Prototype networks on hyperspheres improve classification and regression.
problem Improving classification and regression performance.
method Using hyperspherical prototypes for classification and regression, optimizing prototypes through data-independent margin separation.
result Hyperspherical prototype networks outperform other methods in classification, regression, and their combination.
Gaussian prototypical networks improve few-shot learning on Omniglot.
problem Few-shot classification on the Omniglot dataset.
method Extends prototypical networks by incorporating uncertainty estimates as Gaussian covariance matrices to define a distance metric.
result Report state-of-the-art performance in 1-shot and 5-shot classification.
A neural network that explains its predictions through prototypes.
problem Lack of interpretability in deep neural networks.
method A novel network architecture with an autoencoder and prototype layer, trained with four terms.
result The network learns to explain its predictions through learned prototypes.
A new neural network method improves interpretability and detection of outliers.
problem Improving interpretability and detection of outliers in neural networks.
method Prototype-based learning (PbL) using a winner-take-all (WTA) network with two prototypes: positive and negative.
result The negative prototype is similar to the positive one, aligning with the BCM theory.
Prototype-based methods improve neural networks' robustness and interpretability.
problem Neural networks lack robustness and interpretability.
method Prototype-based vector quantization methods are integrated into neural networks.
result Established a strong theoretical framework for prototype-based neural network layers.
We propose prototypical networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class. Prototypical networks learn a metric space in which classification can be performed by computing distances…
Infinite mixture prototypes adapt to complex data for few-shot learning.
problem Few-shot learning with complex data distributions.
method Adaptive representation of classes by clusters, inferring cluster number.
result 25% absolute accuracy improvement on alphabets, state-of-the-art semi-supervised clustering.
Paper proposes integrating hierarchical class structure into prototypical network supervision.
problem Improving classification accuracy in tasks with hierarchical class structures.
method Integrates hierarchical class structure (metric) into prototypical network supervision.
result Consistent improvement of error rate weighted by the cost matrix compared to traditional methods.
ProtoryNet interprets text sequences using prototype trajectories for better understanding.
problem Improving text classification interpretability and accuracy.
method ProtoryNet uses prototype trajectories to interpret text sequences, with prototype pruning for better interpretability.
result ProtoryNet outperforms baseline models and reduces performance gap compared to black-box models.
Prototypical Networks improve multi-label classification accuracy.
problem Multi-label classification with nonlinear label dependencies.
method Formulate multi-label learning as class distribution in a non-linear embedding space. For each label, positive and negative embeddings are compactly distributed. Labels are inferred by measuring the distance to prototype positive or negative embeddings.
result Extensive experiments show improved accuracy compared to state-of-the-art algorithms.
Improved few-shot learning with unlabeled data using random walks.
problem Few-shot learning with limited labeled data.
method Prototypical Random Walk Networks (PRWN) with semi-supervised loss.
result Significant performance improvements in most benchmarks.
ProtoPNet uses deep learning to classify images by identifying prototypical parts.
problem Challenging image classification tasks where understanding reasoning is important.
method ProtoPNet architecture that reasons by finding prototypical parts and combining evidence.
result ProtoPNet achieves comparable accuracy to non-interpretable models and provides interpretability.
Prototype Matching Network (PMN) improves genomic TFBS prediction.
problem Predicting Transcription Factor Binding Sites (TFBSs) with hundreds of TFs as labels.
method Prototype Matching Network (PMN) that learns motif-like features and TF-TF interactions.
result PMN significantly outperforms baselines on a large TFBS dataset.
Algorithm generates new drug molecules from prototypes, showing diversity and validity.
problem Designing new drugs from existing prototypes is expensive and time-consuming.
method Conditional Diversity Networks (CDN) for unsupervised generation of drug molecules.
result Generated molecules are valid and significantly different from prototypes, including FDA-approved drugs.
Prototype model improves model auditing and understanding.
problem Auditing and understanding modern language models is expensive and approximate.
method Introduced a sparse, non-negative mixture of learned prototypes trained with clustering objectives.
result Prototype models either surpass or remain within 2.5 percentage points of dense baselines on downstream tasks.
New method trains prototypical few-shot models on single class.
problem Train few-shot models on single class with limited examples.
method Introduce 'null class' and use batch normalization; propose Gaussian layer for distance calculation.
result Achieved high accuracy on test sets (Omniglot: 98%, MiniImageNet: 80%).
