CAOS aggregates multiple one-shot predictors for efficient uncertainty quantification.
problem Lack of principled uncertainty quantification in one-shot prediction.
method CAOS, a conformal framework that aggregates multiple one-shot predictors and uses a leave-one-out calibration scheme.
result CAOS produces smaller prediction sets with reliable coverage compared to split conformal baselines.
MetaR learns few-shot link prediction in KGs by transferring relation-specific meta info.
problem Few-shot link prediction in KGs with limited associative triples.
method MetaR framework focusing on transferring relation-specific meta information.
result MetaR achieves state-of-the-art results on few-shot link prediction benchmarks.
Theory explains how AI models can predict unseen tasks without labeled data.
problem Understanding how AI models can generalize to unseen tasks.
method Developed a theoretical framework to analyze zero-shot prediction.
result Identified key quantities and independence relationships for generalization.
One-shot federated learning method for prediction sets.
problem Constructing prediction sets in a one-shot federated learning setting.
method Quantile-of-quantiles estimator for one-shot federated learning with privacy.
result Achieves desired coverage and length similar to centralized setting.
Paper improves zero-shot protein stability prediction by clarifying free-energy foundations.
problem Improving zero-shot protein stability prediction using inverse folding models.
method Clarifying the free-energy foundations of inverse folding models and proposing better estimates of relative stability.
result Significant gains in zero-shot performance can be achieved with simple methods.
Paper proposes an inductive RGCN for few-shot link prediction in drug-repurposing.
problem Predicting rare interactions in drug-repurposing for novel diseases.
method Proposes an inductive RGCN to learn relation embeddings for few-shot learning.
result Significantly outperforms state-of-the-art models in few-shot learning tasks.
Enhances drug discovery models by understanding human language.
problem Low predictive quality of activity prediction models in drug discovery.
method Proposes a novel architecture with separate chemical and natural language input modules and a contrastive pre-training objective.
result Improves predictive performance on few-shot and zero-shot learning benchmarks.
Aggregates diverse zero-shot LLM outputs for better corporate disclosure classification.
problem Combining varied zero-shot LLM predictions for improved stock return prediction.
method Multi-prompt framework with three fixed zero-shot LLM classifiers, logistic meta-classifier aggregation.
result Aggregated model outperforms single classifiers and baseline models, increasing balanced accuracy from 0.566 to 0.606.
Hopfield networks improve reaction template prediction for few/zero-shot scenarios.
problem Predicting reaction templates for new molecules in CASP.
method Adapted Hopfield networks to associate reaction templates, molecules, and structural information.
result Significantly improved performance for templates with few or zero training examples.
Study efficient algorithms for one-shot federated conformal prediction.
problem Valid prediction sets in one-shot federated learning.
method Quantile-of-quantiles family of estimators and split conformal prediction.
result No significant loss in coverage and length compared to centralized setting.
Memory-enhanced model predicts tennis shots based on player history.
problem Predicting shot location and type in tennis.
method Semi-supervised Generative Adversarial Network with neural memory modules.
result The model learns player-specific behavioral patterns from match data.
Meta-Graph learns to predict missing edges quickly from few samples.
problem Few-shot link prediction on graphs, especially when samples are sparse.
method Meta-learning framework that uses higher-order gradients and learned graph signatures.
result Meta-Graph can quickly adapt to new graphs using only a small sample of true edges.
Paper proposes a new framework for predictive optimization without training data.
problem Prediction in a new domain without training samples.
method Proposes a simple framework for predictive optimization with zero-shot domain adaptation.
result Demonstrates the potential usefulness of the proposed framework through numerical experiments.
BayPrAnoMeta tackles few-shot industrial image anomaly detection with Bayesian methods.
problem Challenges in industrial image anomaly detection, especially class imbalance and scarcity of labeled samples.
method Bayesian Proto-MAML approach with probabilistic normality models and Bayesian posterior predictive likelihood.
result Consistent and significant AUROC improvements over existing methods in few-shot anomaly detection.
Theoretical analysis improves few-shot learning performance.
problem Optimizing the number of labeled examples per category in few-shot learning.
method Theoretical analysis of Prototypical Networks, proposing a robust method to the shot number.
result Model trained for arbitrary meta-training shot number performs well across different meta-testing shot numbers.
Cross-Modulation Networks improve few-shot learning by combining information at multiple levels.
problem Few-shot learning challenges in deep feature extraction.
method Integrates support and query examples at various levels of abstraction using a feature-wise modulation mechanism.
result Encouraging initial results on miniImageNet, closing the gap with state-of-the-art.
