The increasing occurrence of ordinal data, mainly sociodemographic, led to a renewed research interest in ordinal regression, i.e. the prediction of ordered classes. Besides model accuracy, the interpretation of these models itself is of high relevance, and existing approaches therefore enforce e.g. model sparsity. For…
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48 results for “relevance models”
System detects relevant financial news and predictions from unstructured text.
problem Manual extraction of relevant financial information from news is cumbersome and error-prone.
method Topic modeling with LDA, co-reference resolution, multi-paragraph segmentation, and temporal analysis.
result ROUGE-L values for relevant text and predictions/forecasts were 0.662 and 0.982, respectively.
Methodology to measure lag relevance in time series models.
problem Measuring lag relevance in machine learning models for univariate time series.
method Ghost variables, Shapley values, additive importance measures, auto-relevance and partial auto-relevance functions, one-step forecast.
result Calculated relevance measures successfully demonstrate expected lag structure in almost all cases.
FDive helps analysts create relevant patterns in high-dimensional datasets.
problem Difficult detection of relevant patterns in large, high-dimensional datasets.
method FDive uses a visual active learning system with pattern-based similarity measures to rank and classify data.
result FDive enhances the quality and understanding of relevance models, leading to new insights.
Study evaluates relevance metrics for similarity-based model explanations.
problem Providing understandable explanations for complex model predictions.
method Evaluated three relevance metrics using three tests.
result Cosine similarity of gradients performs best for explanations.
Finding relevant information from large document collections such as the World Wide Web is a common task in our daily lives. Estimation of a user's interest or search intention is necessary to recommend and retrieve relevant information from these collections. We introduce a brain-information interface used for recomme…
Variable selection for Gaussian process models is often done using automatic relevance determination, which uses the inverse length-scale parameter of each input variable as a proxy for variable relevance. This implicitly determined relevance has several drawbacks that prevent the selection of optimal input variables i…
A new method exposes motion-related relevance in video frames.
problem Deconstructing relevance in spatio-temporal models for video processing.
method Proposes a discriminative method to separate spatial and temporal relevance.
result Demonstrates effectiveness on UCF-101 action recognition dataset.
The paper tackles feature selection for ordinal regression, considering feature redundancies and privileged information.
problem Discovering relevant factors in ranked data with potentially redundant features and privileged information.
method Develops feature relevance bounds for linear ordinal regression, considering feature redundancies and privileged information.
result Identifies all strongly and weakly relevant features and their type of relevance.
A Technique for Determining Relevance Scores of Process Activities using Graph-based Neural Networkscs.LG
GRM uses graph neural networks to score process activity relevance.
problem Improving business processes with performance measures.
method Graph Relevance Miner (GRM) based on graph neural networks.
result Quantitatively evaluated relevance scores with four datasets.
In this paper, we tackle the problem of explanations in a deep-learning based model for recommendations by leveraging the technique of layer-wise relevance propagation. We use a Deep Convolutional Neural Network to extract relevant features from the input images before identifying similarity between the images in featu…
Federated learning method selects relevant data to improve model accuracy.
problem Noisy and irrelevant data negatively impacts model training in federated learning.
method Use a benchmark model to evaluate and select relevant data subsets from each client.
result Up to 25% improvement in model accuracy compared to training with all data.
Proposes UICR to improve novelty in recommendation systems without sacrificing relevance.
problem Balancing relevance and novelty in recommendation systems is challenging, especially for long-tail items.
method Introduces uncertainty modeling in the matching stage and multi-task modeling of model and index uncertainty.
result Improves novelty without sacrificing relevance, as shown by experimental results and online A/B tests.
We improve neural network explainability by bypassing batch normalization.
problem Lack of transparency in neural networks.
method Layer-wise Relevance Propagation with a method to include normalization layers.
result Heatmaps are more accurate for convolutional layers with our method.
Paper aims to improve relevance of e-commerce search results.
problem Improving relevance of online product search results.
method Combines machine learning, NLP, and IR techniques to predict relevance scores.
result Deep learning models outperform conventional IR models in relevance prediction.
Pixel-wise relevance method shows how CNNs classify faces, varying across datasets and tasks.
problem Interpreting black-box CNN face recognition models.
method Layer-wise relevance propagation (LRP) applied to VGG-16 models trained for face recognition.
result Relevance maps are generally stable across random initializations and tasks, but less so across pretraining datasets.
