Papers Meta-Learning
“Meta-Learning” 태그가 달린 논문 3,569편 · 필터 해제
Are encoders able to learn landmarkers for warm-starting of Hyperparameter Optimization?
Effectively representing heterogeneous tabular datasets for meta-learning purposes is still an open problem. Previous approaches rely on representations that are intended to be universal. This paper proposes two novel me…
Hyperparameter OptimizationMeta-LearningMetric LearningRepresentation LearningImbalanced Regression Pipeline Recommendation
Imbalanced problems are prevalent in various real-world scenarios and are extensively explored in classification tasks. However, they also present challenges for regression tasks due to the rarity of certain target value…
AutoMLMeta-LearningregressionCLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy Labels
Learning with noisy labels (LNL) is essential for training deep neural networks with imperfect data. Meta-learning approaches have achieved success by using a clean unbiased labeled set to train a robust model. However, …
Learning with noisy labelsMeta-LearningMixture of Experts in Large Language Models
This paper presents a comprehensive review of the Mixture-of-Experts (MoE) architecture in large language models, highlighting its ability to significantly enhance model performance while maintaining minimal computationa…
DiversityLanguage ModelingLanguage ModellingLarge Language Model+2Iceberg: Enhancing HLS Modeling with Synthetic Data
Deep learning-based prediction models for High-Level Synthesis (HLS) of hardware designs often struggle to generalize. In this paper, we study how to close the generalizability gap of these models through pretraining on …
Data AugmentationHigh-Level SynthesisLanguage ModelingLanguage Modelling+2Meta-Reinforcement Learning for Fast and Data-Efficient Spectrum Allocation in Dynamic Wireless Networks
The dynamic allocation of spectrum in 5G / 6G networks is critical to efficient resource utilization. However, applying traditional deep reinforcement learning (DRL) is often infeasible due to its immense sample complexi…
Deep Reinforcement LearningFairnessMeta-LearningMeta Reinforcement LearningGeo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection
Digital Twins (DT) have the potential to transform traffic management and operations by creating dynamic, virtual representations of transportation systems that sense conditions, analyze operations, and support decision-…
Computational EfficiencyFederated LearningLane DetectionMeta-Learning+1The Bayesian Approach to Continual Learning: An Overview
Continual learning is an online paradigm where a learner continually accumulates knowledge from different tasks encountered over sequential time steps. Importantly, the learner is required to extend and update its knowle…
Bayesian Inferenceclass-incremental learningClass Incremental LearningContinual Learning+4A statistical physics framework for optimal learning
Learning is a complex dynamical process shaped by a range of interconnected decisions. Careful design of hyperparameter schedules for artificial neural networks or efficient allocation of cognitive resources by biologica…
DenoisingMeta-LearningCHOMET: Conditional Handovers via Meta-Learning
Handovers (HOs) are the cornerstone of modern cellular networks for enabling seamless connectivity to a vast and diverse number of mobile users. However, as mobile networks become more complex with more diverse users and…
Meta-LearningEstimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning
In scientific domains -- from biology to the social sciences -- many questions boil down to \textit{What effect will we observe if we intervene on a particular variable?} If the causal relationships (e.g.~a causal graph)…
Bayesian InferenceCausal InferenceMeta-LearningMeta-Learning Transformers to Improve In-Context Generalization
In-context learning enables transformer models to generalize to new tasks based solely on input prompts, without any need for weight updates. However, existing training paradigms typically rely on large, unstructured dat…
In-Context LearningMeta-LearningAcquiring and Adapting Priors for Novel Tasks via Neural Meta-Architectures
The ability to transfer knowledge from prior experiences to novel tasks stands as a pivotal capability of intelligent agents, including both humans and computational models. This principle forms the basis of transfer lea…
3D GenerationComputational chemistryMeta-LearningMolecular Property Prediction+4High-Order Deep Meta-Learning with Category-Theoretic Interpretation
We introduce a new hierarchical deep learning framework for recursive higher-order meta-learning that enables neural networks (NNs) to construct, solve, and generalise across hierarchies of tasks. Central to this approac…
Meta-LearningTransfer LearningAutomated Grading of Students' Handwritten Graphs: A Comparison of Meta-Learning and Vision-Large Language Models
With the rise of online learning, the demand for efficient and consistent assessment in mathematics has significantly increased over the past decade. Machine Learning (ML), particularly Natural Language Processing (NLP),…
Meta-LearningMC-INR: Efficient Encoding of Multivariate Scientific Simulation Data using Meta-Learning and Clustered Implicit Neural Representations
Implicit Neural Representations (INRs) are widely used to encode data as continuous functions, enabling the visualization of large-scale multivariate scientific simulation data with reduced memory usage. However, existin…
ClusteringMeta-LearningCan Gradient Descent Simulate Prompting?
There are two primary ways of incorporating new information into a language model (LM): changing its prompt or changing its parameters, e.g. via fine-tuning. Parameter updates incur no long-term storage cost for model ch…
Meta-LearningTailored Conversations beyond LLMs: A RL-Based Dialogue Manager
In this work, we propose a novel framework that integrates large language models (LLMs) with an RL-based dialogue manager for open-ended dialogue with a specific goal. By leveraging hierarchical reinforcement learning to…
Hierarchical Reinforcement LearningMeta-LearningFAF: A Feature-Adaptive Framework for Few-Shot Time Series Forecasting
Multi-task and few-shot time series forecasting tasks are commonly encountered in scenarios such as the launch of new products in different cities. However, traditional time series forecasting methods suffer from insuffi…
Meta-LearningTime SeriesTime Series ForecastingDIP: Unsupervised Dense In-Context Post-training of Visual Representations
We introduce DIP, a novel unsupervised post-training method designed to enhance dense image representations in large-scale pretrained vision encoders for in-context scene understanding. Unlike prior approaches that rely …
GPUMeta-LearningScene Understanding