We investigate the non-identifiability issues associated with bidirectional adversarial training for joint distribution matching. Within a framework of conditional entropy, we propose both adversarial and non-adversarial approaches to learn desirable matched joint distributions for unsupervised and supervised tasks. We…
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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.
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48 results for “joint supervision”
Unified framework for learning with indirect supervision signals.
problem Learning from indirect supervision signals when gold labels are missing or costly.
method Developed a unified theoretical framework for multi-class classification with variable supervision.
result Introduced the concept of separation to characterize learnability and generalization bounds.
New framework combines semi-supervised data programming with subset selection for improved text classification.
problem Sub-optimal performance in data programming with noisy labelling functions.
method Introduces a framework \model for semi-supervised data programming that uses joint models and subset selection.
result Significantly outperforms state-of-the-art on seven datasets.
Joint Unsupervised and Supervised Training for Automatic Speech Recognition via Bilevel Optimizationcs.CL
Novel approach trains ASR models with less supervision using bilevel optimization.
problem Training acoustic models for ASR with minimal supervision.
method Bilevel optimization with unsupervised and supervised losses.
result Achieves superior performance compared to existing methods.
Paper explores supervised learning methods to approximate ideal observer for joint signal detection and localization.
problem Optimizing medical imaging systems by assessing their performance using the Ideal Observer model.
method Uses supervised learning methods, specifically convolutional neural networks, to approximate the Ideal Observer for joint signal detection and localization tasks.
result Supervised learning-based methods can approximate the Ideal Observer for joint signal detection and localization tasks, as shown by comparisons to MCMC and analytical methods.
Protein contacts contain important information for protein structure and functional study, but contact prediction from sequence remains very challenging. Both evolutionary coupling (EC) analysis and supervised machine learning methods are developed to predict contacts, making use of different types of information, resp…
Given a set of possible models (e.g., Bayesian network structures) and a data sample, in the unsupervised model selection problem the task is to choose the most accurate model with respect to the domain joint probability distribution. In contrast to this, in supervised model selection it is a priori known that the chos…
JRFs improve semi-supervised learning by balancing generation and classification.
problem Mode missing and mode covering issues in GANs and VAEs, and conflict between good classification and generation.
method Joint-stochastic-approximation random fields (JRFs) for deep undirected generative models.
result JRFs achieve good classification and generation results in SSL.
Given a set of possible models (e.g., Bayesian network structures) and a data sample, in the unsupervised model selection problem the task is to choose the most accurate model with respect to the domain joint probability distribution. In contrast to this, in supervised model selection it is a priori known that the chos…
New algorithm improves source separation with multi-trial supervision.
problem Non-convex optimization and interpretability of independent components.
method Proximal gradient-type algorithm in invertible matrices with backpropagation for joint learning.
result Increased success rate of non-convex optimization and improved interpretability.
New analysis reveals masked self-supervised learning's effectiveness in extracting data structure.
problem Analyzing masked self-supervised learning in high-dimensional data.
method Developed precise high-dimensional analysis of masked modeling objectives.
result Identified phase transitions and structured regimes for masked self-supervised learning.
We consider a family of problems that are concerned about making predictions for the majority of unlabeled, graph-structured data samples based on a small proportion of labeled samples. Relational information among the data samples, often encoded in the graph/network structure, is shown to be helpful for these semi-sup…
JoCoR improves deep learning with noisy labels by reducing network diversity.
problem Learning with noisy labels in deep learning.
method JoCoR uses two networks to make predictions, calculates a joint loss with Co-Regularization, and updates both networks simultaneously.
result JoCoR outperforms state-of-the-art approaches in learning with noisy labels.
The paper emphasizes the importance of joint predictions over marginal predictions for decision-making.
problem The need for accurate joint predictions in decision-making problems.
method The paper analyzes combinatorial decision problems, sequential predictions, and multi-armed bandits, introducing an approximate Thompson sampling algorithm and new regret bounds.
result Accurate joint predictions are essential for good performance in decision-making problems.
A Triangle Generative Adversarial Network (-GAN) is developed for semi-supervised cross-domain joint distribution matching, where the training data consists of samples from each domain, and supervision of domain correspondence is provided by only a few paired samples. -GAN consists of four neural networks, two ge…
New JSA autoencoders tackle discrete latent variable models for semi-supervised learning.
problem Handling discrete observations and latent codes in deep generative models.
method Joint-stochastic-approximation (JSA) autoencoders that directly maximize data log-likelihood and minimize KL divergence.
result JSA autoencoders achieve comparable performance to continuous latent space models in semi-supervised tasks.