Relevance vector machine (RVM) can be seen as a probabilistic version of support vector machines which is able to produce sparse solutions by linearly weighting a small number of basis functions instead using all of them. Regardless of a few merits of RVM such as giving probabilistic predictions and relax of parameter …
Sparse GEMINI selects relevant features for clustering without assumptions.
problem Feature selection in clustering with relevant clusters and variables.
method Discriminative clustering model maximizing GEMINI with l1 penalty.
result Sparse GEMINI selects relevant subsets of variables without prior hypotheses.
MambaLRP enhances Mamba models' explainability and performance.
problem Lack of transparency in Mamba models for real-world applications.
method Layer-wise Relevance Propagation (LRP) with relevance conservation axioms.
result MambaLRP provides stable and reliable explanations for Mamba models.
New method distinguishes feature relevance in non-linear contexts.
problem Finding relevant features with preserved redundancies.
method Random forest models and statistical methods.
result Distinguishes strong from weak feature relevance in non-linear problems.
InteractionNet models noncovalent protein-ligand interactions with GNNs and explains predictions.
problem Modeling noncovalent protein-ligand interactions with graph neural networks.
method InteractionNet uses a GNN architecture with separated covalent and noncovalent convolution layers and layer-wise relevance propagation for explainability.
result InteractionNet successfully predicts noncovalent protein-ligand interactions with chemical relevance.
Choppy optimizes ranked list truncation using Transformer architecture.
problem Optimal truncation of ranked search results to balance relevance and user cost.
method Assumption-free Transformer model optimizing user-defined IR metrics.
result Choppy improves upon recent state-of-the-art methods.
Selective relevance method improves motion explainability in 3D activity recognition models.
problem Models do not appropriately factor motion information into their decisions.
method Selective relevance method to adapt 2D explanation techniques for 3D inputs.
result Improves selectivity of motion explanations, revealing model's spatial bias.
PRI-VAE learns disentangled representations by optimizing principle-of-relevant-information.
problem Learning disentangled representations under VAE framework remains unknown.
method Proposes PRI-VAE, a novel learning objective to optimize disentanglement.
result Demonstrates effectiveness of PRI-VAE on four benchmark datasets.
mRMR method selects relevant features for marketing models without redundancy.
problem Selecting relevant features from large feature spaces in machine learning.
method Extends mRMR framework with non-linear redundancy and model-based relevance measures.
result Implemented mRMR method in production for Uber's marketing machine learning platform.
We propose a novel VAE-based deep auto-encoder model that can learn disentangled latent representations in a fully unsupervised manner, endowed with the ability to identify all meaningful sources of variation and their cardinality. Our model, dubbed Relevance-Factor-VAE, leverages the total correlation (TC) in the late…
Proposes a new hyperprior and predictive criterion for weakly informative hyperprior in relevance vector machine.
problem Capturing non-homogeneous data structure with limited kernel functions.
method Uses inverse gamma hyperprior with a shape parameter close to zero and a scale parameter not close to zero. Applies multiple kernel method with different widths. Proposes extended predictive information criterion for scale parameter selection.
result Obtains a multiple kernel relevance vector regression model with good predictive accuracy.
Combines k-NN and RVM for improved classification accuracy.
problem Improving k-NN's performance by considering relevancy.
method Integrates k-NN and RVM in kernel space, introduces a new stopping parameter.
result Significantly prunes irrelevant attributes and improves classification accuracy.
The paper revisits and improves on a Bayesian relevance vector machine method for small sample sizes.
problem Statistical modeling with small sample sizes relative to the number of covariates.
method Introduces a new class of global-local priors and provides theoretical properties.
result Results on posterior consistency and contraction rates are provided.
Paper introduces MCRP for estimating feature relevance uncertainty in neural networks.
problem Lack of uncertainty in feature relevance for neural network decisions.
method Monte Carlo Relevance Propagation (MCRP) method.
result Allows deeper understanding of neural networks' perception and reasoning.
New method selects relevant dimensions for better prediction in mixtures.
problem Learning mixtures with limited components for prediction tasks.
method Prediction-focused modeling for mixtures.
result Improves prediction performance compared to non-focused models.
Paper addresses bias in search intent affecting click behavior.
problem Bias in user search intent affects click behavior and relevance.
method Proposes a search intent bias hypothesis to improve click models.
result Click models can better interpret user clicks and improve retrieval performance.