DPTA improves CIL by adapting PTMs with dual prototypes.
problem Catastrophic forgetting in incremental learning with pre-trained models.
method Dual-Prototype Network with Task-wise Adaptation (DPTA).
result DPTA consistently outperforms recent methods by 1\%-5\% on multiple benchmarks.
The paper uses learned prototypes to explain deep learning models for time-series data.
problem Lack of explainable AI in deep learning models for high-risk decisions.
method Learned prototypes in latent space of deep learning models.
result Prototypes improve classification decisions and provide explainable insights.
Few-shot learning improved with semi-supervised and active methods.
problem Few-shot classification with limited labeled data.
method Prototypical Networks features combined with K-means clustering guided by few labeled examples and user feedback. result Active adaptation through user feedback significantly improves performance.
HitNet uses a Hit-or-Miss layer to enhance feature interpretability in capsule networks.
problem Difficulty in interpreting complex neural network architectures.
method Replacing the last layer with a Hit-or-Miss layer that trains capsules to hit or miss a target capsule using centripetal loss.
result HitNet achieves better performance than initial CapsNet on various datasets and provides interpretable feature representations.
Paper introduces a novel framework for set input tasks in meta-learning.
problem Meta-learning problems with set inputs often require efficient summary networks.
method Prototype-oriented optimal transport (POT) framework to improve summary networks.
result Significantly improves summary statistics from sets in meta-learning.
Graph Prototypical Networks improve few-shot node classification on attributed networks.
problem Few-shot node classification in attributed networks with limited labeled instances.
method Graph Prototypical Networks (GPN) using meta-learning to extract meta-knowledge and identify informative labeled instances.
result GPN achieves superior performance in few-shot node classification.
ProtoNAM models tabular data with neural networks, making predictions transparent.
problem Tabular data analysis using neural networks lacks transparency and accuracy compared to tree-based methods.
method ProtoNAM introduces prototypes into neural networks to model tabular data while maintaining explainability.
result ProtoNAM outperforms existing NN-based GAMs and provides insights into learned feature patterns.
A new method improves few-shot learning by combining ProtoNet with LFD.
problem Few-shot learning struggles with high variance support sets.
method Combines ProtoNet with Local Fisher Discriminant Analysis.
result Superior classification accuracy on miniImageNet and tieredImageNet.
DDCL-INCRT learns its own structure during training, reducing unnecessary complexity.
problem Fixed-size neural network architectures require manual tuning, leading to overfitting.
method Combines DDCL (Deep Dual Competitive Learning) and INCRT (Incremental Transformer) to self-organize network structure.
result The network self-organizes into a hierarchy of heads, reducing unnecessary complexity.
SLEEPER combines deep learning with expert rules for accurate sleep staging.
problem Manual sleep staging is tedious and requires expert time.
method SLEEPER uses convolutional neural networks and expert rules to generate interpretable models.
result SLEEPER achieves comparable accuracy to human experts and deep neural networks.
PPN learns from weakly-labeled data to improve few-shot learning.
problem Few-shot learning with limited labeled data.
method Prototype Propagation Network (PPN) trained on few-shot tasks with coarse-label data.
result PPN significantly outperforms other methods on benchmarks.
A probabilistic method for deep embedding that improves classification accuracy and interpretability.
problem Improving classification accuracy and interpretability in deep learning.
method A probabilistic approach that treats embeddings as random variables, using a product distribution over labeled instances and marginalizing prototype proximity.
result Superior large- and open-set classification accuracy compared to state-of-the-art methods.
FiberNet integrates geometry into machine learning for clearer classification.
problem Lack of interpretability in traditional deep learning.
method Reformulates classification as geometric optimization on fiber bundles, introducing learnable Riemannian metrics and variational prototype optimization.
result Clear geometric interpretability and efficiency in classification.
New method improves few-shot learning with randomized SPSA.
problem Training classifiers on limited examples of new classes.
method Randomized stochastic approximation and prototypical networks.
result The proposed method outperforms original prototypical networks.
Adaptive RNN using mixture layer for multi-pattern sequences.
problem Inadequate RNN performance on sequences with multiple patterns.
method Introducing a mixture layer to partition and store prototype vectors, enabling adaptive state updates.
result M-RNN outperforms traditional RNN in assimilating sequences with multiple patterns.
