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← Learning Paradigms
Artificial Intelligence
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Learning Paradigms
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Transfer Learning
8,078 papers
Papers per year
2002: 2
2003: 1
2005: 3
2006: 5
2007: 12
2008: 7
2009: 7
2010: 16
2011: 15
2012: 13
2013: 35
2014: 32
2015: 40
2016: 72
2017: 149
2018: 341
2019: 619
2020: 711
2021: 992
2022: 980
2023: 1182
2024: 1282
2025: 1254
2026: 308
Papers
Sim and Real: Better Together
NIPS 2021
Teachable Reinforcement Learning via Advice Distillation
NIPS 2021
On the Universality of Graph Neural Networks on Large Random Graphs
NIPS 2021
Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification
NIPS 2021
LSH-SMILE: Locality Sensitive Hashing Accelerated Simulation and Learning
NIPS 2021
LEADS: Learning Dynamical Systems that Generalize Across Environments
NIPS 2021
A Provably Efficient Sample Collection Strategy for Reinforcement Learning
NIPS 2021
Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery
NIPS 2021
Ensembling Graph Predictions for AMR Parsing
NIPS 2021
Inverse Problems Leveraging Pre-trained Contrastive Representations
NIPS 2021
The Unbalanced Gromov Wasserstein Distance: Conic Formulation and Relaxation
NIPS 2021
Scalable Neural Data Server: A Data Recommender for Transfer Learning
NIPS 2021
Pareto-Optimal Learning-Augmented Algorithms for Online Conversion Problems
NIPS 2021
Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression
NIPS 2021
What’s a good imputation to predict with missing values?
NIPS 2021
Learning Stable Deep Dynamics Models for Partially Observed or Delayed Dynamical Systems
NIPS 2021
Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks
NIPS 2021
On Plasticity, Invariance, and Mutually Frozen Weights in Sequential Task Learning
NIPS 2021
Pretraining Representations for Data-Efficient Reinforcement Learning
NIPS 2021
Implicit Semantic Response Alignment for Partial Domain Adaptation
NIPS 2021
ToAlign: Task-Oriented Alignment for Unsupervised Domain Adaptation
NIPS 2021
Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions
NIPS 2021
Sequential Causal Imitation Learning with Unobserved Confounders
NIPS 2021
A Theoretical Analysis of Fine-tuning with Linear Teachers
NIPS 2021
Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning
NIPS 2021
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