Advances in neural information processing systems最新文献_第8页

Fairness-Aware Estimation of Graphical Models. 图形模型的公平感知估计。

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

Zhuoping Zhou, Davoud Ataee Tarzanagh, Bojian Hou, Qi Long, Li Shen

引用次数: 0

Crafting Interpretable Embeddings for Language Neuroscience by Asking LLMs Questions. 通过问法学硕士问题来制作语言神经科学的可解释嵌入。

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

Vinamra Benara, Chandan Singh, John X Morris, Richard J Antonello, Ion Stoica, Alexander G Huth, Jianfeng Gao

{"title":"Crafting Interpretable Embeddings for Language Neuroscience by Asking LLMs Questions.","authors":"Vinamra Benara, Chandan Singh, John X Morris, Richard J Antonello, Ion Stoica, Alexander G Huth, Jianfeng Gao","doi":"","DOIUrl":"","url":null,"abstract":"Large language models (LLMs) have rapidly improved text embeddings for a growing array of natural-language processing tasks. However, their opaqueness and proliferation into scientific domains such as neuroscience have created a growing need for interpretability. Here, we ask whether we can obtain interpretable embeddings through LLM prompting. We introduce question-answering embeddings (QA-Emb), embeddings where each feature represents an answer to a yes/no question asked to an LLM. Training QA-Emb reduces to selecting a set of underlying questions rather than learning model weights. We use QA-Emb to flexibly generate interpretable models for predicting fMRI voxel responses to language stimuli. QA-Emb significantly outperforms an established interpretable baseline, and does so while requiring very few questions. This paves the way towards building flexible feature spaces that can concretize and evaluate our understanding of semantic brain representations. We additionally find that QA-Emb can be effectively approximated with an efficient model, and we explore broader applications in simple NLP tasks.","PeriodicalId":72099,"journal":{"name":"Advances in neural information processing systems","volume":"37 ","pages":"124137-124162"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12021422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144029876","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

Generative Adversarial Model-Based Optimization via Source Critic Regularization. 基于源批评正则化的生成对抗模型优化。

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

Michael S Yao, Yimeng Zeng, Hamsa Bastani, Jacob Gardner, James C Gee, Osbert Bastani

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

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

Topology-Aware Uncertainty for Image Segmentation. 拓扑感知图像分割的不确定性

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

Saumya Gupta, Yikai Zhang, Xiaoling Hu, Prateek Prasanna, Chao Chen

{"title":"Topology-Aware Uncertainty for Image Segmentation.","authors":"Saumya Gupta, Yikai Zhang, Xiaoling Hu, Prateek Prasanna, Chao Chen","doi":"","DOIUrl":"","url":null,"abstract":"Segmentation of curvilinear structures such as vasculature and road networks is challenging due to relatively weak signals and complex geometry/topology. To facilitate and accelerate large scale annotation, one has to adopt semi-automatic approaches such as proofreading by experts. In this work, we focus on uncertainty estimation for such tasks, so that highly uncertain, and thus error-prone structures can be identified for human annotators to verify. Unlike most existing works, which provide pixel-wise uncertainty maps, we stipulate it is crucial to estimate uncertainty in the units of topological structures, e.g., small pieces of connections and branches. To achieve this, we leverage tools from topological data analysis, specifically discrete Morse theory (DMT), to first capture the structures, and then reason about their uncertainties. To model the uncertainty, we (1) propose a joint prediction model that estimates the uncertainty of a structure while taking the neighboring structures into consideration (inter-structural uncertainty); (2) propose a novel Probabilistic DMT to model the inherent uncertainty within each structure (intra-structural uncertainty) by sampling its representations via a perturb- and-walk scheme. On various 2D and 3D datasets, our method produces better structure-wise uncertainty maps compared to existing works. Code available at https://github.com/Saumya-Gupta-26/struct-uncertainty.","PeriodicalId":72099,"journal":{"name":"Advances in neural information processing systems","volume":"36 ","pages":"8186-8207"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11526043/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142559599","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

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

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