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Meta-Learning

1524 directly classified papers

Papers per year

Papers

Human-Timescale Adaptation in an Open-Ended Task Space ICML 2023
Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot Classification NIPS 2023
MGDD: A Meta Generator for Fast Dataset Distillation NIPS 2023
CLUTR: Curriculum Learning via Unsupervised Task Representation Learning ICML 2023
Sample-efficient Multi-objective Molecular Optimization with GFlowNets NIPS 2023
Difficulty-Net: Learning To Predict Difficulty for Long-Tailed Recognition WACV 2023
Meta-Learning with Adjoint Methods AISTATS 2023
Meta-Learning for Adaptation of Deep Optical Flow Networks WACV 2023
Episodic Multi-Task Learning with Heterogeneous Neural Processes NIPS 2023
Contextual Stochastic Bilevel Optimization NIPS 2023
GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection NIPS 2023
Recasting Continual Learning as Sequence Modeling NIPS 2023
Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice JMLR 2023
Learning to Select Pivotal Samples for Meta Re-weighting AAAI 2023
Learning Large-scale Neural Fields via Context Pruned Meta-Learning NIPS 2023
A Simple Recipe to Meta-Learn Forward and Backward Transfer ICCV 2023
Learn TAROT with MENTOR: A Meta-Learned Self-Supervised Approach for Trajectory Prediction ICCV 2023
Stabilized In-Context Learning with Pre-trained Language Models for Few Shot Dialogue State Tracking EACL 2023
Learning an Explicit Hyper-parameter Prediction Function Conditioned on Tasks JMLR 2023
Delayed Feedback in Generalised Linear Bandits Revisited AISTATS 2023
Learning Functional Transduction NIPS 2023
MeCo: Zero-Shot NAS with One Data and Single Forward Pass via Minimum Eigenvalue of Correlation NIPS 2023
DiFA: Differentiable Feature Acquisition AAAI 2023
Set-to-Sequence Ranking-Based Concept-Aware Learning Path Recommendation AAAI 2023
Gradient Estimation for Binary Latent Variables via Gradient Variance Clipping AAAI 2023