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Found 24 papers, 6 papers with code
Learning From Scenarios for Stochastic Repairable Scheduling
We are interested in a stochastic scheduling problem, in which processing times are uncertain, which brings uncertain values in the constraints, and thus repair of an initial schedule may be needed.
Personalized Anomaly Detection in PPG Data using Representation Learning and Biometric Identification
This paper introduces a two-stage framework leveraging representation learning and personalization to improve anomaly detection performance in PPG data.
iPINNs: Incremental learning for Physics-informed neural networks
Physics-informed neural networks (PINNs) have recently become a powerful tool for solving partial differential equations (PDEs).
Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability
Unfortunately, there is high inter-subject variability in the SSL-learned representations, which makes working with this data more challenging when labeled data is scarce.
A view on model misspecification in uncertainty quantification
Estimating uncertainty of machine learning models is essential to assess the quality of the predictions that these models provide.
Continual Prune-and-Select: Class-incremental learning with specialized subnetworks
In particular, CP&S is capable of sequentially learning 10 tasks from ImageNet-1000 keeping an accuracy around 94% with negligible forgetting, a first-of-its-kind result in class-incremental learning.
Conversation Group Detection With Spatio-Temporal Context
These affinity values are also continuous in time, since relationships and group membership do not occur instantaneously, even though the ground truths of group membership are binary.
Neural network relief: a pruning algorithm based on neural activity
Current deep neural networks (DNNs) are overparameterized and use most of their neuronal connections during inference for each task.
A Brief Prehistory of Double Descent
In their thought-provoking paper [1], Belkin et al. illustrate and discuss the shape of risk curves in the context of modern high-complexity learners.
Characterizing multiple instance datasets
When performing a comparison of different MIL classifiers, it is important to understand the differences of the datasets, used in the comparison.
Unsupervised Learning of Sequence Representations by Autoencoders
Sequence data is challenging for machine learning approaches, because the lengths of the sequences may vary between samples.
Attended End-to-end Architecture for Age Estimation from Facial Expression Videos
In this paper, we present an end-to-end architecture for age estimation, called Spatially-Indexed Attention Model (SIAM), which is able to simultaneously learn both the appearance and dynamics of age from raw videos of facial expressions.
Interacting Attention-gated Recurrent Networks for Recommendation
In particular, we propose a novel attention scheme to learn the attention scores of user and item history in an interacting way, thus to account for the dependencies between user and item dynamics in shaping user-item interactions.
Classification of COPD with Multiple Instance Learning
Chronic obstructive pulmonary disease (COPD) is a lung disease where early detection benefits the survival rate.
Label Stability in Multiple Instance Learning
We address the problem of \emph{instance label stability} in multiple instance learning (MIL) classifiers.
Temporal Attention-Gated Model for Robust Sequence Classification
An important advantage of our approach is interpretability since the temporal attention weights provide a meaningful value for the salience of each time step in the sequence.
Modeling Time Series Similarity with Siamese Recurrent Networks
Traditional techniques for measuring similarities between time series are based on handcrafted similarity measures, whereas more recent learning-based approaches cannot exploit external supervision.
Survey on the attention based RNN model and its applications in computer vision
In this survey, we introduce some attention based RNN models which can focus on different parts of the input for each output item, in order to explore and take advantage of the implicit relations between the input and the output items.
Time Series Classification using the Hidden-Unit Logistic Model
We present a new model for time series classification, called the hidden-unit logistic model, that uses binary stochastic hidden units to model latent structure in the data.
On Classification with Bags, Groups and Sets
To better deal with such problems, several extensions of supervised learning have been proposed, where either training and/or test objects are sets of feature vectors.
Quantile Representation for Indirect Immunofluorescence Image Classification
Considering one whole slide as a collection (a bag) of feature vectors, however, poses the problem of how to handle this bag.
Dissimilarity-based Ensembles for Multiple Instance Learning
In multiple instance learning, objects are sets (bags) of feature vectors (instances) rather than individual feature vectors.
Multiple Instance Learning with Bag Dissimilarities
Multiple instance learning (MIL) is concerned with learning from sets (bags) of objects (instances), where the individual instance labels are ambiguous.
