Proceedings of machine learning research最新文献_第10页

Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets 集合范数与等变跳跃连接:在深度集合中放置深度

Proceedings of machine learning research Pub Date : 2022-06-23 DOI: 10.48550/arXiv.2206.11925

Lily H. Zhang, Veronica Tozzo, J. Higgins, R. Ranganath

引用次数: 4

Fair Generalized Linear Models with a Convex Penalty 具有凸惩罚的公平广义线性模型

Proceedings of machine learning research Pub Date : 2022-06-18 DOI: 10.48550/arXiv.2206.09076

Hyungrok Do, Preston J. Putzel, Axel Martin, Padhraic Smyth, Judy Zhong

引用次数: 5

LIMO: Latent Inceptionism for Targeted Molecule Generation LIMO:靶向分子生成的潜在初始论

Proceedings of machine learning research Pub Date : 2022-06-17 DOI: 10.48550/arXiv.2206.09010

P. Eckmann, Kunyang Sun, Bo Zhao, Mudong Feng, M. Gilson, Rose Yu

{"title":"LIMO: Latent Inceptionism for Targeted Molecule Generation","authors":"P. Eckmann, Kunyang Sun, Bo Zhao, Mudong Feng, M. Gilson, Rose Yu","doi":"10.48550/arXiv.2206.09010","DOIUrl":"https://doi.org/10.48550/arXiv.2206.09010","url":null,"abstract":"Generation of drug-like molecules with high binding affinity to target proteins remains a difficult and resource-intensive task in drug discovery. Existing approaches primarily employ reinforcement learning, Markov sampling, or deep generative models guided by Gaussian processes, which can be prohibitively slow when generating molecules with high binding affinity calculated by computationally-expensive physics-based methods. We present Latent Inceptionism on Molecules (LIMO), which significantly accelerates molecule generation with an inceptionism-like technique. LIMO employs a variational autoencoder-generated latent space and property prediction by two neural networks in sequence to enable faster gradient-based reverse-optimization of molecular properties. Comprehensive experiments show that LIMO performs competitively on benchmark tasks and markedly outperforms state-of-the-art techniques on the novel task of generating drug-like compounds with high binding affinity, reaching nanomolar range against two protein targets. We corroborate these docking-based results with more accurate molecular dynamics-based calculations of absolute binding free energy and show that one of our generated drug-like compounds has a predicted K D (a measure of binding affinity) of 6 · 10-14 M against the human estrogen receptor, well beyond the affinities of typical early-stage drug candidates and most FDA-approved drugs to their respective targets. Code is available at https://github.com/Rose-STL-Lab/LIMO.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"162 1","pages":"5777-5792"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44429182","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: 14

FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation FBNETGEN:基于任务感知gnn的脑功能网络生成fMRI分析

Proceedings of machine learning research Pub Date : 2022-05-25 DOI: 10.48550/arXiv.2205.12465

Xuan Kan, Hejie Cui, Joshua Lukemire, Ying Guo, Carl Yang

{"title":"FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation","authors":"Xuan Kan, Hejie Cui, Joshua Lukemire, Ying Guo, Carl Yang","doi":"10.48550/arXiv.2205.12465","DOIUrl":"https://doi.org/10.48550/arXiv.2205.12465","url":null,"abstract":"Functional magnetic resonance imaging (fMRI) is one of the most common imaging modalities to investigate brain functions. Recent studies in neuroscience stress the great potential of functional brain networks constructed from fMRI data for clinical predictions. Traditional functional brain networks, however, are noisy and unaware of downstream prediction tasks, while also incompatible with the deep graph neural network (GNN) models. In order to fully unleash the power of GNNs in network-based fMRI analysis, we develop FBNETGEN, a task-aware and interpretable fMRI analysis framework via deep brain network generation. In particular, we formulate (1) prominent region of interest (ROI) features extraction, (2) brain networks generation, and (3) clinical predictions with GNNs, in an end-to-end trainable model under the guidance of particular prediction tasks. Along with the process, the key novel component is the graph generator which learns to transform raw time-series features into task-oriented brain networks. Our learnable graphs also provide unique interpretations by highlighting prediction-related brain regions. Comprehensive experiments on two datasets, i.e., the recently released and currently largest publicly available fMRI dataset Adolescent Brain Cognitive Development (ABCD), and the widely-used fMRI dataset PNC, prove the superior effectiveness and interpretability of FBNETGEN. The implementation is available at https://github.com/Wayfear/FBNETGEN.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"172 1","pages":"618-637"},"PeriodicalIF":0.0,"publicationDate":"2022-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42345723","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: 40

