14 citations · 31 across the 10 of their papers we have counts for
11 papers
Continual Zero-Shot Learning through Semantically Guided Generative Random Walks
Wenxuan Zhang, Paul Janson, Kai Yi +2
Learning novel concepts, remembering previous knowledge, and adapting it to future tasks occur simultaneously throughout a human's lifetime. To model such comprehensive abilities,…
Explicit Personalization and Local Training: Double Communication Acceleration in Federated Learning
Kai Yi, Laurent Condat, Peter Richtárik
Federated Learning is an evolving machine learning paradigm, in which multiple clients perform computations based on their individual private data, interspersed by communication wi…
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning
Grigory Malinovsky, Kai Yi, Peter Richtárik
We study distributed optimization methods based on the {\em local training (LT)} paradigm: achieving communication efficiency by performing richer local gradient-based training on…
EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization
Laurent Condat, Kai Yi, Peter Richtárik
In distributed or federated optimization and learning, communication between the different computing units is often the bottleneck and gradient compression is widely used to reduce…
Exploring Hierarchical Graph Representation for Large-Scale Zero-Shot Image Classification
Kai Yi, Xiaoqian Shen, Yunhao Gou +1
The main question we address in this paper is how to scale up visual recognition of unseen classes, also known as zero-shot learning, to tens of thousands of categories as in the I…
Domain-Aware Continual Zero-Shot Learning
Kai Yi, Paul Janson, Wenxuan Zhang +1
Modern visual systems have a wide range of potential applications in vision tasks for natural science research, such as aiding in species discovery, monitoring animals in the wild,…