LSDM uses unpaired data to match latent space distributions for generative modeling.
problem Generating high-quality images with limited paired data.
method Two-stage approach: latent space learning from paired and unpaired data, followed by joint distribution matching.
result LSDM enhances geometric fidelity in generated outputs and provides theoretical insights into LDMs.
Multiple modalities often co-occur when describing natural phenomena. Learning a joint representation of these modalities should yield deeper and more useful representations. Previous generative approaches to multi-modal input either do not learn a joint distribution or require additional computation to handle missing …
PeakSegJoint: fast supervised peak detection via joint segmentation of multiple count data samplesstat.ML
Joint peak detection is a central problem when comparing samples in genomic data analysis, but current algorithms for this task are unsupervised and limited to at most 2 sample types. We propose PeakSegJoint, a new constrained maximum likelihood segmentation model for any number of sample types. To select the number of…
This paper proposes a semi-conditional normalizing flow model for semi-supervised learning. The model uses both labelled and unlabeled data to learn an explicit model of joint distribution over objects and labels. Semi-conditional architecture of the model allows us to efficiently compute a value and gradients of the m…
Proposes a new method for handling domain shift in samples with biases in both covariates and labels.
problem Domain shift in samples with biases in both covariates and labels.
method Factorizable Joint Shift (FJS) and Joint Importance Aligning (JIA).
result Our method can handle co-existence of sampling bias in covariates and labels.
sJIVE combines structure and prediction in multi-source data.
problem Analyzing multi-source data with shared and unique structures.
method Supervised Joint and Individual Variation Explained (sJIVE) method.
result sJIVE outperforms existing methods in noisy data.
Bayesian Topic Regression models causal inference with text and numerical data.
problem Causal inference using observational text data with both text and numerical confounders.
method Combines supervised Bayesian topic model with Bayesian regression framework, respecting the Frisch-Waugh-Lovell theorem.
result Joint approach recovers ground truth with lower bias than benchmarks, superior prediction results compared to separate approaches.
Supervised topic models utilize document's side information for discovering predictive low dimensional representations of documents. Existing models apply the likelihood-based estimation. In this paper, we present a general framework of max-margin supervised topic models for both continuous and categorical response var…
Improved multimodal variational models capture more complex joint distributions.
problem Limited expressiveness of multimodal variational models.
method Used normalizing flows to approximate and transform a simple parametric joint posterior into a more complex one.
result The model improves on state-of-the-art multimodal variational methods on various tasks.
Self-supervised method predicts clean signal and noise distribution from noisy images.
problem Blind denoising and noise estimation in biomedical images with limited clean data.
method Two neural networks jointly predict clean signal and noise distribution from noisy observations.
result Significantly outperforms state-of-the-art algorithms on six biomedical image datasets.
Joint Structured Learning and Predictions under Logical Constraints in Conditional Random Fieldsstat.ML
This paper is concerned with structured machine learning, in a supervised machine learning context. It discusses how to make joint structured learning on interdependent objects of different nature, as well as how to enforce logical con-straints when predicting labels. We explain how this need arose in a Document Unders…
Generalizes adversarial learning for better latent variable inference in GANs.
problem Improving latent variable inference in GANs for diverse applications.
method Adversarial learning with multiple feedback layers, self-supervision, and auxiliary tasks.
result Achieves global optimum matching multiple joint distributions.
We introduce the Neural Conditioner (NC), a self-supervised machine able to learn about all the conditional distributions of a random vector . The NC is a function that leverages adversarial training to match each conditional distribution . After training, the NC generalizes to …
Learning to drive faithfully in highly stochastic urban settings remains an open problem. To that end, we propose a Multi-task Learning from Demonstration (MT-LfD) framework which uses supervised auxiliary task prediction to guide the main task of predicting the driving commands. Our framework involves an end-to-end tr…
A new method for semi-supervised learning of sparse features using elastic-net.
problem Semi-supervised learning of sparse features in generalized linear models.
method Generalized Semi-Supervised Elastic-Net (s2net) framework.
result The s2net framework improves upon supervised elastic-net methods for semi-supervised learning.
A new method for weakly supervised learning that improves model accuracy.
problem Training machine learning models with precise labels is expensive; weak supervision provides a low-cost alternative.
method Data consistent weak supervision algorithm that searches over classifiers to find plausible labelings, considering features of the training data and estimating labels for low/no coverage data.
result Empirically, the method significantly outperforms state-of-the-art weak supervision methods on text and image classification tasks.
