-efficient realization of deep learning, when efficiently integrated with mixed-signal integrated circuits on a CMOS substrate.
no code implementations • 9 Jan 2018 • Xinyu Wu, Vishal Saxena
Brain-inspired learning mechanisms, e. g. spike timing dependent plasticity (STDP), enable agile and fast on-the-fly adaptation capability in a spiking neural network.
no code implementations • 5 Dec 2016 • Xinyu Wu, Vishal Saxena
Large-scale integration of emerging nanoscale non-volatile memory devices, e. g. resistive random-access memory (RRAM), can enable a new generation of neuromorphic computers that can solve a wide range of machine learning problems.
no code implementations • 2 Jun 2015 • Xinyu Wu, Vishal Saxena, Kehan Zhu
Neuromorphic systems that densely integrate CMOS spiking neurons and nano-scale memristor synapses open a new avenue of brain-inspired computing.
no code implementations • 2 Jun 2015 • Xinyu Wu, Vishal Saxena, Kehan Zhu
A neuromorphic chip that combines CMOS analog spiking neurons and memristive synapses offers a promising solution to brain-inspired computing, as it can provide massive neural network parallelism and density.
no code implementations • 28 May 2015 • Xinyu Wu, Vishal Saxena, Kehan Zhu, Sakkarapani Balagopal
Nanoscale resistive memories are expected to fuel dense integration of electronic synapses for large-scale neuromorphic system.
no code implementations • 1 Jun 2014 • Sheng Han, Suzhen Wang, Xinyu Wu
This paper proposed a new regression model called $l_1$-regularized outlier isolation and regression (LOIRE) and a fast algorithm based on block coordinate descent to solve this model.