Prototype-based memory network learns visual categories from unlabeled data.
problem Learning from nonstationary, unlabeled data with sequential dependencies.
method Online prototype-based memory network with contrastive loss.
result Significantly better category recognition compared to state-of-the-art methods.
Prototypal analysis is introduced to overcome two shortcomings of archetypal analysis: its sensitivity to outliers and its non-locality, which reduces its applicability as a learning tool. Same as archetypal analysis, prototypal analysis finds prototypes through convex combination of the data points and approximates th…
Optimal prototypes found for challenging pathological geometries.
problem Finding optimal prototypes for pathological geometries is challenging.
method Analytical and heuristic algorithms for finding nearly-optimal prototypes.
result Optimal prototypes can be found analytically for challenging geometries.
Bayesian meta-learning on relation graphs improves few-shot relation extraction.
problem Predicting relations in sentences with limited labeled examples.
method Bayesian meta-learning on a global relation graph, using graph neural networks and Langevin dynamics.
result Framework effectively learns and generalizes to new relations.
A new method explains Siamese neural networks using feature comparison and autoencoder.
problem Explaining the decision-making process of Siamese neural networks.
method Feature comparison at embedding level and autoencoder reconstruction.
result The method effectively explains Siamese neural networks using MNIST dataset.
Reformulates RBF networks for graph-based data.
problem Applying RBF networks to graph data.
method Reformulate RBF networks for adjacency matrices, derive gradient updates.
result Guaranteed same responses as vector-based RBF networks.
Method generates prototypes from small datasets for efficient learning.
problem Efficiently learning from small datasets with soft labels.
method Modular method for generating soft-label prototypical lines and Hierarchical Soft-Label Prototype k-Nearest Neighbor algorithm.
result High classification accuracy with significantly fewer prototypes than classes.
New networks interpret kernel decompositions for signal analysis.
problem Mode decomposition in signal analysis.
method Programmable and interpretable regression networks using kernels and data.
result Near machine precision recovery of signal modes under regularity and separation assumptions.
Paper optimizes hyperspherical prototypes for better class separation.
problem Previous HPL approaches either lack principled optimisation or are limited to one latent dimension.
method Develops a principled optimisation procedure and uses linear block codes to create well-separated prototypes in various dimensions.
result Optimal prototype placement is characterized with achievable and converse bounds, showing near-optimality.
A neural network learns word-referent associations across various contexts.
problem Learning word-referent associations in different contexts.
method A biologically inspired multi-layered architecture that takes images and phonemes as input, builds representations, and adjusts prototypes based on current context.
result The model achieves up to 78% accuracy in ambiguous situations and mimics human learning patterns.
Paper proposes interpretable RL policies from a mixture of experts.
problem Making RL policies transparent and understandable in real-world applications.
method Policy iteration scheme with interpretable experts and prototypical states.
result Proposed algorithm learns policies comparable to neural networks but more interpretable.
Proposes a Manifold Graph for semi-supervised image classification.
problem Improving semi-supervised image classification with limited labeled data.
method Graph networks for feature extraction, graph connectivity, and feature propagation. Prototype Generator for unlabeled data representation.
result Achieves state-of-the-art performance with significantly fewer labeled data.
Meta-learning improves few-shot learning by propagating knowledge across related classes on a graph.
problem Few-shot learning suffers from insufficient training data.
method Developed a Gated Propagation Network (GPN) that learns to propagate messages between prototypes of different classes on a graph.
result GPN outperforms recent meta-learning methods on benchmark datasets.
Improves few-shot learning by adding a large margin to metric-based methods.
problem Few-shot learning's challenge of generalizing well with limited data.
method Unified framework with large margin distance loss function.
result Significant performance improvement with minimal computational overhead.
Ensembles of decision trees perform well on many problems, but are not interpretable. In contrast to existing approaches in interpretability that focus on explaining relationships between features and predictions, we propose an alternative approach to interpret tree ensemble classifiers by surfacing representative poin…
Improved CSKS with limited data using novel loss functions and transfer learning.
problem Spotting keywords in continuous speech with limited training data.
method Combination of Prototypical networks' loss and metric loss with transfer learning.
result Improves F1 score by over 10%
Meta-learning improves few-shot classification with unlabeled data.
problem Learning from very few labeled examples and unlabeled examples of the same class.
method Extended Prototypical Networks trained on episodes with labeled and unlabeled data.
result Prototypical Networks can leverage unlabeled data to improve predictions.