Meta-learned confidence improves few-shot learning accuracy.
problem Improving accuracy in few-shot learning with unreliable model confidence.
method Meta-learning confidence weights for query samples to improve transductive inference performance.
result Meta-learned confidence leads to new state-of-the-art results on benchmark datasets.
In principle, zero-shot learning makes it possible to train a recognition model simply by specifying the category's attributes. For example, with classifiers for generic attributes like \emph{striped} and \emph{four-legged}, one can construct a classifier for the zebra category by enumerating which properties it posses…
GEN tackles few-shot out-of-graph link prediction in evolving multi-relational graphs.
problem Predicting links between unseen nodes in evolving multi-relational graphs with few edges per node.
method Transductive meta-learning framework (GEN) for inductive and transductive inference.
result GEN significantly outperforms relevant baselines for out-of-graph link prediction tasks.
Novel LSTM network predicts pulsar timing residuals with few-shot data.
problem Predicting pulsar timing residuals with limited data.
method Long Short-Term Memory (LSTM) network optimized with model-agnostic meta-learning and particle swarm optimization.
result Robust generalization and accurate predictions across high-frequency test domains with minimal data.
Paper proposes E3BM for robust few-shot learning with few examples.
problem Few-shot learning with limited data leads to poor model performance.
method Meta-learn ensemble of epoch-wise empirical Bayes models (E3BM).
result Top performance achieved using epoch-dependent transductive hyperprior learner.
Memory augmented neural networks improve active learning for one-shot predictions.
problem Scarcity and cost of labeled training data in deep architectures.
method Memory augmented neural networks and Class Margin Sampling (CMS) for reinforcement learning.
result The proposed method outperforms existing baselines in label predictions and reduces label requests.
AMP0 predicts antimicrobial peptides targeting specific microbes.
problem Low-throughput screening of antimicrobial peptides.
method Zero-shot and few-shot machine learning.
result AMP0 can predict antimicrobial activity against specific microbes.
A statistical model predicts generalization in few-shot learning.
problem Lack of validation sets in few-shot learning makes generalization estimation difficult.
method Introduced a Gaussian model of feature distribution and an unbiased estimator for class-conditional density distances.
result Our approach outperforms alternatives like leave-one-out cross-validation.
Study evaluates ensemble methods for zero-shot uncertainty quantification with diffusion models.
problem Quantifying uncertainty in zero-shot regression problems using diffusion models.
method Used diffusion probabilistic models for ensemble prediction and evaluated their effectiveness on various regression tasks.
result Ensemble methods consistently improve model prediction accuracy across different regression tasks.
Method predicts spatial values with few data using GP framework.
problem Few data limit predictive performance in spatial regression.
method Trains neural network to infer task representation from small data, uses GP framework to predict spatial values.
result Proposed method achieves better predictive performance than meta-learning methods.
LaT-PFN model predicts time series with zero-shot capability.
problem Zero-shot time series forecasting.
method In-context latent space learning with JEPA and PFN integration.
result Superior zero-shot predictions compared to baselines.
New metric improves latent dynamics inference from neural data.
problem Limitations of co-smoothing in predicting latent dynamics.
method Few-shot co-smoothing to assess latent dynamics.
result High co-smoothing models often have extraneous dynamics, which few-shot co-smoothing detects.
A fast single-shot MC dropout method for neural networks.
problem Inability of DNNs to provide uncertainty measures for new situations.
method Analytically approximates MC dropout for fully connected networks.
result Approach preserves BDNN advantages while being faster.
Hybrid model improves few-shot learning across diverse tasks.
problem Few-shot learning with limited data.
method Combines optimization and metric-based approaches.
result Superior performance across various settings.
Study shows zero-shot super-resolution in neural operators is impossible in many cases.
problem Understanding the theoretical limits of zero-shot super-resolution in neural operators.
method Systematic theoretical study including information-theoretic and generalization bounds analysis.
result Zero-shot super-resolution is information-theoretically impossible in many settings.
Study evaluates ZSL methods for unseen hashtag predictions from tweet text.
problem Lack of labeled data for all possible hashtag labels in supervised training.
method Proposed a Zero Shot Learning (ZSL) paradigm to predict unseen hashtag labels.
result Demonstrated effectiveness and scalability of ZSL methods for unseen hashtag recommendations.
