NeurIPS
Collection
Accepted papers for NeurIPS (Conference on Neural Information Processing Systems), one dataset per year. • 13 items • Updated
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values | abstract large_stringlengths 292 1.97k | keywords listlengths 0 0 | TL;DR large_stringclasses 0
values | submission_number int64 1 1.01k | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
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Synthesized Policies for Transfer and Adaptation across Tasks and Environments | https://proceedings.neurips.cc/paper_files/paper/2018/hash/00ac8ed3b4327bdd4ebbebcb2ba10a00-Abstract.html | [
"Hexiang Hu",
"Liyu Chen",
"Boqing Gong",
"Fei Sha"
] | null | null | The ability to transfer in reinforcement learning is key towards building an agent of general artificial intelligence. In this paper, we consider the problem of learning to simultaneously transfer across both environments and tasks, probably more importantly, by learning from only sparse (environment, task) pairs out o... | [] | null | 1 | 1904.03276 | title_snapshot | [
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Self-Supervised Generation of Spatial Audio for 360° Video | https://proceedings.neurips.cc/paper_files/paper/2018/hash/01161aaa0b6d1345dd8fe4e481144d84-Abstract.html | [
"Pedro Morgado",
"Nuno Nvasconcelos",
"Timothy Langlois",
"Oliver Wang"
] | null | null | We introduce an approach to convert mono audio recorded by a 360° video camera into spatial audio, a representation of the distribution of sound over the full viewing sphere. Spatial audio is an important component of immersive 360° video viewing, but spatial audio microphones are still rare in current 360° video produ... | [] | null | 2 | 1809.02587 | title_snapshot | [
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On GANs and GMMs | https://proceedings.neurips.cc/paper_files/paper/2018/hash/0172d289da48c48de8c5ebf3de9f7ee1-Abstract.html | [
"Eitan Richardson",
"Yair Weiss"
] | null | null | A longstanding problem in machine learning is to find unsupervised methods that can learn the statistical structure of high dimensional signals. In recent years, GANs have gained much attention as a possible solution to the problem, and in particular have shown the ability to generate remarkably realistic high resoluti... | [] | null | 3 | 1805.12462 | title_snapshot | [
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Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks | https://proceedings.neurips.cc/paper_files/paper/2018/hash/018b59ce1fd616d874afad0f44ba338d-Abstract.html | [
"Hyeonseob Nam",
"Hyo-Eun Kim"
] | null | null | Real-world image recognition is often challenged by the variability of visual styles including object textures, lighting conditions, filter effects, etc. Although these variations have been deemed to be implicitly handled by more training data and deeper networks, recent advances in image style transfer suggest that it... | [] | null | 4 | 1805.07925 | title_snapshot | [
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Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies | https://proceedings.neurips.cc/paper_files/paper/2018/hash/018dd1e07a2de4a08e6612341bf2323e-Abstract.html | [
"Sungryull Sohn",
"Junhyuk Oh",
"Honglak Lee"
] | null | null | We introduce a new RL problem where the agent is required to generalize to a previously-unseen environment characterized by a subtask graph which describes a set of subtasks and their dependencies. Unlike existing hierarchical multitask RL approaches that explicitly describe what the agent should do at a high level, ou... | [] | null | 5 | 1807.07665 | title_snapshot | [
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KDGAN: Knowledge Distillation with Generative Adversarial Networks | https://proceedings.neurips.cc/paper_files/paper/2018/hash/019d385eb67632a7e958e23f24bd07d7-Abstract.html | [
"Xiaojie Wang",
"Rui Zhang",
"Yu Sun",
"Jianzhong Qi"
] | null | null | Knowledge distillation (KD) aims to train a lightweight classifier suitable to provide accurate inference with constrained resources in multi-label learning. Instead of directly consuming feature-label pairs, the classifier is trained by a teacher, i.e., a high-capacity model whose training may be resource-hungry. The ... | [] | null | 6 | null | null | [
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Contour location via entropy reduction leveraging multiple information sources | https://proceedings.neurips.cc/paper_files/paper/2018/hash/01a0683665f38d8e5e567b3b15ca98bf-Abstract.html | [
"Alexandre Marques",
"Remi Lam",
"Karen Willcox"
] | null | null | We introduce an algorithm to locate contours of functions that are expensive to evaluate. The problem of locating contours arises in many applications, including classification, constrained optimization, and performance analysis of mechanical and dynamical systems (reliability, probability of failure, stability, etc.).... | [] | null | 7 | 1805.07489 | title_snapshot | [
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Contextual bandits with surrogate losses: Margin bounds and efficient algorithms | https://proceedings.neurips.cc/paper_files/paper/2018/hash/01e9565cecc4e989123f9620c1d09c09-Abstract.html | [
"Dylan J Foster",
"Akshay Krishnamurthy"
] | null | null | We use surrogate losses to obtain several new regret bounds and new algorithms for contextual bandit learning. Using the ramp loss, we derive a new margin-based regret bound in terms of standard sequential complexity measures of a benchmark class of real-valued regression functions. Using the hinge loss, we derive an e... | [] | null | 8 | 1806.10745 | title_snapshot | [
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Adaptive Sampling Towards Fast Graph Representation Learning | https://proceedings.neurips.cc/paper_files/paper/2018/hash/01eee509ee2f68dc6014898c309e86bf-Abstract.html | [
"Wenbing Huang",
"Tong Zhang",
"Yu Rong",
"Junzhou Huang"
] | null | null | Graph Convolutional Networks (GCNs) have become a crucial tool on learning representations of graph vertices. The main challenge of adapting GCNs on large-scale graphs is the scalability issue that it incurs heavy cost both in computation and memory due to the uncontrollable neighborhood expansion across layers. In thi... | [] | null | 9 | 1809.05343 | title_snapshot | [
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Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net | https://proceedings.neurips.cc/paper_files/paper/2018/hash/0245952ecff55018e2a459517fdb40e3-Abstract.html | [
"Tom Michoel"
] | null | null | The lasso and elastic net linear regression models impose a double-exponential prior distribution on the model parameters to achieve regression shrinkage and variable selection, allowing the inference of robust models from large data sets. However, there has been limited success in deriving estimates for the full poste... | [] | null | 10 | 1709.08535 | title_snapshot | [
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Identification and Estimation of Causal Effects from Dependent Data | https://proceedings.neurips.cc/paper_files/paper/2018/hash/024677efb8e4aee2eaeef17b54695bbe-Abstract.html | [
"Eli Sherman",
"Ilya Shpitser"
] | null | null | The assumption that data samples are independent and identically distributed (iid) is standard in many areas of statistics and machine learning. Nevertheless, in some settings, such as social networks, infectious disease modeling, and reasoning with spatial and temporal data, this assumption is false. An extensive lite... | [] | null | 11 | 1902.01443 | title_snapshot | [
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