Paper aims to find joint representation between vocal tract geometry and speech sound acoustics.
problem Finding a joint latent representation between articulatory and acoustic domains for vowel sounds.
method Invertible neural network models, convolutional autoencoder, normalizing flows, semi-supervised learning.
result Satisfactory performance in articulatory-to-acoustic and acoustic-to-articulatory mapping.
Proposes a new model for joint probability distributions in computer vision.
problem Limitation of existing models in meeting diverse downstream tasks.
method Uses parametric conditional probability distributions for each group of variables conditioned on the rest.
result Models can be used for any downstream task without task-specific design.
FreST Loss decorrelates spatio-temporal dependencies in graph signals.
problem Complex spatio-temporal dependencies in graph-structured signals are not well captured by standard forecasting models.
method FreST Loss extends supervision to the joint spatio-temporal spectrum using Joint Fourier Transform (JFT).
result FreST Loss reduces estimation bias and improves forecasting accuracy on real-world datasets.
New ELBO formulation improves multimodal learning.
problem Existing ELBO models struggle with multimodal data.
method Proposes a generalized ELBO for multimodal data.
result Demonstrates improved performance over state-of-the-art models.
The Statistical Learning Theory (SLT) provides the theoretical guarantees for supervised machine learning based on the Empirical Risk Minimization Principle (ERMP). Such principle defines an upper bound to ensure the uniform convergence of the empirical risk Remp(f), i.e., the error measured on a given data sample, to …
Improved neural topic model for semi-supervised learning.
problem Representing textual data in an interpretable manner with limited labeled data.
method Label-Indexed Neural Topic Model (LI-NTM) that combines deep generative models with semi-supervised learning.
result LI-NTM outperforms existing models in document reconstruction and classifier performance.
Graph-based methods for signal processing have shown promise for the analysis of data exhibiting irregular structure, such as those found in social, transportation, and sensor networks. Yet, though these systems are often dynamic, state-of-the-art methods for signal processing on graphs ignore the dimension of time, tr…
Self-supervised learning, which learns by constructing artificial labels given only the input signals, has recently gained considerable attention for learning representations with unlabeled datasets, i.e., learning without any human-annotated supervision. In this paper, we show that such a technique can be used to sign…
SiMLR reduces complex biomedical data into simpler, interpretable forms.
problem Handling high-dimensional biomedical data for better understanding and prediction.
method Similarity-driven multi-view linear reconstruction (SiMLR) with novel objective function and regularization.
result SiMLR outperforms other methods in various biomedical datasets.
New methods improve Bayesian inference and decision-making in online learning.
problem Current Bayesian deep learning does not fully utilize joint predictives for sequential decision-making.
method Proposes new evaluation settings for active learning and active sampling, focusing on marginal and joint cross-entropies.
result Initial experiments suggest challenges in applying current BDL inference techniques in high-dimensional spaces.
We propose the supervised hierarchical Dirichlet process (sHDP), a nonparametric generative model for the joint distribution of a group of observations and a response variable directly associated with that whole group. We compare the sHDP with another leading method for regression on grouped data, the supervised latent…
Traditionally, when generative models of data are developed via deep architectures, greedy layer-wise pre-training is employed. In a well-trained model, the lower layer of the architecture models the data distribution conditional upon the hidden variables, while the higher layers model the hidden distribution prior. Bu…
New framework improves LLM performance by avoiding forgetting during sequential training stages.
problem Forgetting during sequential training stages of LLMs.
method Proposes a joint post-training framework with theoretical convergence guarantees.
result Empirically outperforms sequential post-training framework by up to 23%.
Supervised machine learning (ML) algorithms are aimed at maximizing classification performance under available energy and storage constraints. They try to map the training data to the corresponding labels while ensuring generalizability to unseen data. However, they do not integrate meaning-based relationships among la…
COMBO network improves optical flow estimation by combining deep learning with brightness constancy.
problem Optical flow estimation using deep learning requires complex training schemes.
method COMBO network explicitly exploits brightness constancy and combines it with a data-driven approach.
result COMBO network outperforms state-of-the-art methods on various benchmarks.
ASGN uses active semi-supervised learning to predict molecular properties efficiently.
problem Predicting molecular properties with scarce labeled data and high computational cost.
method ASGN combines a teacher-student framework with active learning to handle joint representation and property learning.
result ASGN achieves remarkable performance in property prediction on public datasets.