no code implementations • 8 Apr 2024 • Aramayis Dallakyan, Yang Ni
In this paper, we introduce a novel identifiability criterion for DAGs that places constraints on the conditional variances of additive noise models.
no code implementations • 20 Mar 2024 • Wenjun Huang, Hanning Chen, Yang Ni, Arghavan Rezvani, Sanggeon Yun, Sungheon Jeon, Eric Pedley, Mohsen Imani
Detecting marine objects inshore presents challenges owing to algorithmic intricacies and complexities in system deployment.
no code implementations • 12 Mar 2024 • Hanning Chen, Wenjun Huang, Yang Ni, Sanggeon Yun, Fei Wen, Hugo Latapie, Mohsen Imani
Nevertheless, the naive application of VLMs leads to sub-optimal quality, due to the misalignment between embeddings of object images and their visual attributes, which are mainly adjective phrases.
no code implementations • 9 Mar 2024 • Hanning Chen, Yang Ni, Ali Zakeri, Zhuowen Zou, Sanggeon Yun, Fei Wen, Behnam Khaleghi, Narayan Srinivasa, Hugo Latapie, Mohsen Imani
When conducting cross-models and cross-platforms comparison, HDReason yields an average 4. 2x higher performance and 3. 4x better energy efficiency with similar accuracy versus the state-of-the-art FPGA-based GCN training platform.
no code implementations • 17 Feb 2024 • Yang Ni, Zhuowen Zou, Wenjun Huang, Hanning Chen, William Youngwoo Chung, Samuel Cho, Ranganath Krishnan, Pietro Mercati, Mohsen Imani
Drawing inspiration from the outstanding learning capability of our human brains, Hyperdimensional Computing (HDC) emerges as a novel computing paradigm, and it leverages high-dimensional vector presentation and operations for brain-like lightweight Machine Learning (ML).
no code implementations • 3 Feb 2024 • Wenjun Huang, Arghavan Rezvani, Hanning Chen, Yang Ni, Sanggeon Yun, Sungheon Jeong, Mohsen Imani
To enhance the framework's performance, the training process is customized and a "lazy" sensor deactivation strategy utilizing temporal information is introduced.
no code implementations • 17 Feb 2023 • Arhit Chakrabarti, Yang Ni, Ellen Ruth A. Morris, Michael L. Salinas, Robert S. Chapkin, Bani K. Mallick
We consider the problem of clustering grouped data with possibly non-exchangeable groups whose dependencies can be characterized by a known directed acyclic graph.
no code implementations • 18 Sep 2022 • Yang Ni
Causal discovery for quantitative data has been extensively studied but less is known for categorical data.
no code implementations • 1 Aug 2022 • Sina Shahhosseini, Yang Ni, Hamidreza Alikhani, Emad Kasaeyan Naeini, Mohsen Imani, Nikil Dutt, Amir M. Rahmani
Considering the significant role of wearable devices in monitoring human body parameters, on-device learning can be utilized to build personalized models for behavioral and physiological patterns, and provide data privacy for users at the same time.
no code implementations • 14 May 2022 • Yang Ni, Danny Abraham, Mariam Issa, Yeseong Kim, Pietro Mercati, Mohsen Imani
Our evaluation shows QHD capability for real-time learning, providing 34. 6 times speedup and significantly better quality of learning than DQN.
no code implementations • 19 Jan 2022 • Yang Ni, Bani Mallick
Causal discovery for purely observational, categorical data is a long-standing challenging problem.
no code implementations • NeurIPS 2020 • Junsouk Choi, Robert Chapkin, Yang Ni
To infer causal relationships in zero-inflated count data, we propose a new zero-inflated Poisson Bayesian network (ZIPBN) model.
no code implementations • 28 Jun 2019 • Yang Ni, Yuan Ji, Peter Mueller
Motivated by three case studies, we focus on clustering induced by a Dirichlet process mixture sampling model, inference under an Indian buffet process prior with a binomial sampling model, and with a categorical sampling model.
no code implementations • 28 May 2019 • Zeya Wang, Baoyu Jing, Yang Ni, Nanqing Dong, Pengtao Xie, Eric P. Xing
In this paper, we propose a novel relationship-aware adversarial domain adaptation (RADA) algorithm, which first utilizes a single multi-class domain discriminator to enforce the learning of inter-class dependency structure during domain-adversarial training and then aligns this structure with the inter-class dependencies that are characterized from training the label predictor on source domain.