... International Conference on Learning Representations最新文献_第7页

Multi-resolution modeling of a discrete stochastic process identifies causes of cancer. 一个离散随机过程的多分辨率模型确定癌症的原因。

... International Conference on Learning Representations Pub Date : 2021-05-01

Adam Yaari, Maxwell Sherman, Oliver Priebe, Po-Ru Loh, Boris Katz, Andrei Barbu, Bonnie Berger

{"title":"Multi-resolution modeling of a discrete stochastic process identifies causes of cancer.","authors":"Adam Yaari, Maxwell Sherman, Oliver Priebe, Po-Ru Loh, Boris Katz, Andrei Barbu, Bonnie Berger","doi":"","DOIUrl":"","url":null,"abstract":"Detection of cancer-causing mutations within the vast and mostly unexplored human genome is a major challenge. Doing so requires modeling the background mutation rate, a highly non-stationary stochastic process, across regions of interest varying in size from one to millions of positions. Here, we present the split-Poisson-Gamma (SPG) distribution, an extension of the classical Poisson-Gamma formulation, to model a discrete stochastic process at multiple resolutions. We demonstrate that the probability model has a closed-form posterior, enabling efficient and accurate linear-time prediction over any length scale after the parameters of the model have been inferred a single time. We apply our framework to model mutation rates in tumors and show that model parameters can be accurately inferred from high-dimensional epigenetic data using a convolutional neural network, Gaussian process, and maximum-likelihood estimation. Our method is both more accurate and more efficient than existing models over a large range of length scales. We demonstrate the usefulness of multi-resolution modeling by detecting genomic elements that drive tumor emergence and are of vastly differing sizes.","PeriodicalId":521258,"journal":{"name":"... International Conference on Learning Representations","volume":"2021 ","pages":"13150-13173"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13058938/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147648802","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

SPARSE ENCODING FOR MORE-INTERPRETABLE FEATURE-SELECTING REPRESENTATIONS IN PROBABILISTIC MATRIX FACTORIZATION. 稀疏编码用于概率矩阵分解中更可解释的特征选择表示。

... International Conference on Learning Representations Pub Date : 2021-05-01

Joshua C Chang, Patrick A Fletcher, Jungmin Han, Ted L Chang, Shashaank Vattikuti, Bart Desmet, Ayah Zirikly, Carson Chow

{"title":"SPARSE ENCODING FOR MORE-INTERPRETABLE FEATURE-SELECTING REPRESENTATIONS IN PROBABILISTIC MATRIX FACTORIZATION.","authors":"Joshua C Chang, Patrick A Fletcher, Jungmin Han, Ted L Chang, Shashaank Vattikuti, Bart Desmet, Ayah Zirikly, Carson Chow","doi":"","DOIUrl":"","url":null,"abstract":"Dimensionality reduction methods for count data are critical to a wide range of applications in medical informatics and other fields where model interpretability is paramount. For such data, hierarchical Poisson matrix factorization (HPF) and other sparse probabilistic non-negative matrix factorization (NMF) methods are considered to be interpretable generative models. They consist of sparse transformations for decoding their learned representations into predictions. However, sparsity in representation decoding does not necessarily imply sparsity in the encoding of representations from the original data features. HPF is often incorrectly interpreted in the literature as if it possesses encoder sparsity. The distinction between decoder sparsity and encoder sparsity is subtle but important. Due to the lack of encoder sparsity, HPF does not possess the column-clustering property of classical NMF - the factor loading matrix does not sufficiently define how each factor is formed from the original features. We address this deficiency by self-consistently enforcing encoder sparsity, using a generalized additive model (GAM), thereby allowing one to relate each representation coordinate to a subset of the original data features. In doing so, the method also gains the ability to perform feature selection. We demonstrate our method on simulated data and give an example of how encoder sparsity is of practical use in a concrete application of representing inpatient comorbidities in Medicare patients.","PeriodicalId":521258,"journal":{"name":"... International Conference on Learning Representations","volume":"2021 ","pages":"4133-4147"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12947065/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147328838","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

Multi-Time Attention Networks for Irregularly Sampled Time Series. 不规则采样时间序列的多时间注意网络。

... International Conference on Learning Representations Pub Date : 2021-05-01

Satya Narayan Shukla, Benjamin M Marlin

引用次数: 0

Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation. 变色龙：快速深度神经网络编译的自适应代码优化。

... International Conference on Learning Representations Pub Date : 2020-04-01

Byung Hoon Ahn, Prannoy Pilligundla, Amir Yazdanbakhsh, Hadi Esmaeilzadeh

{"title":"Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation.","authors":"Byung Hoon Ahn, Prannoy Pilligundla, Amir Yazdanbakhsh, Hadi Esmaeilzadeh","doi":"","DOIUrl":"","url":null,"abstract":"Achieving faster execution with shorter compilation time can foster further diversity and innovation in neural networks. However, the current paradigm of executing neural networks either relies on hand-optimized libraries, traditional compilation heuristics, or very recently genetic algorithms and other stochastic methods. These methods suffer from frequent costly hardware measurements rendering them not only too time consuming but also suboptimal. As such, we devise a solution that can learn to quickly adapt to a previously unseen design space for code optimization, both accelerating the search and improving the output performance. This solution dubbed Chameleon leverages reinforcement learning whose solution takes fewer steps to converge, and develops an adaptive sampling algorithm that not only focuses on the costly samples (real hardware measurements) on representative points but also uses a domain-knowledge inspired logic to improve the samples itself. Experimentation with real hardware shows that Chameleon provides 4.45×speed up in optimization time over AutoTVM, while also improving inference time of the modern deep networks by 5.6%.","PeriodicalId":521258,"journal":{"name":"... International Conference on Learning Representations","volume":"2020 ","pages":"5946-5962"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12931927/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147314765","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

Learning protein sequence embeddings using information from structure. 利用结构信息学习蛋白质序列嵌入。

... International Conference on Learning Representations Pub Date : 2019-05-01

Tristan Bepler, Bonnie Berger

{"title":"Learning protein sequence embeddings using information from structure.","authors":"Tristan Bepler, Bonnie Berger","doi":"","DOIUrl":"","url":null,"abstract":"Inferring the structural properties of a protein from its amino acid sequence is a challenging yet important problem in biology. Structures are not known for the vast majority of protein sequences, but structure is critical for understanding function. Existing approaches for detecting structural similarity between proteins from sequence are unable to recognize and exploit structural patterns when sequences have diverged too far, limiting our ability to transfer knowledge between structurally related proteins. We newly approach this problem through the lens of representation learning. We introduce a framework that maps any protein sequence to a sequence of vector embeddings - one per amino acid position - that encode structural information. We train bidirectional long short-term memory (LSTM) models on protein sequences with a two-part feedback mechanism that incorporates information from (i) global structural similarity between proteins and (ii) pairwise residue contact maps for individual proteins. To enable learning from structural similarity information, we define a novel similarity measure between arbitrary-length sequences of vector embeddings based on a soft symmetric alignment (SSA) between them. Our method is able to learn useful position-specific embeddings despite lacking direct observations of position-level correspondence between sequences. We show empirically that our multi-task framework outperforms other sequence-based methods and even a top-performing structure-based alignment method when predicting structural similarity, our goal. Finally, we demonstrate that our learned embeddings can be transferred to other protein sequence problems, improving the state-of-the-art in transmembrane domain prediction.","PeriodicalId":521258,"journal":{"name":"... International Conference on Learning Representations","volume":"2019 ","pages":"3162-3178"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12947628/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147328927","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