Transactions on machine learning research最新文献_第4页

Spectral learning of Bernoulli linear dynamical systems models. 伯努利线性动力系统模型的谱学习。

Transactions on machine learning research Pub Date : 2023-07-01

Iris Stone, Yotam Sagiv, Il Memming Park, Jonathan Pillow

{"title":"Spectral learning of Bernoulli linear dynamical systems models.","authors":"Iris Stone, Yotam Sagiv, Il Memming Park, Jonathan Pillow","doi":"","DOIUrl":"","url":null,"abstract":"Latent linear dynamical systems with Bernoulli observations provide a powerful modeling framework for identifying the temporal dynamics underlying binary time series data, which arise in a variety of contexts such as binary decision-making and discrete stochastic processes (e.g., binned neural spike trains). Here we develop a spectral learning method for fast, efficient fitting of probit-Bernoulli latent linear dynamical system (LDS) models. Our approach extends traditional subspace identification methods to the Bernoulli setting via a transformation of the first and second sample moments. This results in a robust, fixed-cost estimator that avoids the hazards of local optima and the long computation time of iterative fitting procedures like the expectation-maximization (EM) algorithm. In regimes where data is limited or assumptions about the statistical structure of the data are not met, we demonstrate that the spectral estimate provides a good initialization for Laplace-EM fitting. Finally, we show that the estimator provides substantial benefits to real world settings by analyzing data from mice performing a sensory decision-making task.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"2023 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12981602/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147470527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the Convergence and Calibration of Deep Learning with Differential Privacy. 论具有差异隐私的深度学习的收敛与校准

Transactions on machine learning research Pub Date : 2023-06-01

Zhiqi Bu, Hua Wang, Zongyu Dai, Qi Long

引用次数: 0

Traditional Machine Learning Models for Building Energy Performance Prediction: A Comparative Research 建筑节能性能预测的传统机器学习模型比较研究

Transactions on machine learning research Pub Date : 2023-05-29 DOI: 10.11648/j.mlr.20230801.11

Zeyu Wu, Hongyang He

引用次数: 0

Automatic Indexing of Digital Objects Through Learning from User Data 通过学习用户数据实现数字对象的自动索引

Transactions on machine learning research Pub Date : 2023-01-31 DOI: 10.11648/j.mlr.20220702.12

C. Leung, Yuanxi Li

引用次数: 0

How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts. 你的公平性有多强?看不见的分配变化下的公平评估与维持。

Transactions on machine learning research Pub Date : 2023-01-01

Haotao Wang, Junyuan Hong, Jiayu Zhou, Zhangyang Wang

引用次数: 0

Estimating Potential Outcome Distributions with Collaborating Causal Networks. 利用协作因果网络估算潜在结果分布。

Transactions on machine learning research Pub Date : 2022-09-01

Tianhui Zhou, William E Carson, David Carlson

{"title":"Estimating Potential Outcome Distributions with Collaborating Causal Networks.","authors":"Tianhui Zhou, William E Carson, David Carlson","doi":"","DOIUrl":"","url":null,"abstract":"Traditional causal inference approaches leverage observational study data to estimate the difference in observed (factual) and unobserved (counterfactual) outcomes for a potential treatment, known as the Conditional Average Treatment Effect (CATE). However, CATE corresponds to the comparison on the first moment alone, and as such may be insufficient in reflecting the full picture of treatment effects. As an alternative, estimating the full potential outcome distributions could provide greater insights. However, existing methods for estimating treatment effect potential outcome distributions often impose restrictive or overly-simplistic assumptions about these distributions. Here, we propose Collaborating Causal Networks (CCN), a novel methodology which goes beyond the estimation of CATE alone by learning the full potential outcome distributions. Estimation of outcome distributions via the CCN framework does not require restrictive assumptions of the underlying data generating process (e.g. Gaussian errors). Additionally, our proposed method facilitates estimation of the utility of each possible treatment and permits individual-specific variation through utility functions (e.g. risk tolerance variability). CCN not only extends outcome estimation beyond traditional risk difference, but also enables a more comprehensive decision making process through definition of flexible comparisons. Under assumptions commonly made in the causal inference literature, we show that CCN learns distributions that asymptotically capture the correct potential outcome distributions. Furthermore, we propose an adjustment approach that is empirically effective in alleviating sample imbalance between treatment groups in observational studies. Finally, we evaluate the performance of CCN in multiple experiments on both synthetic and semi-synthetic data. We demonstrate that CCN learns improved distribution estimates compared to existing Bayesian and deep generative methods as well as improved decisions with respects to a variety of utility functions.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"2022 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10769464/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139378979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts 你的公平性有多强?看不见的分配变化下的公平评估与维持

Transactions on machine learning research Pub Date : 2022-07-04 DOI: 10.48550/arXiv.2207.01168

Haotao Wang, Junyuan Hong, Jiayu Zhou, Zhangyang Wang

引用次数: 5

Design to Build E-learning Application in SMP N 2 Busalangga Busalangga中学电子学习应用的设计

Transactions on machine learning research Pub Date : 2021-01-01 DOI: 10.11648/j.mlr.20210602.11

Jimi Asmara, Gregorius Rinduh Iriane, Edwin Ariesto Umbu Malahina

引用次数: 0

A Genetic Neuro-Fuzzy System for Diagnosing Clinical Depression 临床抑郁症的遗传神经模糊诊断系统

Transactions on machine learning research Pub Date : 2021-01-01 DOI: 10.11648/j.mlr.20210602.12

A. Adegboye, Imianvan Anthony Agboizebeta

引用次数: 2

Designing a Voice-controlled Wheelchair for Persian-speaking Users Using Deep Learning Networks with a Small Dataset 使用小数据集的深度学习网络为波斯语用户设计语音控制轮椅

Transactions on machine learning research Pub Date : 2021-01-01 DOI: 10.11648/j.mlr.20210601.11

Masoud Amiri, Manizheh Ranjbar, Mostafa Azami Gharetappeh

{"title":"Designing a Voice-controlled Wheelchair for Persian-speaking Users Using Deep Learning Networks with a Small Dataset","authors":"Masoud Amiri, Manizheh Ranjbar, Mostafa Azami Gharetappeh","doi":"10.11648/j.mlr.20210601.11","DOIUrl":"https://doi.org/10.11648/j.mlr.20210601.11","url":null,"abstract":": With the advancement of technology, the demand for improving the quality of life of the elderly and disabled has increased and their hope to overcome their problem is realized by using advanced technologies in the field of rehabilitation. Many existing electrical and electronic devices can be turned into more controllable and more functional devices using artificial intelligence. In every society, some spinal disabled people lack physical and motor abilities such as moving their limbs and they cannot use the normal wheelchair and need a wheelchair with voice control. The main challenge of this project is to identify the voice patterns of disabled people. Audio classification is one of the challenges in the field of pattern recognition. In this paper, a method of classifying ambient sounds based on the sound spectrogram, using deep neural networks is presented to classify Persian speakers sound for building a voice-controlled intelligent wheelchair. To do this, we used Inception-V3 as a convolutional neural network which is pretrained by the ImageNet dataset. In the next step, we trained the network with images that are generated using spectrogram images of the ambient sound of about 50 Persian speakers. The experimental results achieved a mean accuracy of 83.33%. In this plan, there is the ability to control the wheelchair by a third party (such as spouse, children or parents) by installing an application on their mobile phones. This wheelchair will be able to execute five commands such as stop, left, right, front and back.","PeriodicalId":75238,"journal":{"name":"Transactions on machine learning research","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74669781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0