Meta-learning reduces set prediction size in conformal prediction for few-shot calibration.
problem Inefficient set prediction in conformal prediction for limited training data.
method Meta-learning approach using cross-validation-based conformal prediction.
result Meta-learning scheme reduces set prediction size and preserves formal guarantees.
Paper tackles target shift in zero-shot learning using adversarial learning.
problem Target shift in zero-shot learning leads to performance degradation.
method Estimates target shift using class-attribute mapping and applies grouped adversarial learning.
result Improves zero-shot learning performance on multiple datasets.
The paper proposes methods to predict classifier generalization with few labeled samples.
problem Measuring classifier generalization with limited labeled data.
method Analysis of generalization variability, transfer-based solutions in supervised, semi-supervised, and unsupervised settings.
result Simple measures correlate with classifier generalization and can predict it with confidence.
Cluster-based ZSL for multivariate data predicts unseen classes.
problem Predicting unseen classes in multivariate data without labeled training data.
method Cluster-based approach: classify data based on its distance from training clusters.
result The method outperforms existing ZSL methods for multivariate binary classification.
This paper introduces a new framework for data efficient and versatile learning. Specifically: 1) We develop ML-PIP, a general framework for Meta-Learning approximate Probabilistic Inference for Prediction. ML-PIP extends existing probabilistic interpretations of meta-learning to cover a broad class of methods. 2) We i…
Enhances few-shot image classification using unlabelled examples.
problem Few-shot image classification with limited labeled data.
method Transductive meta-learning combining soft k-means clustering and neural feature extractor.
result State-of-the-art performance on Meta-Dataset, mini-ImageNet, and tiered-ImageNet benchmarks.
Improved few-shot learning with interpretable models.
problem Few-shot learning with limited data.
method Linear Distillation Learning using linear functions for each class.
result Better performance compared to other interpretable models.
ChatGPT struggles in predicting stock movements, underperforming traditional methods.
problem Predicting stock market movements using ChatGPT.
method Zero-shot analysis of ChatGPT's multimodal stock prediction capabilities.
result ChatGPT underperforms traditional methods and state-of-the-art models in predicting stock movements.
LLMs predict SGD convergence without training on specific data.
problem Predicting SGD convergence without labeled data.
method Link between SGD and Markov chains, leveraging LLMs' understanding of dynamical systems.
result Zero-shot prediction of SGD convergence for new starting points.
FROB model improves robustness and reliable confidence for few-shot OoD detection.
problem Challenges in few-shot classification and OoD detection due to limited samples and adversarial attacks.
method FROB model combines support boundary generation and few-shot Outlier Exposure (OE) for improved robustness and reliable confidence.
result FROB achieves generalization to unseen OoD and maintains robustness independent of few-shot number.
Adapts pretrained models to new classes without additional training.
problem Training models on limited labels and predicting new, unseen classes.
method Uses Fréchet mean instead of argmax for prediction, leveraging metric space distances.
result Improves model performance on unseen classes by up to 29.7% on ImageNet.
Meta metric learning improves few-shot learning for diverse domains.
problem Few-shot learning struggles with diverse domains and varying label numbers.
method Task-specific learners with metric learning and a meta learner to discover task-specific metrics.
result Meta metric learning achieves superior performance in diverse multi-domain tasks and flexible label numbers.
Unified framework explains few-shot multimodal medical imaging performance.
problem Limited labeled data in rare diseases and low-resource settings.
method PAC learning, VC theory, PAC Bayesian analysis, information gain, Chain of Thought reasoning.
result Unified theoretical framework for few-shot multimodal medical imaging.
A new method transfers knowledge without data, matching teacher's predictions closely.
problem Lack of access to training data for knowledge transfer.
method Adversarial training to match teacher's predictions without data.
result Zero-shot student performs well on CIFAR10, improving state-of-the-art.
Adaptive meta-learning improves few-shot learning and federated learning performance.
problem Improving few-shot learning and federated learning performance.
method Adaptive gradient-based meta-learning methods integrating online convex optimization and sequential prediction algorithms.
result Improved meta-test-time performance on standard problems in few-shot learning and federated learning.
Fine-tuning harms in-context learning, but restricting updates to the value matrix improves zero-shot performance.
problem Fine-tuning harms in-context learning, reducing zero-shot performance on unseen tasks.
method Theoretical analysis of linear attention models, identifying conditions for degraded few-shot performance.
result Restricting updates to the value matrix improves zero-shot performance while preserving in-context learning.