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Found 5 papers, 1 papers with code
KD-FixMatch: Knowledge Distillation Siamese Neural Networks
Firstly, an outer SNN is trained using labeled and unlabeled data.
CMA-CLIP: Cross-Modality Attention CLIP for Image-Text Classification
In this paper, we propose the Cross-Modality Attention Contrastive Language-Image Pre-training (CMA-CLIP), a new framework which unifies two types of cross-modality attentions, sequence-wise attention and modality-wise attention, to effectively fuse information from image and text pairs.
Learning Classifiers on Positive and Unlabeled Data with Policy Gradient
Existing algorithms aiming to learn a binary classifier from positive (P) and unlabeled (U) data generally require estimating the class prior or label noises ahead of building a classification model.
Newton Methods for Convolutional Neural Networks
Deep learning involves a difficult non-convex optimization problem, which is often solved by stochastic gradient (SG) methods.
Distributed Newton Methods for Deep Neural Networks
First, to reduce the communication cost, we propose a diagonalization method such that an approximate Newton direction can be obtained without communication between machines.
