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Actively Testing Y our Model While It Learns: Realizing Label-Efficient Learning in Practice Dayou Yu
In active learning (AL), we focus on reducing the data annotation cost from the model training perspective. However, "testing", which often refers to the model
On Single Source Robustness in Deep Fusion Models
One natural way of improving a model's performance is to make use of multiple input sources relevant to a given task so that enough information can be extracted to build strong features.
Synaptic Strength For Convolutional Neural Network
CHEN LIN, Zhao Zhong, Wu Wei, Junjie Yan
ModernCNNscanreachhundreds of millions of parameters and billions of operations, which makes it difficult to deploy. To alleviate aforementioned problem, various methods have been proposed to increase the efficiency of CNNs.
Learning Multiple Markov Chains via Adaptive Allocation
Mohammad Sadegh Talebi, Odalric-Ambrym Maillard
Neural Information Processing Systems http://nips.cc/
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
Conditional sampling is an essential primitive in the machine learning toolkit.
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
Conditional sampling is an essential primitive in the machine learning toolkit.
Random Path Selection for Continual Learning
Jathushan Rajasegaran, Munawar Hayat, Salman H. Khan, Fahad Shahbaz Khan, Ling Shao
Neural Information Processing Systems http://nips.cc/