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Found 11 papers, 3 papers with code
Exploiting Shared Representations for Personalized Federated Learning
Based on this intuition, we propose a novel federated learning framework and algorithm for learning a shared data representation across clients and unique local heads for each client.
Imaging Mechanism for Hyperspectral Scanning Probe Microscopy via Gaussian Process Modelling
We investigate the ability to reconstruct and derive spatial structure from sparsely sampled 3D piezoresponse force microcopy data, captured using the band-excitation (BE) technique, via Gaussian Process (GP) methods.
Computational Physics Materials Science
Super-resolution and signal separation in contact Kelvin Probe Force Microscopy of Electrochemically Active Ferroelectric Materials
Imaging mechanisms in contact Kelvin Probe Force Microscopy (cKPFM) are explored via information theory-based methods.
Applied Physics Materials Science
Task-Robust Model-Agnostic Meta-Learning
Meta-learning methods have shown an impressive ability to train models that rapidly learn new tasks.
How Does the Task Landscape Affect MAML Performance?
Model-Agnostic Meta-Learning (MAML) has become increasingly popular for training models that can quickly adapt to new tasks via one or few stochastic gradient descent steps.
MAML and ANIL Provably Learn Representations
Recent empirical evidence has driven conventional wisdom to believe that gradient-based meta-learning (GBML) methods perform well at few-shot learning because they learn an expressive data representation that is shared across tasks.
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning
We show that the reason behind generalizability of the FedAvg's output is its power in learning the common data representation among the clients' tasks, by leveraging the diversity among client data distributions via local updates.
InfoNCE Loss Provably Learns Cluster-Preserving Representations
The goal of contrasting learning is to learn a representation that preserves underlying clusters by keeping samples with similar content, e. g. the ``dogness'' of a dog, close to each other in the space generated by the representation.
Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks
An increasingly popular machine learning paradigm is to pretrain a neural network (NN) on many tasks offline, then adapt it to downstream tasks, often by re-training only the last linear layer of the network.
Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning
In many applications of federated learning (FL), clients desire models that are personalized using their local data, yet are also robust in the sense that they retain general global knowledge.
In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness
We show that an attention unit learns a window that it uses to implement a nearest-neighbors predictor adapted to the landscape of the pretraining tasks.
