Advances in neural information processing systems最新文献_第10页

Sequential Causal Imitation Learning with Unobserved Confounders 未观察混杂因素的顺序因果模仿学习

Advances in neural information processing systems Pub Date : 2022-08-12 DOI: 10.48550/arXiv.2208.06276

D. Kumor, Junzhe Zhang

{"title":"Sequential Causal Imitation Learning with Unobserved Confounders","authors":"D. Kumor, Junzhe Zhang","doi":"10.48550/arXiv.2208.06276","DOIUrl":"https://doi.org/10.48550/arXiv.2208.06276","url":null,"abstract":"\"Monkey see monkey do\"is an age-old adage, referring to na\"ive imitation without a deep understanding of a system's underlying mechanics. Indeed, if a demonstrator has access to information unavailable to the imitator (monkey), such as a different set of sensors, then no matter how perfectly the imitator models its perceived environment (See), attempting to reproduce the demonstrator's behavior (Do) can lead to poor outcomes. Imitation learning in the presence of a mismatch between demonstrator and imitator has been studied in the literature under the rubric of causal imitation learning (Zhang et al., 2020), but existing solutions are limited to single-stage decision-making. This paper investigates the problem of causal imitation learning in sequential settings, where the imitator must make multiple decisions per episode. We develop a graphical criterion that is necessary and sufficient for determining the feasibility of causal imitation, providing conditions when an imitator can match a demonstrator's performance despite differing capabilities. Finally, we provide an efficient algorithm for determining imitability and corroborate our theory with simulations.","PeriodicalId":72099,"journal":{"name":"Advances in neural information processing systems","volume":"9 1","pages":"14669-14680"},"PeriodicalIF":0.0,"publicationDate":"2022-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77907059","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: 24

Information bottleneck theory of high-dimensional regression: relevancy, efficiency and optimality 高维回归的信息瓶颈理论：相关性、有效性和最优性

Advances in neural information processing systems Pub Date : 2022-08-08 DOI: 10.48550/arXiv.2208.03848

Wave Ngampruetikorn, David J. Schwab

引用次数: 3

Gradual Domain Adaptation without Indexed Intermediate Domains 无索引中间域的逐步域自适应

Advances in neural information processing systems Pub Date : 2022-07-11 DOI: 10.48550/arXiv.2207.04587

Hong-You Chen, Wei-Lun Chao

{"title":"Gradual Domain Adaptation without Indexed Intermediate Domains","authors":"Hong-You Chen, Wei-Lun Chao","doi":"10.48550/arXiv.2207.04587","DOIUrl":"https://doi.org/10.48550/arXiv.2207.04587","url":null,"abstract":"The effectiveness of unsupervised domain adaptation degrades when there is a large discrepancy between the source and target domains. Gradual domain adaptation (GDA) is one promising way to mitigate such an issue, by leveraging additional unlabeled data that gradually shift from the source to the target. Through sequentially adapting the model along the\"indexed\"intermediate domains, GDA substantially improves the overall adaptation performance. In practice, however, the extra unlabeled data may not be separated into intermediate domains and indexed properly, limiting the applicability of GDA. In this paper, we investigate how to discover the sequence of intermediate domains when it is not already available. Concretely, we propose a coarse-to-fine framework, which starts with a coarse domain discovery step via progressive domain discriminator training. This coarse domain sequence then undergoes a fine indexing step via a novel cycle-consistency loss, which encourages the next intermediate domain to preserve sufficient discriminative knowledge of the current intermediate domain. The resulting domain sequence can then be used by a GDA algorithm. On benchmark data sets of GDA, we show that our approach, which we name Intermediate DOmain Labeler (IDOL), can lead to comparable or even better adaptation performance compared to the pre-defined domain sequence, making GDA more applicable and robust to the quality of domain sequences. Codes are available at https://github.com/hongyouc/IDOL.","PeriodicalId":72099,"journal":{"name":"Advances in neural information processing systems","volume":"11 1","pages":"8201-8214"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82513404","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: 23

Manifold Interpolating Optimal-Transport Flows for Trajectory Inference 轨迹推断的流形插值最优输运流

Advances in neural information processing systems Pub Date : 2022-06-29 DOI: 10.48550/arXiv.2206.14928

