Advances in neural information processing systems最新文献_第3页

Contrastive-Equivariant Self-Supervised Learning Improves Alignment with Primate Visual Area IT. 对比等变自监督学习提高了灵长类动物视觉区域信息技术的一致性。

Advances in neural information processing systems Pub Date : 2024-01-01

Thomas Yerxa, Jenelle Feather, Eero P Simoncelli, SueYeon Chung

{"title":"Contrastive-Equivariant Self-Supervised Learning Improves Alignment with Primate Visual Area IT.","authors":"Thomas Yerxa, Jenelle Feather, Eero P Simoncelli, SueYeon Chung","doi":"","DOIUrl":"","url":null,"abstract":"Models trained with self-supervised learning objectives have recently matched or surpassed models trained with traditional supervised object recognition in their ability to predict neural responses of object-selective neurons in the primate visual system. A self-supervised learning objective is arguably a more biologically plausible organizing principle, as the optimization does not require a large number of labeled examples. However, typical self-supervised objectives may result in network representations that are overly invariant to changes in the input. Here, we show that a representation with structured variability to input transformations is better aligned with known features of visual perception and neural computation. We introduce a novel framework for converting standard invariant SSL losses into \"contrastive-equivariant\" versions that encourage preservation of input transformations without supervised access to the transformation parameters. We demonstrate that our proposed method systematically increases the ability of models to predict responses in macaque inferior temporal cortex. Our results demonstrate the promise of incorporating known features of neural computation into task-optimization for building better models of visual cortex.","PeriodicalId":72099,"journal":{"name":"Advances in neural information processing systems","volume":"37 ","pages":"96045-96070"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12058038/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144065347","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

Generalizing CNNs to Graphs with Learnable Neighborhood Quantization. 将cnn推广到具有可学习邻域量化的图。

Advances in neural information processing systems Pub Date : 2024-01-01

Isaac Osafo Nkansah, Neil Gallagher, Ruchi Sandilya, Conor Liston, Logan Grosenick

{"title":"Generalizing CNNs to Graphs with Learnable Neighborhood Quantization.","authors":"Isaac Osafo Nkansah, Neil Gallagher, Ruchi Sandilya, Conor Liston, Logan Grosenick","doi":"","DOIUrl":"","url":null,"abstract":"Convolutional neural networks (CNNs) have led to a revolution in analyzing array data. However, many important sources of data, such as biological and social networks, are naturally structured as graphs rather than arrays, making the design of graph neural network (GNN) architectures that retain the strengths of CNNs an active and exciting area of research. Here, we introduce Quantized Graph Convolution Networks (QGCNs), the first framework for GNNs that formally and directly extends CNNs to graphs. QGCNs do this by decomposing the convolution operation into non-overlapping sub-kernels, allowing them to fit graph data while reducing to a 2D CNN layer on array data. We generalize this approach to graphs of arbitrary size and dimension by approaching sub-kernel assignment as a learnable multinomial assignment problem. Integrating this approach into a residual network architecture, we demonstrate performance that matches or exceeds other state-of-the-art GNNs on benchmark graph datasets and for predicting properties of nonlinear dynamics on a new finite element graph dataset. In summary, QGCNs are a novel GNN framework that generalizes CNNs and their strengths to graph data, allowing for more accurate and expressive models.","PeriodicalId":72099,"journal":{"name":"Advances in neural information processing systems","volume":"37 ","pages":"82318-82349"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12124760/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144200955","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

pcaGAN: Improving Posterior-Sampling cGANs via Principal Component Regularization. 通过主成分正则化改进后验抽样cgan。

Advances in neural information processing systems Pub Date : 2024-01-01

Matthew C Bendel, Rizwan Ahmad, Philip Schniter

引用次数: 0

A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysis. 教科书补救领域转移：医学图像分析的知识先验。

Advances in neural information processing systems Pub Date : 2024-01-01

Yue Yang, Mona Gandhi, Yufei Wang, Yifan Wu, Michael S Yao, Chris Callison-Burch, James C Gee, Mark Yatskar

{"title":"A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysis.","authors":"Yue Yang, Mona Gandhi, Yufei Wang, Yifan Wu, Michael S Yao, Chris Callison-Burch, James C Gee, Mark Yatskar","doi":"","DOIUrl":"","url":null,"abstract":"While deep networks have achieved broad success in analyzing natural images, when applied to medical scans, they often fail in unexpected situations. We investigate this challenge and focus on model sensitivity to domain shifts, such as data sampled from different hospitals or data confounded by demographic variables such as sex, race, etc, in the context of chest X-rays and skin lesion images. A key finding we show empirically is that existing visual backbones lack an appropriate prior from the architecture for reliable generalization in these settings. Taking inspiration from medical training, we propose giving deep networks a prior grounded in explicit medical knowledge communicated in natural language. To this end, we introduce Knowledge-enhanced Bottlenecks (KnoBo), a class of concept bottleneck models that incorporates knowledge priors that constrain it to reason with clinically relevant factors found in medical textbooks or PubMed. KnoBo uses retrieval-augmented language models to design an appropriate concept space and an automatic training procedure for recognizing the concept. We evaluate different resources of knowledge and recognition architectures on a broad range of domain shifts across 20 datasets. In our comprehensive evaluation with two imaging modalities, KnoBo outperforms fine-tuned models on confounded datasets by 32.4% on average. Finally, evaluations reveal that PubMed is a promising resource for making medical models less sensitive to domain shift, outperforming other resources on both diversity of information and final prediction performance.","PeriodicalId":72099,"journal":{"name":"Advances in neural information processing systems","volume":"37 ","pages":"90683-90713"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12064272/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060948","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

