NeurIPS
Collection
Accepted papers for NeurIPS (Conference on Neural Information Processing Systems), one dataset per year. • 13 items • Updated
title stringlengths 15 153 | paper_url stringlengths 41 45 | authors listlengths 1 21 | type stringclasses 3
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values | abstract large_stringlengths 310 2.43k | keywords listlengths 1 32 | TL;DR large_stringlengths 23 250 ⌀ | submission_number int64 1 11.7k | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
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Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning | https://openreview.net/forum?id=xXYjxli-2i | [
"Ali Taghibakhshi",
"Scott MacLachlan",
"Luke Olson",
"Matthew West"
] | Poster | null | Large sparse linear systems of equations are ubiquitous in science and engineering, such as those arising from discretizations of partial differential equations. Algebraic multigrid (AMG) methods are one of the most common methods of solving such linear systems, with an extensive body of underlying mathematical theory.... | [
"Algebraic Multigrid",
"Reinforcement Learning",
"Graph Partitioning"
] | Providing a reinforcement learning method utilizing graph neural networks for algebraic multigrid coarsening, outperforming existing algorithms. | 7,640 | 2106.01854 | title_snapshot | [
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Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time | https://openreview.net/forum?id=9pt6F8w1Jgs | [
"Feng Zhu",
"Andrew R Sedler",
"Harrison A Grier",
"Nauman Ahad",
"Mark A. Davenport",
"Matthew Kaufman",
"Andrea Giovannucci",
"Chethan Pandarinath"
] | Poster | null | Modern neural interfaces allow access to the activity of up to a million neurons within brain circuits. However, bandwidth limits often create a trade-off between greater spatial sampling (more channels or pixels) and the temporal frequency of sampling. Here we demonstrate that it is possible to obtain spatio-temporal ... | [
"Computational neuroscience",
"Systems neuroscience",
"Neural population dynamics",
"intermittent sampling",
"Electrophysiology",
"Calcium imaging",
"Brain-computer interfaces",
"Neuroscience",
"Neuroprosthetics",
"Neural coding",
"Motor control",
"Sequential autoencoders"
] | We develop a novel learning rule for backpropagating loss in neuroscientific time series data with intermittent sampling, enabling sequential autoencoders to increase spatiotemporal resolution in electrophysiology and calcium imaging datasets. | 11,666 | 2111.00070 | title_snapshot | [
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Skipping the Frame-Level: Event-Based Piano Transcription With Neural Semi-CRFs | https://openreview.net/forum?id=DGA8XbJ8FVd | [
"Yujia Yan",
"Frank Cwitkowitz",
"Zhiyao Duan"
] | Poster | null | Piano transcription systems are typically optimized to estimate pitch activity at each frame of audio. They are often followed by carefully designed heuristics and post-processing algorithms to estimate note events from the frame-level predictions. Recent methods have also framed piano transcription as a multi-task lea... | [
"Music",
"Audio",
"Piano Transcrition",
"Music Transcription",
"Semi-Markov",
"CRFs",
"Sound Event Detection",
"Music Information Retrieval"
] | We propose a novel piano transcription system that is simple, fast, and well-performing. | 5,577 | null | null | [
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Active Learning of Convex Halfspaces on Graphs | https://openreview.net/forum?id=O-fOgeI_D-B | [
"Maximilian Thiessen",
"Thomas Gärtner"
] | Poster | null | We systematically study the query complexity of learning geodesically convex halfspaces on graphs. Geodesic convexity is a natural generalisation of Euclidean convexity and allows the definition of convex sets and halfspaces on graphs. We prove an upper bound on the query complexity linear in the treewidth and the mini... | [
"active learning",
"learning theory",
"semi-supervised learning",
"transduction",
"vertex classification",
"graphs",
"convexity theory",
"geodesic convexity",
"shortest paths",
"halfspaces",
"query complexity"
] | We systematically study the query complexity of learning geodesically convex halfspaces on a vertex-labelled graph. | 9,657 | null | null | [
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Nested Variational Inference | https://openreview.net/forum?id=kBrHzFtwdp | [
"Heiko Zimmermann",
"Hao Wu",
"Babak Esmaeili",
"Jan-Willem van de Meent"
