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Found 13 papers, 2 papers with code
Decomposed Mutual Information Estimation for Contrastive Representation Learning
We propose decomposing the full MI estimation problem into a sum of smaller estimation problems by splitting one of the views into progressively more informed subviews and by applying the chain rule on MI between the decomposed views.
Decomposing Mutual Information for Representation Learning
In this paper, we transform each view into a set of subviews and then decompose the original MI bound into a sum of bounds involving conditional MI between the subviews.
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift
However, recent work has shown limitations of this approach when label distributions differ between the source and target domains.
Expressiveness and Learning of Hidden Quantum Markov Models
Extending classical probabilistic reasoning using the quantum mechanical view of probability has been of recent interest, particularly in the development of hidden quantum Markov models (HQMMs) to model stochastic processes.
A Reduction from Reinforcement Learning to No-Regret Online Learning
We present a reduction from reinforcement learning (RL) to no-regret online learning based on the saddle-point formulation of RL, by which "any" online algorithm with sublinear regret can generate policies with provable performance guarantees.
Learning From the Experience of Others: Approximate Empirical Bayes in Neural Networks
Learning deep neural networks could be understood as the combination of representation learning and learning halfspaces.
Learning Hidden Quantum Markov Models
We extend previous work on HQMMs with three contributions: (1) we show how classical hidden Markov models (HMMs) can be simulated on a quantum circuit, (2) we reformulate HQMMs by relaxing the constraints for modeling HMMs on quantum circuits, and (3) we present a learning algorithm to estimate the parameters of an HQMM from data.
Frank-Wolfe Optimization for Symmetric-NMF under Simplicial Constraint
Symmetric nonnegative matrix factorization has found abundant applications in various domains by providing a symmetric low-rank decomposition of nonnegative matrices.
Principled Hybrids of Generative and Discriminative Domain Adaptation
This provides us a very general way to interpolate between generative and discriminative extremes through different choices of priors.
DeepArchitect: Automatically Designing and Training Deep Architectures
In addition, these experiments show that our framework can be used effectively for model discovery, as it is possible to describe expressive search spaces and discover competitive models without much effort from the human expert.
Linear Time Computation of Moments in Sum-Product Networks
We propose a dynamic programming method to further reduce the computation of the moments of all the edges in the graph from quadratic to linear.
Efficient Multitask Feature and Relationship Learning
In this paper, we consider a formulation of multitask learning that learns the relationships both between tasks and between features, represented through a task covariance and a feature covariance matrix, respectively.
A Unified Approach for Learning the Parameters of Sum-Product Networks
We present a unified approach for learning the parameters of Sum-Product networks (SPNs).
