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

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

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

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

Open Visual Knowledge Extraction via Relation-Oriented Multimodality Model Prompting. 通过面向关系的多模态模型提示进行开放式视觉知识提取

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

Hejie Cui, Xinyu Fang, Zihan Zhang, Ran Xu, Xuan Kan, Xin Liu, Yue Yu, Manling Li, Yangqiu Song, Carl Yang

{"title":"Open Visual Knowledge Extraction via Relation-Oriented Multimodality Model Prompting.","authors":"Hejie Cui, Xinyu Fang, Zihan Zhang, Ran Xu, Xuan Kan, Xin Liu, Yue Yu, Manling Li, Yangqiu Song, Carl Yang","doi":"","DOIUrl":"","url":null,"abstract":"Images contain rich relational knowledge that can help machines understand the world. Existing methods on visual knowledge extraction often rely on the pre-defined format (e.g., sub-verb-obj tuples) or vocabulary (e.g., relation types), restricting the expressiveness of the extracted knowledge. In this work, we take a first exploration to a new paradigm of open visual knowledge extraction. To achieve this, we present OpenVik which consists of an open relational region detector to detect regions potentially containing relational knowledge and a visual knowledge generator that generates format-free knowledge by prompting the large multimodality model with the detected region of interest. We also explore two data enhancement techniques for diversifying the generated format-free visual knowledge. Extensive knowledge quality evaluations highlight the correctness and uniqueness of the extracted open visual knowledge by OpenVik. Moreover, integrating our extracted knowledge across various visual reasoning applications shows consistent improvements, indicating the real-world applicability of OpenVik.","PeriodicalId":72099,"journal":{"name":"Advances in neural information processing systems","volume":"36 ","pages":"23499-23519"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11315466/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141918610","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

Understanding Deep Gradient Leakage via Inversion Influence Functions. 通过反演影响函数了解深度梯度泄漏。

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

Haobo Zhang, Junyuan Hong, Yuyang Deng, Mehrdad Mahdavi, Jiayu Zhou

{"title":"Understanding Deep Gradient Leakage via Inversion Influence Functions.","authors":"Haobo Zhang, Junyuan Hong, Yuyang Deng, Mehrdad Mahdavi, Jiayu Zhou","doi":"","DOIUrl":"","url":null,"abstract":"Deep Gradient Leakage (DGL) is a highly effective attack that recovers private training images from gradient vectors. This attack casts significant privacy challenges on distributed learning from clients with sensitive data, where clients are required to share gradients. Defending against such attacks requires but lacks an understanding of when and how privacy leakage happens, mostly because of the black-box nature of deep networks. In this paper, we propose a novel Inversion Influence Function (I2F) that establishes a closed-form connection between the recovered images and the private gradients by implicitly solving the DGL problem. Compared to directly solving DGL, I2F is scalable for analyzing deep networks, requiring only oracle access to gradients and Jacobian-vector products. We empirically demonstrate that I2F effectively approximated the DGL generally on different model architectures, datasets, modalities, attack implementations, and perturbation-based defenses. With this novel tool, we provide insights into effective gradient perturbation directions, the unfairness of privacy protection, and privacy-preferred model initialization. Our codes are provided in https://github.com/illidanlab/inversion-influence-function.","PeriodicalId":72099,"journal":{"name":"Advances in neural information processing systems","volume":"36 ","pages":"3921-3944"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11007681/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140869599","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

Brain-like Flexible Visual Inference by Harnessing Feedback-Feedforward Alignment. 利用 "反馈-前馈对齐 "实现类脑灵活视觉推理

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

Tahereh Toosi, Elias B Issa

{"title":"Brain-like Flexible Visual Inference by Harnessing Feedback-Feedforward Alignment.","authors":"Tahereh Toosi, Elias B Issa","doi":"","DOIUrl":"","url":null,"abstract":"In natural vision, feedback connections support versatile visual inference capabilities such as making sense of the occluded or noisy bottom-up sensory information or mediating pure top-down processes such as imagination. However, the mechanisms by which the feedback pathway learns to give rise to these capabilities flexibly are not clear. We propose that top-down effects emerge through alignment between feedforward and feedback pathways, each optimizing its own objectives. To achieve this co-optimization, we introduce Feedback-Feedforward Alignment (FFA), a learning algorithm that leverages feedback and feedforward pathways as mutual credit assignment computational graphs, enabling alignment. In our study, we demonstrate the effectiveness of FFA in co-optimizing classification and reconstruction tasks on widely used MNIST and CIFAR10 datasets. Notably, the alignment mechanism in FFA endows feedback connections with emergent visual inference functions, including denoising, resolving occlusions, hallucination, and imagination. Moreover, FFA offers bio-plausibility compared to traditional back-propagation (BP) methods in implementation. By repurposing the computational graph of credit assignment into a goal-driven feedback pathway, FFA alleviates weight transport problems encountered in BP, enhancing the bio-plausibility of the learning algorithm. Our study presents FFA as a promising proof-of-concept for the mechanisms underlying how feedback connections in the visual cortex support flexible visual functions. This work also contributes to the broader field of visual inference underlying perceptual phenomena and has implications for developing more biologically inspired learning algorithms.","PeriodicalId":72099,"journal":{"name":"Advances in neural information processing systems","volume":"37 ","pages":"56979-56997"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11567678/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649777","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

Fair Canonical Correlation Analysis. 公平的典型相关分析

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

Zhuoping Zhou, Davoud Ataee Tarzanagh, Bojian Hou, Boning Tong, Jia Xu, Yanbo Feng, Qi Long, Li Shen

引用次数: 0