Statistical Learning
Gradient Descent for Spiking Neural Networks
Dongsung Huh, Terrence J. Sejnowski
Research in spike-based computation has been impeded by the lack of efficient supervised learning algorithm for spiking neural networks.
Alternating optimization of decision trees, with application to learning sparse oblique trees
Miguel A. Carreira-Perpinan, Pooya Tavallali
Neural Information Processing Systems http://nips.cc/
Amortized Inference Regularization
Rui Shu, Hung H. Bui, Shengjia Zhao, Mykel J. Kochenderfer, Stefano Ermon
Canonically, the variational principle suggests to prefer an expressive inference model so that the variational approximation is accurate.
Learning a latent manifold of odor representations from neural responses in piriform cortex
Anqi Wu, Stan Pashkovski, Sandeep R. Datta, Jonathan W. Pillow
Neural Information Processing Systems http://nips.cc/
Delta-encoder: an effective sample synthesis method for few-shot object recognition
Eli Schwartz, Leonid Karlinsky, Joseph Shtok, Sivan Harary, Mattias Marder, Abhishek Kumar, Rogerio Feris, Raja Giryes, Alex Bronstein
Learning to classify new categories based on just one or a few examples is a long-standing challenge in modern computer vision. In this work, we propose a simple yet effective method for few-shot (and one-shot) object recognition.
Heterogeneous Multi-output Gaussian Process Prediction
Pablo Moreno-Muñoz, Antonio Artés, Mauricio Álvarez
Neural Information Processing Systems http://nips.cc/
Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data
Xenia Miscouridou, Francois Caron, Yee Whye Teh
We propose a novel class of network models for temporal dyadic interaction data.
Dual Policy Iteration
Wen Sun, Geoffrey J. Gordon, Byron Boots, J. Bagnell
We also provide a general convergence analysis to support our empirical findings. Although our analysis is similar to CPI's, it has a key difference: as long as MBOC succeeds, we can provide a larger policy improvement than CPI at each iteration.
Generalized Zero-Shot Learning with Deep Calibration Network
Shichen Liu, Mingsheng Long, Jianmin Wang, Michael I. Jordan
Zero-shot learning leverages semantic representations such as attributes or class prototypes to bridge source and target classes.
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator
Cong Fang, Chris Junchi Li, Zhouchen Lin, Tong Zhang
Neural Information Processing Systems http://nips.cc/