The paper proposes a method to detect relevant model degradations without over-alerting.
problem Detecting meaningful changes in machine learning model performance over time.
method Sequential monitoring scheme accounting for temporal dependence and multiple testing issues.
result The proposed method outperforms benchmark methods in detecting relevant changes in model quality.
Data coarse graining can improve model performancecond-mat.stat-mech
Data coarse graining improves model performance by filtering out less relevant features.
problem Lossy data transformations lose information but can improve model generalization.
method Data coarse graining schemes that systematically discard features based on relevance to the learning task.
result A 'high-pass' scheme helps models generalize better by filtering out less relevant features.
We consider the problem of clustering data points in high dimensions, i.e. when the number of data points may be much smaller than the number of dimensions. Specifically, we consider a Gaussian mixture model (GMM) with non-spherical Gaussian components, where the clusters are distinguished by only a few relevant dimens…
We present a representation for describing transition models in complex uncertain domains using relational rules. For any action, a rule selects a set of relevant objects and computes a distribution over properties of just those objects in the resulting state given their properties in the previous state. An iterative g…
Models extract relevant EHR snippets to aid radiologists in diagnosis.
problem Difficulty in identifying relevant patient record information for diagnosis.
method Distantly supervised transformer-based neural model for extractive summarization.
result Models yield better extractive summaries than unsupervised approaches.
Click-through rate (CTR) is a key signal of relevance for search engine results, both organic and sponsored. CTR of a result has two core components: (a) the probability of examination of a result by a user, and (b) the perceived relevance of the result given that it has been examined by the user. There has been consid…
This paper improves image retrieval accuracy through novel relevance feedback methods.
problem Improving image retrieval accuracy in Content-Based Image Retrieval (CBIR).
method Novel addition to feature re-weighting and classification techniques, focusing on 0-th iteration improvement.
result Significantly improved retrieval accuracy from relevance feedback.
We propose in this contribution a method for l one regularization in prototype based relevance learning vector quantization (LVQ) for sparse relevance profiles. Sparse relevance profiles in hyperspectral data analysis fade down those spectral bands which are not necessary for classification. In particular, we consider …
Aiming for Relevancecs.LG
Novel metrics improve machine learning models for ICU patient care.
problem Predicting vital sign trajectories for early detection of adverse events.
method Developed novel performance metrics aligned with clinical contexts, validated on simulated and real datasets, and optimized neural networks using these metrics.
result Neural networks trained with these metrics excel in predicting clinically significant events.
Paper proposes a method to make learned models focus on task-relevant information.
problem Mismatch between model objective and downstream task objective.
method Direct prediction towards task-relevant information, self-supervised.
result Model more effectively models relevant parts of the scene conditioned on the goal.
Systems rank PubMed abstracts and sentences for RDoC criteria, achieving high mAP and MAA.
problem Lack of RDoC labeled datasets and complex labelling process hinder full use of RDoC framework.
method Attention-based neural topic models, supervised and unsupervised sentence ranking models, BM25, BoW, TF-IDF.
result Best systems achieved 1st rank with 0.86 mAP and 0.58 MAA.
The paper addresses the relevance problem in statistical inference.
problem The relevance problem in statistical inference from large-scale data.
method Not specified in the abstract, likely involves statistical methods and analysis of large-scale data.
result The relevance problem is a long-neglected topic in statistical inference.
Exponential Family Attentionstat.ML
EFA extends self-attention to handle mixed data types and dynamic relevance.
problem Handling high-dimensional, mixed data types with dynamic relevance.
method Probabilistic generative model using self-attention and latent factor model.
result EFA consistently outperforms existing models in complex latent structure capture and reconstruction.
A method to assess variable importance in complex predictive models.
problem Assessing the importance of variables in complex predictive models.
method Assigning relevance measures to each variable by comparing predictions with a ghost variable and analyzing joint effects.
result The method provides insights into variable importance and joint effects not available with other methods.
The relevance of data quantifies learning efficiency.
problem Understanding the statistical nature of high-dimensional, sparse data.
method Defining relevance as information content, and using it to define ideal limits of samples and learning machines.
result Maximally informative samples and optimal learning machines exhibit critical features like power-law frequency distributions and anomalously large susceptibility.
Neural network framework for language recognition considers sequence information and improves accuracy.
problem Challenging task of automatic language identification in noisy conditions.
method Proposes a neural network framework with bidirectional LSTM and attention modeling for relevance weighting.
result Significant improvements over conventional methods in noisy conditions and multi-speaker speech.