Artificial Intelligence
Pursuing computing advances to create intelligent machines that complement human reasoning to augment and enrich our experience and competencies.
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NIPS: Oral Session 6 – Wei Chen
Combinatorial Pure Exploration of Multi-Armed Bandits We study the {\em combinatorial pure exploration (CPE)} problem in the stochastic multi-armed bandit setting, where a learner explores a set of arms with the objective of identifying the…
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NIPS: Spotlight Session 5 – Regression and Time Series Spotlights
M. Lopes A Residual Bootstrap for High-Dimensional Regression with Near Low-Rank Designs B. McWilliams, G. Krummenacher, M. Lucic, J. Buhmann Fast and Robust Least Squares Estimation in Corrupted Linear Models M. Bahadori, R. Yu, Y.…
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NIPS: Oral Session 4 – Jason Yosinski
How transferable are features in deep neural networks? Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color…
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NIPS: Oral Session 1 – Deeparnab Chakrabarty
Provable Submodular Minimization using Wolfe’s Algorithm Owing to several applications in large scale learning and vision problems, fast submodular function minimization (SFM) has become a critical problem. Theoretically, unconstrained SFM can be performed in polynomial…
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NIPS: Oral Session 2 – Anshumali Shrivastava
Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS) We present the first provably sublinear time hashing algorithm for approximate \emph{Maximum Inner Product Search} (MIPS). Searching with (un-normalized) inner product as the underlying…
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NIPS: Oral Session 3 – Dylan Festa
Analog Memories in a Balanced Rate-Based Network of E-I Neurons The persistent and graded activity often observed in cortical circuits is sometimes seen as a signature of autoassociative retrieval of memories stored earlier in synaptic…
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NIPS: Spotlight Session 1 – Optimization Spotlights
W. Su, S. Boyd, E. Candes A Differential Equation for Modeling Nesterov’s Accelerated Gradient Method: Theory and Insights V. Srikumar, C. Manning Learning Distributed Representations for Structured Output Prediction J. Hernández-Lobato, M. Hoffman, Z. Ghahramani…
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NIPS: Spotlight Session 4 – Deep Spotlights
J. Mairal, P. Koniusz, Z. Harchaoui, C. Schmid Convolutional Kernel Networks B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, A. Oliva Learning Deep Features for Scene Recognition using Places Database W. Zaremba, K. Kurach, R.…
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NIPS: Oral Session 4 – Ilya Sutskever
Sequence to Sequence Learning with Neural Networks Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they…
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NIPS: Oral Session 4 – Waleed Ammar
Conditional Random Field Autoencoders for Unsupervised Structured Prediction We introduce a framework for unsupervised learning of structured predictors with overlapping, global features. Each input’s latent representation is predicted conditional on the observed data using a…
