Search Results for author: Yang Ni

Found 14 papers, 0 papers with code

Generalized Criterion for Identifiability of Additive Noise Models Using Majorization

no code implementations8 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.

TaskCLIP: Extend Large Vision-Language Model for Task Oriented Object Detection

no code implementations12 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.

Language Modelling Object +3

HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning

no code implementations9 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.

Graph Classification Graph Learning +1

HEAL: Brain-inspired Hyperdimensional Efficient Active Learning

no code implementations17 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).

Active Learning Diversity

A Plug-in Tiny AI Module for Intelligent and Selective Sensor Data Transmission

no code implementations3 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.

Graphical Dirichlet Process for Clustering Non-Exchangeable Grouped Data

no code implementations17 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.


Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation

no code implementations18 Sep 2022 Yang Ni

Causal discovery for quantitative data has been extensively studied but less is known for categorical data.

Causal Discovery

Efficient Personalized Learning for Wearable Health Applications using HyperDimensional Computing

no code implementations1 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.

BIG-bench Machine Learning Privacy Preserving

Efficient Off-Policy Reinforcement Learning via Brain-Inspired Computing

no code implementations14 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.

Decision Making Q-Learning +2

Ordinal Causal Discovery

no code implementations19 Jan 2022 Yang Ni, Bani Mallick

Causal discovery for purely observational, categorical data is a long-standing challenging problem.

Causal Discovery

Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks

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.

Bayesian Inference

Consensus Monte Carlo for Random Subsets using Shared Anchors

no code implementations28 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.

Clustering valid

Adversarial Domain Adaptation Being Aware of Class Relationships

no code implementations28 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.

Domain Adaptation Transfer Learning

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