Activity recognition from sensor data deals with various challenges, such as overlapping activities, activity labeling, and activity detection. Although each challenge in the field of recognition has great importance, the most important one refers to online activity recognition. The present study tries to use online hi…
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48 results for “Activation Methods”
New method speeds up neural kernel computations for various activations.
problem Inefficient computation of neural kernels for general activations.
method Fast sketching method using truncated Hermite expansion.
result 106x speedup for approximate CNTK computation on CIFAR-10.
Active learning method balances bias and variance under class imbalance.
problem Active learning under label shift when class proportions differ.
method Mediated Active Learning under Label Shift (MALLS) using a 'medial distribution'.
result MALLS reduces asymptotic sample complexity under arbitrary label shift.
DAL uses disentanglement for automatic labeling in GAN-based active learning.
problem Reducing human labeling in GAN-based active learning.
method DAL leverages disentanglement in InfoGAN to automatically label datapoints, deciding human labeling based on disagreement with InfoGAN labels and label correction.
result DAL achieves better performance than existing GAN-based active learning approaches on image classification tasks.
Active learning method improves AI performance by balancing exploration and exploitation.
problem Efficiently acquiring samples for supervised learning in streaming data.
method Ensemble active learning by contextual bandits.
result Improved AI modeling performance through better sample acquisition.
New active learning methods for Gaussian process improve predictive modeling of composite fuselage.
problem Improving predictive modeling of composite fuselage with limited training samples and uncertainties.
method Proposed two new active learning algorithms for Gaussian process considering uncertainties.
result The proposed approach realizes better prediction performance for automatic shape control of composite fuselage.
Learning automatically the best activation function for the task is an active topic in neural network research. At the moment, despite promising results, it is still difficult to determine a method for learning an activation function that is at the same time theoretically simple and easy to implement. Moreover, most of…
New active learning method uses combinatorial coverage to improve data transfer and reduce bias.
problem Inability to transfer sampled data to new models and sampling bias issues.
method Data-centric active learning methods utilizing combinatorial coverage.
result Sampling data with coverage leads to better data transfer and competitive sampling bias.
An activation boundary for a neuron refers to a separating hyperplane that determines whether the neuron is activated or deactivated. It has been long considered in neural networks that the activations of neurons, rather than their exact output values, play the most important role in forming classification friendly par…
Collaborative filtering is a useful technique for exploiting the preference patterns of a group of users to predict the utility of items for the active user. In general, the performance of collaborative filtering depends on the number of rated examples given by the active user. The more the number of rated examples giv…
Bayesian active learning method improved for censored regression data.
problem Challenges in estimating BALD for censored regression data.
method Derived entropy and mutual information for censored distributions, developed -BALD objective, proposed novel modelling approach.
result Demonstrated -BALD outperforms other methods in censored regression.
In the last few years there has been a growing interest in Human Activity Recognition~(HAR) topic. Sensor-based HAR approaches, in particular, has been gaining more popularity owing to their privacy preserving nature. Furthermore, due to the widespread accessibility of the internet, a broad range of streaming-based app…
Improved video tracking accuracy with active learning.
problem Efficiently predicting object matches in videos with active learning.
method Adversarial approach for active learning with structured prediction domains.
result Better accuracy and computational efficiency for object tracking.
GAPA method provides efficient uncertainty quantification for pretrained networks.
problem Reliable uncertainty estimates for pretrained models are challenging.
method Post-hoc Gaussian Process Activations (GAPA) method that shifts Bayesian modeling from weights to activations.
result GAPA method provides efficient uncertainty quantification without altering the backbone's predictions.
An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class Classifierscs.LG
Active learning methods increase classification quality by means of user feedback. An important subcategory is active learning for outlier detection with one-class classifiers. While various methods in this category exist, selecting one for a given application scenario is difficult. This is because existing methods rel…
Efficient Human-in-the-Loop Active Learning: A Novel Framework for Data Labeling in AI Systemsstat.ML
A new AI framework optimizes expert labeling of unlabeled data.
problem Efficiently labeling unlabeled data for AI systems.
method Human-in-the-loop active learning framework with innovative query schemes.
result Higher accuracy and lower loss compared to other methods.