An Extensive Data Processing Pipeline for MIMIC-IV 用于MIMIC-IV的扩展数据处理管道

Proceedings of machine learning research Pub Date : 2022-04-29 DOI: 10.48550/arXiv.2204.13841

Mehak Gupta, Brennan M Gallamoza, Nicolas Cutrona, Pranjal Dhakal, Raphael Poulain, Rahmatollah Beheshti

{"title":"An Extensive Data Processing Pipeline for MIMIC-IV","authors":"Mehak Gupta, Brennan M Gallamoza, Nicolas Cutrona, Pranjal Dhakal, Raphael Poulain, Rahmatollah Beheshti","doi":"10.48550/arXiv.2204.13841","DOIUrl":"https://doi.org/10.48550/arXiv.2204.13841","url":null,"abstract":"An increasing amount of research is being devoted to applying machine learning methods to electronic health record (EHR) data for various clinical purposes. This growing area of research has exposed the challenges of the accessibility of EHRs. MIMIC is a popular, public, and free EHR dataset in a raw format that has been used in numerous studies. The absence of standardized preprocessing steps can be, however, a significant barrier to the wider adoption of this rare resource. Additionally, this absence can reduce the reproducibility of the developed tools and limit the ability to compare the results among similar studies. In this work, we provide a greatly customizable pipeline to extract, clean, and preprocess the data available in the fourth version of the MIMIC dataset (MIMIC-IV). The pipeline also presents an end-to-end wizard-like package supporting predictive model creations and evaluations. The pipeline covers a range of clinical prediction tasks which can be broadly classified into four categories - readmission, length of stay, mortality, and phenotype prediction. The tool is publicly available at https://github.com/healthylaife/MIMIC-IV-Data-Pipeline.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"193 1","pages":"311-325"},"PeriodicalIF":0.0,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46827218","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: 13

CAiD: Context-Aware Instance Discrimination for Self-supervised Learning in Medical Imaging 医学影像中自我监督学习的情境感知实例辨析

Proceedings of machine learning research Pub Date : 2022-04-15 DOI: 10.48550/arXiv.2204.07344

M. Taher, Fatemeh Haghighi, M. Gotway, Jianming Liang

{"title":"CAiD: Context-Aware Instance Discrimination for Self-supervised Learning in Medical Imaging","authors":"M. Taher, Fatemeh Haghighi, M. Gotway, Jianming Liang","doi":"10.48550/arXiv.2204.07344","DOIUrl":"https://doi.org/10.48550/arXiv.2204.07344","url":null,"abstract":"Recently, self-supervised instance discrimination methods have achieved significant success in learning visual representations from unlabeled photographic images. However, given the marked differences between photographic and medical images, the efficacy of instance-based objectives, focusing on learning the most discriminative global features in the image (i.e., wheels in bicycle), remains unknown in medical imaging. Our preliminary analysis showed that high global similarity of medical images in terms of anatomy hampers instance discrimination methods for capturing a set of distinct features, negatively impacting their performance on medical downstream tasks. To alleviate this limitation, we have developed a simple yet effective self-supervised framework, called Context-Aware instance Discrimination (CAiD). CAiD aims to improve instance discrimination learning by providing finer and more discriminative information encoded from a diverse local context of unlabeled medical images. We conduct a systematic analysis to investigate the utility of the learned features from a three-pronged perspective: (i) generalizability and transferability, (ii) separability in the embedding space, and (iii) reusability. Our extensive experiments demonstrate that CAiD (1) enriches representations learned from existing instance discrimination methods; (2) delivers more discriminative features by adequately capturing finer contextual information from individual medial images; and (3) improves reusability of low/mid-level features compared to standard instance discriminative methods. As open science, all codes and pre-trained models are available on our GitHub page: https://github.com/JLiangLab/CAiD.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"172 1","pages":"535-551"},"PeriodicalIF":0.0,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45679429","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: 19