G. Huguet, D. S. Magruder, O. Fasina, Alexander Tong, Manik Kuchroo, Guy Wolf, Smita Krishnaswamy

{"title":"Manifold Interpolating Optimal-Transport Flows for Trajectory Inference","authors":"G. Huguet, D. S. Magruder, O. Fasina, Alexander Tong, Manik Kuchroo, Guy Wolf, Smita Krishnaswamy","doi":"10.48550/arXiv.2206.14928","DOIUrl":"https://doi.org/10.48550/arXiv.2206.14928","url":null,"abstract":"We present a method called Manifold Interpolating Optimal-Transport Flow (MIOFlow) that learns stochastic, continuous population dynamics from static snapshot samples taken at sporadic timepoints. MIOFlow combines dynamic models, manifold learning, and optimal transport by training neural ordinary differential equations (Neural ODE) to interpolate between static population snapshots as penalized by optimal transport with manifold ground distance. Further, we ensure that the flow follows the geometry by operating in the latent space of an autoencoder that we call a geodesic autoencoder (GAE). In GAE the latent space distance between points is regularized to match a novel multiscale geodesic distance on the data manifold that we define. We show that this method is superior to normalizing flows, Schrödinger bridges and other generative models that are designed to flow from noise to data in terms of interpolating between populations. Theoretically, we link these trajectories with dynamic optimal transport. We evaluate our method on simulated data with bifurcations and merges, as well as scRNA-seq data from embryoid body differentiation, and acute myeloid leukemia treatment.","PeriodicalId":72099,"journal":{"name":"Advances in neural information processing systems","volume":"35 1","pages":"29705-29718"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41508648","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: 16

Distinguishing Learning Rules with Brain Machine Interfaces 用脑机接口区分学习规则

Advances in neural information processing systems Pub Date : 2022-06-27 DOI: 10.48550/arXiv.2206.13448

Jacob P. Portes, Christian Schmid, J. Murray

{"title":"Distinguishing Learning Rules with Brain Machine Interfaces","authors":"Jacob P. Portes, Christian Schmid, J. Murray","doi":"10.48550/arXiv.2206.13448","DOIUrl":"https://doi.org/10.48550/arXiv.2206.13448","url":null,"abstract":"Despite extensive theoretical work on biologically plausible learning rules, clear evidence about whether and how such rules are implemented in the brain has been difficult to obtain. We consider biologically plausible supervised- and reinforcement-learning rules and ask whether changes in network activity during learning can be used to determine which learning rule is being used. Supervised learning requires a credit-assignment model estimating the mapping from neural activity to behavior, and, in a biological organism, this model will inevitably be an imperfect approximation of the ideal mapping, leading to a bias in the direction of the weight updates relative to the true gradient. Reinforcement learning, on the other hand, requires no credit-assignment model and tends to make weight updates following the true gradient direction. We derive a metric to distinguish between learning rules by observing changes in the network activity during learning, given that the mapping from brain to behavior is known by the experimenter. Because brain-machine interface (BMI) experiments allow for precise knowledge of this mapping, we model a cursor-control BMI task using recurrent neural networks, showing that learning rules can be distinguished in simulated experiments using only observations that a neuroscience experimenter would plausibly have access to.","PeriodicalId":72099,"journal":{"name":"Advances in neural information processing systems","volume":"35 1","pages":"25937-25950"},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47202636","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: 3

OpenSRH: optimizing brain tumor surgery using intraoperative stimulated Raman histology openrh:利用术中刺激拉曼组织学优化脑肿瘤手术

Advances in neural information processing systems Pub Date : 2022-06-16 DOI: 10.48550/arXiv.2206.08439

Cheng Jiang, Asadur Chowdury, X. Hou, A. Kondepudi, C. Freudiger, Kyle S. Conway, S. Camelo-Piragua, D. Orringer, Ho Hin Lee, Todd C. Hollon

