Deep Learning
The Normalization Method for Alleviating Pathological Sharpness in Wide Neural Networks
Ryo Karakida, Shotaro Akaho, Shun-ichi Amari
Normalization methods play an important role in enhancing the performance of deep learning while their theoretical understandings have been limited.
Generative Well-intentioned Networks
An essential aspect of any machine learning system is understanding what the model does not know.
SOL: Sampling-based Optimal Linear bounding of arbitrary scalar functions
Finding tight linear bounds for activation functions in neural networks is an essential part of several state of the art neural network robustness certification tools.
AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification
Ronghui You, Zihan Zhang, Ziye Wang, Suyang Dai, Hiroshi Mamitsuka, Shanfeng Zhu
Neural Information Processing Systems http://nips.cc/
LOVM: Language-Only Vision Model Selection
Pre-trained multi-modal vision-language models (VLMs) are becoming increasingly popular due to their exceptional performance on downstream vision applications, particularly in the few-and zero-shot settings.
Large Memory Layers with Product Keys
Guillaume Lample, Alexandre Sablayrolles, Marc'Aurelio Ranzato, Ludovic Denoyer, Herve Jegou
This paper introduces a structured memory which can be easily integrated into a neural network. The memory is very large by design and significantly increases the capacity of the architecture, by up to a billion parameters with a negligible computational overhead.