New method uses trainable activations to make BNNs behave like GPs.
problem Making Bayesian Neural Networks (BNNs) behave like Gaussian Processes (GPs).
method Introduced trainable activations and periodic activations to map GP priors to BNNs. Used 2-Wasserstein distance for optimization.
result Method consistently outperforms existing approaches or matches heuristic methods.
A new method for recommending groups and activities based on geo-social data.
problem Recommendation of groups and activities based on geo-social data with social constraints.
method Proposes an attentive geo-social group recommendation method with an attention mechanism and a spatial query algorithm.
result Significantly outperforms baseline methods in real-world datasets.
Proposes an offline active learning method for graphs.
problem Active learning on graphs where labeling is expensive.
method Two-stage biased sampling strategy considering informativeness and representativeness.
result Establishes a theoretical relationship between generalization error and number of nodes selected.
Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completionphysics.chem-ph
New method predicts activity coefficients for binary mixtures without using physical descriptors.
problem Predicting activity coefficients for unexplored binary mixtures.
method Probabilistic matrix factorization model.
result Method outperforms state-of-the-art models requiring less training effort.
Efficient active learning method defends against malicious mislabeling and data poisoning attacks.
problem Malicious mislabeling and data poisoning attacks on deep neural networks.
method Adversarial retraining and active learning with random sampling strategy.
result The proposed method achieves 89% accuracy with only one-third of the labeled data, significantly outperforming the baseline method.
Deep learning models outperform classical methods in forecasting neural activity.
problem Improving forecasting of neural activity using deep learning models.
method Systematic evaluation of eight probabilistic deep learning models against classical statistical models and baseline methods.
result Several deep learning models consistently outperform classical approaches in forecasting neural activity.
Proposes a graph-based method for active learning in regression.
problem Active learning for regression is understudied and faces challenges with noisy labels and parameter uncertainty.
method Formulates active regression as a bipartite graph optimization problem.
result Demonstrates the effectiveness of the proposed graph-based approach on benchmark data sets.
Neurons in cortical circuits exhibit coordinated spiking activity, and can produce correlated synchronous spikes during behavior and cognition. We recently developed a method for estimating the dynamics of correlated ensemble activity by combining a model of simultaneous neuronal interactions (e.g., a spin-glass model)…
New method improves active statistical inference by reducing noise.
problem Inaccurate uncertainty estimates in active sampling lead to noisy results.
method Robust sampling strategies that interpolate between uniform and active sampling based on uncertainty scores.
result The robust sampling ensures that the estimator is never worse than uniform sampling and usually outperforms active inference.
Proposes a method to improve deep active learning for NER tasks.
problem Weaknesses of existing deep active learning algorithms in practice.
method Estimates error decay curves of feature-defined subsets to improve sampling efficiency and robustness.
result Significantly outperforms diversification-based methods for black-box NER taggers and makes sampling more robust to labeling noise.
This paper proposes an active metric learning method for clustering with pairwise constraints.
problem Clustering with pairwise constraints and improving clustering performance.
method Active metric learning method that queries informative instance pairs and updates the learned metric sequentially.
result The proposed method enhances clustering performance and provides a tighter error bound.
Deep active learning improves solvability prediction in power systems.
problem Inconclusive conservatism in traditional solvability region analysis methods.
method Proposes a deep active learning framework to reduce labeled dataset size.
result Significantly reduces the size of labeled dataset for training.
We propose a new optimization method for training feed-forward neural networks. By rewriting the activation function as an equivalent proximal operator, we approximate a feed-forward neural network by adding the proximal operators to the objective function as penalties, hence we call the lifted proximal operator machin…
Study improves peptide design efficiency using active and meta-learning.
problem Designing novel functional peptides is inefficient due to low throughput and lack of data.
method Investigated active learning and meta-learning for optimizing peptide design experiments.
result Meta-learning improved average accuracy, but neither method outperformed random choice.
New method learns neural network activation functions from data.
problem Learning activation functions for neural networks.
method Model each neuron's activation function as a small neural network.
result Learned activation functions improve network performance.
