A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Optimizes e-commerce traffic sales by incorporating hidden costs into auction mechanisms.
Study uncovers sexual harassment experiences in academia using text mining.
A novel RNN model with shuffled hidden states.
This is a technical report which explores the estimation methodologies on hyper-parameters in Markov Random Field and Gaussian Hidden Markov Random Field. In first section, we briefly investigate a theoretical framework on Metropolis-Hastings algorithm. Next, by using MH algorithm, we simulate the data from Ising model…
Hidden Markov Neural Networks balance adaptation and forgetting in time-series data.
Paper tackles causal effect estimation in observational data with hidden variables.
Modified asymmetric hidden Markov models for time series with autoregressive components.
New algorithm for collective Gaussian hidden Markov models inference.
New findings on hidden symmetries in ReLU networks.
Identifying causal direction in location-scale noise models with hidden variables
Deep networks have achieved impressive results across a variety of important tasks. However a known weakness is a failure to perform well when evaluated on data which differ from the training distribution, even if these differences are very small, as is the case with adversarial examples. We propose Fortified Networks,…
Recently, we proposed to transform the outputs of each hidden neuron in a multi-layer perceptron network to have zero output and zero slope on average, and use separate shortcut connections to model the linear dependencies instead. We continue the work by firstly introducing a third transformation to normalize the scal…
As one of Bayesian analysis tools, Hidden Markov Model (HMM) has been used to in extensive applications. Most HMMs are solved by Baum-Welch algorithm (BWHMM) to predict the model parameters, which is difficult to find global optimal solutions. This paper proposes an optimized Hidden Markov Model with Particle Swarm Opt…
Chemical networks outperform spiking neural networks in classification tasks.
The paper uses HMM and LSTM for stock market trend analysis.
Estimates effects of multiple interventions with hidden confounders using single-variable interventions.
Stochastic variational inference for collapsed models has recently been successfully applied to large scale topic modelling. In this paper, we propose a stochastic collapsed variational inference algorithm for hidden Markov models, in a sequential data setting. Given a collapsed hidden Markov Model, we break its long M…
Generalization is one of the most important issues in machine learning problems. In this study, we consider generalization in restricted Boltzmann machines (RBMs). We propose an RBM with multivalued hidden variables, which is a simple extension of conventional RBMs. We demonstrate that the proposed model is better than…
We examine the effect of the Group Lasso (gLasso) regularizer in selecting the salient nodes of Deep Neural Network (DNN) hidden layers by applying a DNN-HMM hybrid speech recognizer to TED Talks speech data. We test two types of gLasso regularization, one for outgoing weight vectors and another for incoming weight vec…
In this paper, we present an infinite hierarchical non-parametric Bayesian model to extract the hidden factors over observed data, where the number of hidden factors for each layer is unknown and can be potentially infinite. Moreover, the number of layers can also be infinite. We construct the model structure that allo…
Study explores reinforcement learning in a complex game environment, analyzing rule inference and policy learning.
LIFE framework improves model accuracy and interpretability.
Algorithm adapts to shifting domains with minimal label queries.
Multi-view data are becoming common in real-world modeling tasks and many multi-view data clustering algorithms have thus been proposed. The existing algorithms usually focus on the cooperation of different views in the original space but neglect the influence of the hidden information among these different visible vie…
Caus-Modens uses deep ensembles to better predict causal outcomes in hidden confounding scenarios.
FEALM learns features for better nonlinear DR of hidden patterns.
Learning nonlinear dynamics from diffusion data is a challenging problem since the individuals observed may be different at different time points, generally following an aggregate behaviour. Existing work cannot handle the tasks well since they model such dynamics either directly on observations or enforce the availabi…
We propose a framework to model the distribution of sequential data coming from a set of entities connected in a graph with a known topology. The method is based on a mixture of shared hidden Markov models (HMMs), which are jointly trained in order to exploit the knowledge of the graph structure and in such a way that …
New algorithm reduces sample complexity for learning CNNs.
We study the problem of learning one-hidden-layer neural networks with Rectified Linear Unit (ReLU) activation function, where the inputs are sampled from standard Gaussian distribution and the outputs are generated from a noisy teacher network. We analyze the performance of gradient descent for training such kind of n…
As a new neural machine translation approach, Non-Autoregressive machine Translation (NAT) has attracted attention recently due to its high efficiency in inference. However, the high efficiency has come at the cost of not capturing the sequential dependency on the target side of translation, which causes NAT to suffer …
New method reduces overfitting in deep neural networks by measuring and regulating hidden unit diversity.
Estimates dependent parameters using Markovian dependence with shrinkage.
A new metric space model for point process excitations uncovers hidden interactions.
New algorithm for aggregate inference in HMMs with continuous observations.
Stochastic variational inference for collapsed models has recently been successfully applied to large scale topic modelling. In this paper, we propose a stochastic collapsed variational inference algorithm in the sequential data setting. Our algorithm is applicable to both finite hidden Markov models and hierarchical D…
We introduce a new paradigm that is important for community detection in the realm of network analysis. Networks contain a set of strong, dominant communities, which interfere with the detection of weak, natural community structure. When most of the members of the weak communities also belong to stronger communities, t…
Cubic spline smoothing improves interpolation between irregularly sampled data.
Observational data is increasingly used as a means for making individual-level causal predictions and intervention recommendations. The foremost challenge of causal inference from observational data is hidden confounding, whose presence cannot be tested in data and can invalidate any causal conclusion. Experimental dat…
The infinite restricted Boltzmann machine (iRBM) is an extension of the classic RBM. It enjoys a good property of automatically deciding the size of the hidden layer according to specific training data. With sufficient training, the iRBM can achieve a competitive performance with that of the classic RBM. However, the c…
Algorithm improves RL by discovering delayed causal relations.
The study analyzes local minima in ReLU networks and finds low probability of bad local minima.
Polynomial delay algorithm tests causal models with hidden variables.
Method estimates CATE using RCT data to handle hidden confounders.
FactorGCL uses hypergraph learning to predict stock returns by mining hidden factors.
UNMIX identifies hidden buyers in darknet markets by clustering anonymized IDs.
Although deep learning has shown its powerful performance in many applications, the mathematical principles behind neural networks are still mysterious. In this paper, we consider the problem of learning a one-hidden-layer neural network with quadratic activations. We focus on the under-parameterized regime where the n…
Two case studies reveal hidden biases and confounders in machine learning models of biomedical data.