Model-based inference of synaptic plasticity rules. 基于模型的突触可塑性规则推理。

Advances in neural information processing systems Pub Date : 2024-01-01

Yash Mehta, Danil Tyulmankov, Adithya E Rajagopalan, Glenn C Turner, James E Fitzgerald, Jan Funke

{"title":"Model-based inference of synaptic plasticity rules.","authors":"Yash Mehta, Danil Tyulmankov, Adithya E Rajagopalan, Glenn C Turner, James E Fitzgerald, Jan Funke","doi":"","DOIUrl":"","url":null,"abstract":"Inferring the synaptic plasticity rules that govern learning in the brain is a key challenge in neuroscience. We present a novel computational method to infer these rules from experimental data, applicable to both neural and behavioral data. Our approach approximates plasticity rules using a parameterized function, employing either truncated Taylor series for theoretical interpretability or multilayer perceptrons. These plasticity parameters are optimized via gradient descent over entire trajectories to align closely with observed neural activity or behavioral learning dynamics. This method can uncover complex rules that induce long nonlinear time dependencies, particularly involving factors like postsynaptic activity and current synaptic weights. We validate our approach through simulations, successfully recovering established rules such as Oja's, as well as more intricate plasticity rules with reward-modulated terms. We assess the robustness of our technique to noise and apply it to behavioral data from Drosophila in a probabilistic reward-learning experiment. Notably, our findings reveal an active forgetting component in reward learning in flies, improving predictive accuracy over previous models. This modeling framework offers a promising new avenue for elucidating the computational principles of synaptic plasticity and learning in the brain.","PeriodicalId":72099,"journal":{"name":"Advances in neural information processing systems","volume":"37 ","pages":"48519-48540"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12257422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144638812","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

Active learning of neural population dynamics using two-photon holographic optogenetics. 利用双光子全息光遗传学主动学习神经种群动态。

Advances in neural information processing systems Pub Date : 2024-01-01

Andrew Wagenmaker, Lu Mi, Marton Rozsa, Matthew S Bull, Karel Svoboda, Kayvon Daie, Matthew D Golub, Kevin Jamieson

{"title":"Active learning of neural population dynamics using two-photon holographic optogenetics.","authors":"Andrew Wagenmaker, Lu Mi, Marton Rozsa, Matthew S Bull, Karel Svoboda, Kayvon Daie, Matthew D Golub, Kevin Jamieson","doi":"","DOIUrl":"","url":null,"abstract":"Recent advances in techniques for monitoring and perturbing neural populations have greatly enhanced our ability to study circuits in the brain. In particular, two-photon holographic optogenetics now enables precise photostimulation of experimenter-specified groups of individual neurons, while simultaneous two-photon calcium imaging enables the measurement of ongoing and induced activity across the neural population. Despite the enormous space of potential photostimulation patterns and the time-consuming nature of photostimulation experiments, very little algorithmic work has been done to determine the most effective photostimulation patterns for identifying the neural population dynamics. Here, we develop methods to efficiently select which neurons to stimulate such that the resulting neural responses will best inform a dynamical model of the neural population activity. Using neural population responses to photostimulation in mouse motor cortex, we demonstrate the efficacy of a low-rank linear dynamical systems model, and develop an active learning procedure which takes advantage of low-rank structure to determine informative photostimulation patterns. We demonstrate our approach on both real and synthetic data, obtaining in some cases as much as a two-fold reduction in the amount of data required to reach a given predictive power. Our active stimulation design method is based on a novel active learning procedure for low-rank regression, which may be of independent interest.","PeriodicalId":72099,"journal":{"name":"Advances in neural information processing systems","volume":"37 ","pages":"31659-31687"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12466919/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145187631","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

Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Trees. 使用最佳阶次得分搜索和生长-收缩树快速、可扩展、准确地发现 DAG。

Advances in neural information processing systems Pub Date : 2023-12-01 Epub Date: 2024-05-30

Bryan Andrews, Joseph Ramsey, Rubén Sánchez-Romero, Jazmin Camchong, Erich Kummerfeld

