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Found 22 papers, 1 papers with code
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes
We show how to use unlabeled data and a deep belief net (DBN) to learn a good covariance kernel for a Gaussian process.
Evaluating probabilities under high-dimensional latent variable models
We present a simple new Monte Carlo algorithm for evaluating probabilities of observations in complex latent variable models, such as Deep Belief Networks.
Replicated Softmax: an Undirected Topic Model
Each member of the family models the probability distribution of documents of a specific length as a product of topic-specific distributions rather than as a mixture and this gives much better generalization than Latent Dirichlet Allocation for modeling the log probabilities of held-out documents.
Learning in Markov Random Fields using Tempered Transitions
Markov random fields (MRFs), or undirected graphical models, provide a powerful framework for modeling complex dependencies among random variables.
Modelling Relational Data using Bayesian Clustered Tensor Factorization
We consider the problem of learning probabilistic models for complex relational structures between various types of objects.
Practical Large-Scale Optimization for Max-norm Regularization
The max-norm was proposed as a convex matrix regularizer by Srebro et al (2004) and was shown to be empirically superior to the trace-norm for collaborative filtering problems.
Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm
We show that matrix completion with trace-norm regularization can be significantly hurt when entries of the matrix are sampled non-uniformly, but that a properly weighted version of the trace-norm regularizer works well with non-uniform sampling.
Learning to Learn with Compound HD Models
We introduce HD (or ``Hierarchical-Deep'') models, a new compositional learning architecture that integrates deep learning models with structured hierarchical Bayesian models.
Learning with the weighted trace-norm under arbitrary sampling distributions
We provide rigorous guarantees on learning with the weighted trace-norm under arbitrary sampling distributions.
Improving neural networks by preventing co-adaptation of feature detectors
When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data.
Ranked #205 on
Image Classification
on CIFAR-10
Matrix reconstruction with the local max norm
We introduce a new family of matrix norms, the ''local max'' norms, generalizing existing methods such as the max norm, the trace norm (nuclear norm), and the weighted or smoothed weighted trace norms, which have been extensively used in the literature as regularizers for matrix reconstruction problems.
Hamming Distance Metric Learning
Motivated by large-scale multimedia applications we propose to learn mappings from high-dimensional data to binary codes that preserve semantic similarity.
Cardinality Restricted Boltzmann Machines
The Restricted Boltzmann Machine (RBM) is a popular density model that is also good for extracting features.
A Better Way to Pretrain Deep Boltzmann Machines
We describe how the pre-training algorithm for Deep Boltzmann Machines (DBMs) is related to the pre-training algorithm for Deep Belief Networks and we show that under certain conditions, the pre-training procedure improves the variational lower bound of a two-hidden-layer DBM.
Multimodal Learning with Deep Boltzmann Machines
Our experimental results on bi-modal data consisting of images and text show that the Multimodal DBM can learn a good generative model of the joint space of image and text inputs that is useful for information retrieval from both unimodal and multimodal queries.
Modeling Documents with Deep Boltzmann Machines
We introduce a Deep Boltzmann Machine model suitable for modeling and extracting latent semantic representations from a large unstructured collection of documents.
One-shot learning by inverting a compositional causal process
People can learn a new visual class from just one example, yet machine learning algorithms typically require hundreds or thousands of examples to tackle the same problems.
Discriminative Transfer Learning with Tree-based Priors
The tree structure can be used to impose a generative prior over classification parameters.
Ranked #183 on
Image Classification
on CIFAR-100
Annealing between distributions by averaging moments
Many powerful Monte Carlo techniques for estimating partition functions, such as annealed importance sampling (AIS), are based on sampling from a sequence of intermediate distributions which interpolate between a tractable initial distribution and an intractable target distribution.
Learning Stochastic Feedforward Neural Networks
As regressors, MLPs model the conditional distribution of the predictor variables Y given the input variables X.
How Many Samples are Needed to Estimate a Convolutional Neural Network?
We show that for an $m$-dimensional convolutional filter with linear activation acting on a $d$-dimensional input, the sample complexity of achieving population prediction error of $\epsilon$ is $\widetilde{O(m/\epsilon^2)$, whereas the sample-complexity for its FNN counterpart is lower bounded by $\Omega(d/\epsilon^2)$ samples.
GLoMo: Unsupervised Learning of Transferable Relational Graphs
We also show that the learned graphs are generic enough to be transferred to different embeddings on which the graphs have not been trained (including GloVe embeddings, ELMo embeddings, and task-specific RNN hidden units), or embedding-free units such as image pixels.