We propose two approaches of locally adaptive activation functions namely, layer-wise and neuron-wise locally adaptive activation functions, which improve the performance of deep and physics-informed neural networks. The local adaptation of activation function is achieved by introducing a scalable parameter in each lay…
New method uses neural tangent kernel for efficient active learning.
problem Efficiently approximating deep learning's look-ahead selection criteria.
method Approximates retraining with neural tangent kernel for active learning.
result Approximation works asymptotically and enables sequential active learning.
The Use of Unlabeled Data versus Labeled Data for Stopping Active Learning for Text Classificationcs.LG
Annotation of training data is the major bottleneck in the creation of text classification systems. Active learning is a commonly used technique to reduce the amount of training data one needs to label. A crucial aspect of active learning is determining when to stop labeling data. Three potential sources for informing …
Two machine learning methods detect insider trading from investor activity data.
problem Detecting insider trading from trading activity data is challenging.
method Two unsupervised machine learning methods: clustering and group identification.
result Identifies potential insider trading rings around price sensitive events.
ADS filters data points for efficient batch active learning.
problem Efficiently selecting data points for annotation in parallel settings.
method Active Data Shapley (ADS) using the Shapley value of data.
result Significantly increases efficiency of active learning by 6x.
Active Coveringcs.LG
Active learning method reduces label queries for positive examples.
problem Minimizing label queries for identifying all positive examples.
method Repurposed support estimator as offline algorithm, and a simple active learning method.
result Improved excess query cost of compared to offline methods.
Improves graph-based active learning for non-Gaussian models.
problem Efficiently selecting data points for labeling in graph-based semi-supervised learning.
method Approximates non-Gaussian distributions, introduces rank-one update and model change acquisition function.
result Enhanced active learning for graph-based SSL under non-Gaussian models.
Even though active learning forms an important pillar of machine learning, deep learning tools are not prevalent within it. Deep learning poses several difficulties when used in an active learning setting. First, active learning (AL) methods generally rely on being able to learn and update models from small amounts of …
Logistic regression is by far the most widely used classifier in real-world applications. In this paper, we benchmark the state-of-the-art active learning methods for logistic regression and discuss and illustrate their underlying characteristics. Experiments are carried out on three synthetic datasets and 44 real-worl…
Supervised machine learning methods usually require a large set of labeled examples for model training. However, in many real applications, there are plentiful unlabeled data but limited labeled data; and the acquisition of labels is costly. Active learning (AL) reduces the labeling cost by iteratively selecting the mo…
Improved active output selection reduces calibration time by 10% or more.
problem Efficiently calibrate models with noisy data.
method Improved active output selection strategy considering noise estimate.
result At least 10% fewer measurements needed compared to existing strategies.
New method uses GNNs and node feature propagation for active learning in graph node classification.
problem Lack of labeled data for graph neural networks.
method Node feature propagation followed by K-Medoids clustering for instance selection.
result Proposed method significantly outperforms other methods on benchmark datasets.
ICAL improves deep learning model accuracy and NLL with optimized batch labeling.
problem Deep Bayesian Active Learning for efficient model training.
method ICAL uses HSIC to measure dependency and optimizes batch size scaling.
result Significant improvements in model accuracy and NLL on image datasets.
Improved MPC with neural networks and active sets for large-scale problems.
problem Large-scale model predictive control with guarantees on feasibility and stability.
method Combines offline-trained neural network and online primal active set solver.
result Achieves 2x reduction in online inference time for large problems.
Active Kriging Monte Carlo simulation method with conformal certification for failure probability estimation
problem Failure probability estimation in structural reliability analysis
method Active learning framework with conformal prediction
result Improved uncertainty quantification and reliability of failure probability estimates
Multi-class Active Learning: A Hybrid Informative and Representative Criterion Inspired Approachcs.LG
Labeling each instance in a large dataset is extremely labor- and time- consuming . One way to alleviate this problem is active learning, which aims to which discover the most valuable instances for labeling to construct a powerful classifier. Considering both informativeness and representativeness provides a promising…
This paper presents a self-supervised method for visual detection of the active speaker in a multi-person spoken interaction scenario. Active speaker detection is a fundamental prerequisite for any artificial cognitive system attempting to acquire language in social settings. The proposed method is intended to compleme…