] | Poster | null | We develop nested variational inference (NVI), a family of methods that learn proposals for nested importance samplers by minimizing an forward or reverse KL divergence at each level of nesting. NVI is applicable to many commonly-used importance sampling strategies and provides a mechanism for learning intermediate den... | [
"Variational Inference",
"Importance Sampling",
"Monte Carlo methods"
] | null | 9,656 | 2106.11302 | title_snapshot | [
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Row-clustering of a Point Process-valued Matrix | https://openreview.net/forum?id=YXy_2b5wufe | [
"Lihao Yin",
"Ganggang Xu",
"Huiyan Sang",
"Yongtao Guan"
] | Poster | null | Structured point process data harvested from various platforms poses new challenges to the machine learning community. To cluster repeatedly observed marked point processes, we propose a novel mixture model of multi-level marked point processes for identifying potential heterogeneity in the observed data. Specifically,... | [
"Expectation-Solution Algorithm",
"Functional Principal Component Analysis",
"Marked Point Processes",
"Model-based Clustering",
"Semiparametric Model"
] | We propose a mixture model of multi-level marked point processes for clustering repeatedly observed marked event sequences | 11,369 | 2110.01207 | title_snapshot | [
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On learning sparse vectors from mixture of responses | https://openreview.net/forum?id=6k0bAbb6m6 | [
"Nikita Polyanskii"
] | Poster | null | In this paper, we address two learning problems. Suppose a family of $\ell$ unknown sparse vectors is fixed, where each vector has at most $k$ non-zero elements. In the first problem, we concentrate on robust learning the supports of all vectors from the family using a sequence of noisy responses. Each response to a q... | [
"sparse vectors",
"mixture of binary linear classifiers",
"1-bit compessed sensing",
"query complexity",
"noisy measurements"
] | null | 11,264 | null | null | [
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Contextual Recommendations and Low-Regret Cutting-Plane Algorithms | https://openreview.net/forum?id=45GfBQYtYlp | [
"Sreenivas Gollapudi",
"Guru Guruganesh",
"Kostas Kollias",
"Pasin Manurangsi",
"Renato Paes Leme",
"Jon Schneider"
] | Poster | null | We consider the following variant of contextual linear bandits motivated by routing applications in navigational engines and recommendation systems. We wish to learn a hidden $d$-dimensional value $w^*$. Every round, we are presented with a subset $\mathcal{X}_t \subseteq \mathbb{R}^d$ of possible actions. If we choos... | [
"Online Learning",
"Convex Geometry",
"Separation Oracles",
"Linear Bandits",
"Contextual Bandits"
] | We consider variants of the linear contextual bandit problem where we only receive the best arm as feedback. | 11,257 | 2106.04819 | title_snapshot | [
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... |
A Stochastic Newton Algorithm for Distributed Convex Optimization | https://openreview.net/forum?id=5BD4_awH4Fd | [
"Brian Bullins",
"Kumar Kshitij Patel",
"Ohad Shamir",
"Nathan Srebro",
"Blake Woodworth"
] | Poster | null | We propose and analyze a stochastic Newton algorithm for homogeneous distributed stochastic convex optimization, where each machine can calculate stochastic gradients of the same population objective, as well as stochastic Hessian-vector products (products of an independent unbiased estimator of the Hessian of the popu... | [
"Distributed Optimization",
"Stochastic Optimization",
"Federated Learning",
"Newton's Method"
] | We propose and analyze a stochastic Newton algorithm for homogeneous distributed convex optimization based on efficiently solving quadratic objectives in parallel with only a single round of communication. | 9,632 | 2110.02954 | title_snapshot | [
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Efficient Learning of Discrete-Continuous Computation Graphs | https://openreview.net/forum?id=TLIHuw3gcQB | [
"David Friede",
"Mathias Niepert"
] | Poster | null | Numerous models for supervised and reinforcement learning benefit from combinations of discrete and continuous model components. End-to-end learnable discrete-continuous models are compositional, tend to generalize better, and are more interpretable. A popular approach to building discrete-continuous computation graphs... | [
"Discrete-Continuous Learning",
"Stochastic Computation Graphs",
"Gumbel-softmax Trick"
] | We propose two new strategies to enable efficient learning of discrete-continuous computation graphs with multiple stochastic nodes. | 10,964 | 2307.14193 | title_snapshot | [
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... |
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