Fast Sparse Classification for Generalized Linear and Additive Models. 广义线性模型和加法模型的快速稀疏分类。

Proceedings of machine learning research Pub Date : 2022-03-01

Jiachang Liu, Chudi Zhong, Margo Seltzer, Cynthia Rudin

引用次数: 0

Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals. 独立Metropolis-Hastings采样器与归一化流方案的适配。

Proceedings of machine learning research Pub Date : 2022-03-01

James A Brofos, Marylou Gabrié, Marcus A Brubaker, Roy R Lederman

{"title":"Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals.","authors":"James A Brofos, Marylou Gabrié, Marcus A Brubaker, Roy R Lederman","doi":"","DOIUrl":"","url":null,"abstract":"Markov Chain Monte Carlo (MCMC) methods are a powerful tool for computation with complex probability distributions. However the performance of such methods is critically dependent on properly tuned parameters, most of which are difficult if not impossible to know a priori for a given target distribution. Adaptive MCMC methods aim to address this by allowing the parameters to be updated during sampling based on previous samples from the chain at the expense of requiring a new theoretical analysis to ensure convergence. In this work we extend the convergence theory of adaptive MCMC methods to a new class of methods built on a powerful class of parametric density estimators known as normalizing flows. In particular, we consider an independent Metropolis-Hastings sampler where the proposal distribution is represented by a normalizing flow whose parameters are updated using stochastic gradient descent. We explore the practical performance of this procedure on both synthetic settings and in the analysis of a physical field system, and compare it against both adaptive and non-adaptive MCMC methods.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"151 ","pages":"5949-5986"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9923871/pdf/nihms-1869589.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10742395","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

Neural Survival Clustering: Non-parametric mixture of neural networks for survival clustering. 神经生存聚类：用于生存聚类的非参数混合神经网络。

Proceedings of machine learning research Pub Date : 2022-01-01

Vincent Jeanselme, Brian Tom, Jessica Barrett

引用次数: 0

Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness. 临床存在下的估算策略：对算法公平性的影响

Proceedings of machine learning research Pub Date : 2022-01-01

Vincent Jeanselme, Maria De-Arteaga, Zhe Zhang, Jessica Barrett, Brian Tom

{"title":"Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness.","authors":"Vincent Jeanselme, Maria De-Arteaga, Zhe Zhang, Jessica Barrett, Brian Tom","doi":"","DOIUrl":"","url":null,"abstract":"Biases have marked medical history, leading to unequal care affecting marginalised groups. The patterns of missingness in observational data often reflect these group discrepancies, but the algorithmic fairness implications of group-specific missingness are not well understood. Despite its potential impact, imputation is too often an overlooked preprocessing step. When explicitly considered, attention is placed on overall performance, ignoring how this preprocessing can reinforce groupspecific inequities. Our work questions this choice by studying how imputation affects downstream algorithmic fairness. First, we provide a structured view of the relationship between clinical presence mechanisms and groupspecific missingness patterns. Then, through simulations and real-world experiments, we demonstrate that the imputation choice influences marginalised group performance and that no imputation strategy consistently reduces disparities. Importantly, our results show that current practices may endanger health equity as similarly performing imputation strategies at the population level can affect marginalised groups differently. Finally, we propose recommendations for mitigating inequities that may stem from a neglected step of the machine learning pipeline.","PeriodicalId":74504,"journal":{"name":"Proceedings of machine learning research","volume":"193 ","pages":"12-34"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7614014/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10481001","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