"transfer learning" Papers
35 papers found
Implicit In-context Learning
Zhuowei Li, Zihao Xu, Ligong Han et al.
Meta-learning how to Share Credit among Macro-Actions
Ionel-Alexandru Hosu, Traian Rebedea, Razvan Pascanu
On Transferring Transferability: Towards a Theory for Size Generalization
Eitan Levin, Yuxin Ma, Mateo Diaz et al.
Optimal Learning of Kernel Logistic Regression for Complex Classification Scenarios
Hongwei Wen, Annika Betken, Hanyuan Hang
Rethinking Hebbian Principle: Low-Dimensional Structural Projection for Unsupervised Learning
Shikuang Deng, Jiayuan Zhang, Yuhang Wu et al.
Reward-Aware Proto-Representations in Reinforcement Learning
Hon Tik Tse, Siddarth Chandrasekar, Marlos C. Machado
${\rm E}(3)$-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning
Dingyang Chen, Qi Zhang
Bridging Data Gaps in Diffusion Models with Adversarial Noise-Based Transfer Learning
Xiyu Wang, Baijiong Lin, Daochang Liu et al.
CARTE: Pretraining and Transfer for Tabular Learning
Myung Jun Kim, Leo Grinsztajn, Gael Varoquaux
Contextual Pre-planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning
Guy Azran, Mohamad H Danesh, Stefano Albrecht et al.
Cooperative Knowledge Distillation: A Learner Agnostic Approach
Michael Livanos, Ian Davidson, Stephen Wong
DNA-SE: Towards Deep Neural-Nets Assisted Semiparametric Estimation
Qinshuo Liu, Zixin Wang, Xi'an Li et al.
Encodings for Prediction-based Neural Architecture Search
Yash Akhauri, Mohamed Abdelfattah
Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models
Francesca-Zhoufan Li, Ava Amini, Yisong Yue et al.
FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing
Yongzhe Jia, Xuyun Zhang, Amin Beheshti et al.
Fine-Tuning Graph Neural Networks by Preserving Graph Generative Patterns
Yifei Sun, Qi Zhu, Yang Yang et al.
Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem
Maciej Wołczyk, Bartłomiej Cupiał, Mateusz Ostaszewski et al.
Forget Sharpness: Perturbed Forgetting of Model Biases Within SAM Dynamics
Ankit Vani, Frederick Tung, Gabriel Oliveira et al.
Graph Positional and Structural Encoder
Semih Cantürk, Renming Liu, Olivier Lapointe-Gagné et al.
Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples
Thomas T. Zhang, Bruce Lee, Ingvar Ziemann et al.
Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective
Lei Zhao, Mengdi Wang, Yu Bai
Learning from Memory: Non-Parametric Memory Augmented Self-Supervised Learning of Visual Features
Thalles Silva, Helio Pedrini, Adín Ramírez Rivera
LION: Implicit Vision Prompt Tuning
Haixin Wang, Jianlong Chang, Yihang Zhai et al.
Matrix Information Theory for Self-Supervised Learning
Yifan Zhang, Zhiquan Tan, Jingqin Yang et al.
MF-CLR: Multi-Frequency Contrastive Learning Representation for Time Series
Jufang Duan, Wei Zheng, Yangzhou Du et al.
Minimum-Norm Interpolation Under Covariate Shift
Neil Mallinar, Austin Zane, Spencer Frei et al.
One Self-Configurable Model to Solve Many Abstract Visual Reasoning Problems
Mikołaj Małkiński, Jacek Mańdziuk
On Hypothesis Transfer Learning of Functional Linear Models
Haotian Lin, Matthew Reimherr
Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining
Florian Tramer, Gautam Kamath, Nicholas Carlini
Position: Will we run out of data? Limits of LLM scaling based on human-generated data
Pablo Villalobos, Anson Ho, Jaime Sevilla et al.
SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-Supervised Skeleton-Based Action Recognition
Cong Wu, Xiao-Jun Wu, Josef Kittler et al.
To Supervise or Not to Supervise: Understanding and Addressing the Key Challenges of Point Cloud Transfer Learning
Souhail Hadgi, Lei Li, Maks Ovsjanikov
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
Md Shamim Hussain, Mohammed Zaki, Dharmashankar Subramanian
Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts
Shengzhuang Chen, Jihoon Tack, Yunqiao Yang et al.
When is Transfer Learning Possible?
My Phan, Kianté Brantley, Stephanie Milani et al.