{"title":"Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Trees.","authors":"Bryan Andrews, Joseph Ramsey, Rubén Sánchez-Romero, Jazmin Camchong, Erich Kummerfeld","doi":"","DOIUrl":"","url":null,"abstract":"Learning graphical conditional independence structures is an important machine learning problem and a cornerstone of causal discovery. However, the accuracy and execution time of learning algorithms generally struggle to scale to problems with hundreds of highly connected variables-for instance, recovering brain networks from fMRI data. We introduce the best order score search (BOSS) and grow-shrink trees (GSTs) for learning directed acyclic graphs (DAGs) in this paradigm. BOSS greedily searches over permutations of variables, using GSTs to construct and score DAGs from permutations. GSTs efficiently cache scores to eliminate redundant calculations. BOSS achieves state-of-the-art performance in accuracy and execution time, comparing favorably to a variety of combinatorial and gradient-based learning algorithms under a broad range of conditions. To demonstrate its practicality, we apply BOSS to two sets of resting-state fMRI data: simulated data with pseudo-empirical noise distributions derived from randomized empirical fMRI cortical signals and clinical data from 3T fMRI scans processed into cortical parcels. BOSS is available for use within the TETRAD project which includes Python and R wrappers.","PeriodicalId":72099,"journal":{"name":"Advances in neural information processing systems","volume":"36 ","pages":"63945-63956"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11393735/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302203","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

A Hierarchical Spatial Transformer for Massive Point Samples in Continuous Space. 连续空间中大量点样本的分层空间变换器

Advances in neural information processing systems Pub Date : 2023-12-01

Wenchong He, Zhe Jiang, Tingsong Xiao, Zelin Xu, Shigang Chen, Ronald Fick, Miles Medina, Christine Angelini

{"title":"A Hierarchical Spatial Transformer for Massive Point Samples in Continuous Space.","authors":"Wenchong He, Zhe Jiang, Tingsong Xiao, Zelin Xu, Shigang Chen, Ronald Fick, Miles Medina, Christine Angelini","doi":"","DOIUrl":"","url":null,"abstract":"Transformers are widely used deep learning architectures. Existing transformers are mostly designed for sequences (texts or time series), images or videos, and graphs. This paper proposes a novel transformer model for massive (up to a million) point samples in continuous space. Such data are ubiquitous in environment sciences (e.g., sensor observations), numerical simulations (e.g., particle-laden flow, astrophysics), and location-based services (e.g., POIs and trajectories). However, designing a transformer for massive spatial points is non-trivial due to several challenges, including implicit long-range and multi-scale dependency on irregular points in continuous space, a non-uniform point distribution, the potential high computational costs of calculating all-pair attention across massive points, and the risks of over-confident predictions due to varying point density. To address these challenges, we propose a new hierarchical spatial transformer model, which includes multi-resolution representation learning within a quad-tree hierarchy and efficient spatial attention via coarse approximation. We also design an uncertainty quantification branch to estimate prediction confidence related to input feature noise and point sparsity. We provide a theoretical analysis of computational time complexity and memory costs. Extensive experiments on both real-world and synthetic datasets show that our method outperforms multiple baselines in prediction accuracy and our model can scale up to one million points on one NVIDIA A100 GPU. The code is available at https://github.com/spatialdatasciencegroup/HST.","PeriodicalId":72099,"journal":{"name":"Advances in neural information processing systems","volume":"36 ","pages":"33365-33378"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11094554/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140945848","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

Coupled Reconstruction of Cortical Surfaces by Diffeomorphic Mesh Deformation. 通过差构网格变形耦合重建皮质表面

Advances in neural information processing systems Pub Date : 2023-12-01

Hao Zheng, Hongming Li, Yong Fan

{"title":"Coupled Reconstruction of Cortical Surfaces by Diffeomorphic Mesh Deformation.","authors":"Hao Zheng, Hongming Li, Yong Fan","doi":"","DOIUrl":"","url":null,"abstract":"Accurate reconstruction of cortical surfaces from brain magnetic resonance images (MRIs) remains a challenging task due to the notorious partial volume effect in brain MRIs and the cerebral cortex's thin and highly folded patterns. Although many promising deep learning-based cortical surface reconstruction methods have been developed, they typically fail to model the interdependence between inner (white matter) and outer (pial) cortical surfaces, which can help generate cortical surfaces with spherical topology. To robustly reconstruct the cortical surfaces with topological correctness, we develop a new deep learning framework to jointly reconstruct the inner, outer, and their in-between (midthickness) surfaces and estimate cortical thickness directly from 3D MRIs. Our method first estimates the midthickness surface and then learns three diffeomorphic flows jointly to optimize the midthickness surface and deform it inward and outward to the inner and outer cortical surfaces respectively, regularized by topological correctness. Our method also outputs a cortex thickness value for each surface vertex, estimated from its diffeomorphic deformation trajectory. Our method has been evaluated on two large-scale neuroimaging datasets, including ADNI and OASIS, achieving state-of-the-art cortical surface reconstruction performance in terms of accuracy, surface regularity, and computation efficiency.","PeriodicalId":72099,"journal":{"name":"Advances in neural information processing systems","volume":"36 ","pages":"80608-80621"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11149912/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141248594","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

A Regularized Conditional GAN for Posterior Sampling in Image Recovery Problems. 图像复原问题中用于后验采样的正则化条件 GAN。

Advances in neural information processing systems Pub Date : 2023-12-01 Epub Date: 2024-05-30

Matthew C Bendel, Rizwan Ahmad, Philip Schniter

引用次数: 0