Technology
The Multiscale Laplacian Graph Kernel
Neural Information Processing Systems http://nips.cc/
A Unified Approach for Learning the Parameters of Sum-Product Networks
Han Zhao, Pascal Poupart, Geoffrey J. Gordon
Neural Information Processing Systems http://nips.cc/
Probabilistic Inference with Generating Functions for Poisson Latent Variable Models
Kevin Winner, Daniel R. Sheldon
Neural Information Processing Systems http://nips.cc/
Learning Sparse Gaussian Graphical Models with Overlapping Blocks
Seyed Mohammad Javad Hosseini, Su-In Lee
Second, GRAB blocks (Figa priorioruseasequential fixed. Thefirsttwo terms,logdet ( ) trace (S ), in Eq (3) correspondtologP(X| ), thelog-likelihoodof GGM givenaparticularparameter (i.e., anestimateof 1), asdescribedin Section 2.1.
Improved Deep Metric Learning with Multi-class N-pair Loss Objective
Neural Information Processing Systems http://nips.cc/
Ladder Variational Autoencoders
Casper Kaae Sรธnderby, Tapani Raiko, Lars Maalรธe, Sรธren Kaae Sรธnderby, Ole Winther
Neural Information Processing Systems http://nips.cc/
Fast ฮต-free Inference of Simulation Models with Bayesian Conditional Density Estimation
George Papamakarios, Iain Murray
Neural Information Processing Systems http://nips.cc/
Equality of Opportunity in Supervised Learning
Moritz Hardt, Eric Price, Eric Price, Nati Srebro
Unfortunately, the notion is seriously flawed on two counts [8]. First, it doesn't ensure fairness.
Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain
Timothy Rubin, Oluwasanmi O. Koyejo, Michael N. Jones, Tal Yarkoni
This paper presents Generalized Correspondence-LDA (GC-LDA), a generalization of the Correspondence-LDA model that allows for variable spatial representations to be associated with topics, and increased flexibility in terms of the strength of the correspondence between data types induced by the model. We present three variants of GC-LDA, each of which associates topics with a different spatial representation, and apply them to a corpus of neuroimaging data. In the context of this dataset, each topic corresponds to a functional brain region, where the region's spatial extent is captured by a probability distribution over neural activity, and the region's cognitive function is captured by a probability distribution over linguistic terms. We illustrate the qualitative improvements offered by GC-LDA in terms of the types of topics extracted with alternative spatial representations, as well as the model's ability to incorporate a-priori knowledge from the neuroimaging literature. We furthermore demonstrate that the novel features of GC-LDA improve predictions for missing data.
On the Recursive Teaching Dimension of VC Classes
Xi Chen, Xi Chen, Yu Cheng, Bo Tang
The recursive teaching dimension (RTD) of a concept class C {0,1}n, introduced by Zilles et al. [ZLHZ11], is a complexity parameter measured by the worst-case number of labeled examples needed to learn any target concept of C in the recursive teaching model. In this paper, we study the quantitative relation between RTD and the well-known learning complexity measure VC dimension (VCD), and improve the best known upper and (worst-case) lower bounds on the recursive teaching dimension with respect to the VC dimension. Given a concept class C {0,1}n with VCD(C) = d, we first show that RTD(C) is at most d 2d+1. This is the first upper bound for RTD(C)that depends only on VCD(C), independent of the size of the concept class |C| and its domain size n. Before our work, the best known upper bound for RTD(C) is O(d2d loglog|C|), obtained by Moran et al. [MSWY15].