200 citations · 308 across the 11 of their papers we have counts for
12 papers
StarCoder: may the source be with you!
Raymond Li, Loubna Ben Allal, Yangtian Zi +64
The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase…
Exploiting Invariance in Training Deep Neural Networks
Chengxi Ye, Xiong Zhou, Tristan McKinney +3
Inspired by two basic mechanisms in animal visual systems, we introduce a feature transform technique that imposes invariance properties in the training of deep neural networks. Th…
Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications
Biagio Brattoli, Joseph Tighe, Fedor Zhdanov +2
Trained on large datasets, deep learning (DL) can accurately classify videos into hundreds of diverse classes. However, video data is expensive to annotate. Zero-shot learning (ZSL…
Diverse mini-batch Active Learning
Fedor Zhdanov
We study the problem of reducing the amount of labeled training data required to train supervised classification models. We approach it by leveraging Active Learning, through seque…
An Identity for Kernel Ridge Regression
Fedor Zhdanov, Yuri Kalnishkan
This paper derives an identity connecting the square loss of ridge regression in on-line mode with the loss of the retrospectively best regressor. Some corollaries about the proper…
Prediction with Expert Advice under Discounted Loss
Alexey Chernov, Fedor Zhdanov
We study prediction with expert advice in the setting where the losses are accumulated with some discounting---the impact of old losses may gradually vanish. We generalize the Aggr…