{"title":"OpenSRH: optimizing brain tumor surgery using intraoperative stimulated Raman histology","authors":"Cheng Jiang, Asadur Chowdury, X. Hou, A. Kondepudi, C. Freudiger, Kyle S. Conway, S. Camelo-Piragua, D. Orringer, Ho Hin Lee, Todd C. Hollon","doi":"10.48550/arXiv.2206.08439","DOIUrl":"https://doi.org/10.48550/arXiv.2206.08439","url":null,"abstract":"Accurate intraoperative diagnosis is essential for providing safe and effective care during brain tumor surgery. Our standard-of-care diagnostic methods are time, resource, and labor intensive, which restricts access to optimal surgical treatments. To address these limitations, we propose an alternative workflow that combines stimulated Raman histology (SRH), a rapid optical imaging method, with deep learning-based automated interpretation of SRH images for intraoperative brain tumor diagnosis and real-time surgical decision support. Here, we present OpenSRH, the first public dataset of clinical SRH images from 300+ brain tumors patients and 1300+ unique whole slide optical images. OpenSRH contains data from the most common brain tumors diagnoses, full pathologic annotations, whole slide tumor segmentations, raw and processed optical imaging data for end-to-end model development and validation. We provide a framework for patch-based whole slide SRH classification and inference using weak (i.e. patient-level) diagnostic labels. Finally, we benchmark two computer vision tasks: multiclass histologic brain tumor classification and patch-based contrastive representation learning. We hope OpenSRH will facilitate the clinical translation of rapid optical imaging and real-time ML-based surgical decision support in order to improve the access, safety, and efficacy of cancer surgery in the era of precision medicine. Dataset access, code, and benchmarks are available at https://opensrh.mlins.org.","PeriodicalId":72099,"journal":{"name":"Advances in neural information processing systems","volume":"35 DB 1","pages":"28502-28516"},"PeriodicalIF":0.0,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47074390","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: 3

Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification 从NeurIPS 2021 MetaDL挑战中获得的经验教训:无情景元学习的骨干微调在少量学习图像分类中占主导地位

Advances in neural information processing systems Pub Date : 2022-06-15 DOI: 10.48550/arXiv.2206.08138

Adrian El Baz, André Carvalho, Hong Chen, Fábio Ferreira, H. Gouk, S. Hu, F. Hutter, Zhengying Liu, F. Mohr, J. V. Rijn, Xin Wang, Isabelle M Guyon

{"title":"Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification","authors":"Adrian El Baz, André Carvalho, Hong Chen, Fábio Ferreira, H. Gouk, S. Hu, F. Hutter, Zhengying Liu, F. Mohr, J. V. Rijn, Xin Wang, Isabelle M Guyon","doi":"10.48550/arXiv.2206.08138","DOIUrl":"https://doi.org/10.48550/arXiv.2206.08138","url":null,"abstract":"Although deep neural networks are capable of achieving performance superior to humans on various tasks, they are notorious for requiring large amounts of data and computing resources, restricting their success to domains where such resources are available. Metalearning methods can address this problem by transferring knowledge from related tasks, thus reducing the amount of data and computing resources needed to learn new tasks. We organize the MetaDL competition series, which provide opportunities for research groups all over the world to create and experimentally assess new meta-(deep)learning solutions for real problems. In this paper, authored collaboratively between the competition organizers and the top-ranked participants, we describe the design of the competition, the datasets, the best experimental results, as well as the top-ranked methods in the NeurIPS 2021 challenge, which attracted 15 active teams who made it to the final phase (by outperforming the baseline), making over 100 code submissions during the feedback phase. The solutions of the top participants have been open-sourced. The lessons learned include that learning good representations is essential for effective transfer learning.","PeriodicalId":72099,"journal":{"name":"Advances in neural information processing systems","volume":"12 1","pages":"80-96"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78442387","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: 8

A Benchmark for Compositional Visual Reasoning 构成视觉推理的基准

Advances in neural information processing systems Pub Date : 2022-06-11 DOI: 10.48550/arXiv.2206.05379

Aimen Zerroug, Mohit Vaishnav, Julien Colin, Sebastian Musslick, Thomas Serre

{"title":"A Benchmark for Compositional Visual Reasoning","authors":"Aimen Zerroug, Mohit Vaishnav, Julien Colin, Sebastian Musslick, Thomas Serre","doi":"10.48550/arXiv.2206.05379","DOIUrl":"https://doi.org/10.48550/arXiv.2206.05379","url":null,"abstract":"A fundamental component of human vision is our ability to parse complex visual scenes and judge the relations between their constituent objects. AI benchmarks for visual reasoning have driven rapid progress in recent years with state-of-the-art systems now reaching human accuracy on some of these benchmarks. Yet, there remains a major gap between humans and AI systems in terms of the sample efficiency with which they learn new visual reasoning tasks. Humans' remarkable efficiency at learning has been at least partially attributed to their ability to harness compositionality - allowing them to efficiently take advantage of previously gained knowledge when learning new tasks. Here, we introduce a novel visual reasoning benchmark, Compositional Visual Relations (CVR), to drive progress towards the development of more data-efficient learning algorithms. We take inspiration from fluid intelligence and non-verbal reasoning tests and describe a novel method for creating compositions of abstract rules and generating image datasets corresponding to these rules at scale. Our proposed benchmark includes measures of sample efficiency, generalization, compositionality, and transfer across task rules. We systematically evaluate modern neural architectures and find that convolutional architectures surpass transformer-based architectures across all performance measures in most data regimes. However, all computational models are much less data efficient than humans, even after learning informative visual representations using self-supervision. Overall, we hope our challenge will spur interest in developing neural architectures that can learn to harness compositionality for more efficient learning.","PeriodicalId":72099,"journal":{"name":"Advances in neural information processing systems","volume":"35 DB 1","pages":"29776-29788"},"PeriodicalIF":0.0,"publicationDate":"2022-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45131748","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: 7

Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction 用于链路预测的邻域重叠感知图神经网络

Advances in neural information processing systems Pub Date : 2022-06-09 DOI: 10.48550/arXiv.2206.04216

Seongjun Yun, Seoyoon Kim, Junhyun Lee, Jaewoo Kang, Hyunwoo J. Kim

{"title":"Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction","authors":"Seongjun Yun, Seoyoon Kim, Junhyun Lee, Jaewoo Kang, Hyunwoo J. Kim","doi":"10.48550/arXiv.2206.04216","DOIUrl":"https://doi.org/10.48550/arXiv.2206.04216","url":null,"abstract":"Graph Neural Networks (GNNs) have been widely applied to various fields for learning over graph-structured data. They have shown significant improvements over traditional heuristic methods in various tasks such as node classification and graph classification. However, since GNNs heavily rely on smoothed node features rather than graph structure, they often show poor performance than simple heuristic methods in link prediction where the structural information, e.g., overlapped neighborhoods, degrees, and shortest paths, is crucial. To address this limitation, we propose Neighborhood Overlap-aware Graph Neural Networks (Neo-GNNs) that learn useful structural features from an adjacency matrix and estimate overlapped neighborhoods for link prediction. Our Neo-GNNs generalize neighborhood overlap-based heuristic methods and handle overlapped multi-hop neighborhoods. Our extensive experiments on Open Graph Benchmark datasets (OGB) demonstrate that Neo-GNNs consistently achieve state-of-the-art performance in link prediction. Our code is publicly available at https://github.com/seongjunyun/Neo_GNNs.","PeriodicalId":72099,"journal":{"name":"Advances in neural information processing systems","volume":"25 1","pages":"13683-13694"},"PeriodicalIF":0.0,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73321402","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: 42

Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis 用于纵向一致神经图像分析的局部时空表示学习

Advances in neural information processing systems Pub Date : 2022-06-09 DOI: 10.48550/arXiv.2206.04281

Mengwei Ren, Neel Dey, M. Styner, K. Botteron, G. Gerig

{"title":"Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis","authors":"Mengwei Ren, Neel Dey, M. Styner, K. Botteron, G. Gerig","doi":"10.48550/arXiv.2206.04281","DOIUrl":"https://doi.org/10.48550/arXiv.2206.04281","url":null,"abstract":"Recent self-supervised advances in medical computer vision exploit the global and local anatomical self-similarity for pretraining prior to downstream tasks such as segmentation. However, current methods assume i.i.d. image acquisition, which is invalid in clinical study designs where follow-up longitudinal scans track subject-specific temporal changes. Further, existing self-supervised methods for medically-relevant image-to-image architectures exploit only spatial or temporal self-similarity and do so via a loss applied only at a single image-scale, with naive multi-scale spatiotemporal extensions collapsing to degenerate solutions. To these ends, this paper makes two contributions: (1) It presents a local and multi-scale spatiotemporal representation learning method for image-to-image architectures trained on longitudinal images. It exploits the spatiotemporal self-similarity of learned multi-scale intra-subject image features for pretraining and develops several feature-wise regularizations that avoid degenerate representations; (2) During finetuning, it proposes a surprisingly simple self-supervised segmentation consistency regularization to exploit intra-subject correlation. Benchmarked across various segmentation tasks, the proposed framework outperforms both well-tuned randomly-initialized baselines and current self-supervised techniques designed for both i.i.d. and longitudinal datasets. These improvements are demonstrated across both longitudinal neurodegenerative adult MRI and developing infant brain MRI and yield both higher performance and longitudinal consistency.","PeriodicalId":72099,"journal":{"name":"Advances in neural information processing systems","volume":"35 1","pages":"13541-13556"},"PeriodicalIF":0.0,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44474900","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: 9