Gal Chechik

Adaptive Confidence Smoothing for Generalized Zero-Shot Learning

2019

Generalized zero-shot learning (GZSL) is the problem of learning a classifier where some classes have samples and others are learned from side information, like semantic attributes or text description, in a zero-shot learning fashion (ZSL). …

Probabilistic AND-OR Attribute Grouping for Zero-Shot Learning

2018 · Uncertainty in Artificial Intelligence

In zero-shot learning (ZSL), a classifier is trained to recognize visual classes without any image samples. Instead, it is given semantic information about the class, like a textual description or a set of attributes. Learning …

Long-tail learning with class descriptors

2020 · arXiv (Cornell University)

Real-world data is predominantly unbalanced and long-tailed, but deep models struggle to recognize rare classes in the presence of frequent classes. Often, classes can be accompanied by side information like textual descriptions, but it is …

Personalized Federated Learning using Hypernetworks

2021 · arXiv (Cornell University)

Personalized federated learning is tasked with training machine learning models for multiple clients, each with its own data distribution. The goal is to train personalized models in a collaborative way while accounting for data disparities …

Multi-Task Learning as a Bargaining Game

2022 · arXiv (Cornell University)

In Multi-task learning (MTL), a joint model is trained to simultaneously make predictions for several tasks. Joint training reduces computation costs and improves data efficiency; however, since the gradients of these different tasks may conflict, …