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How Often Is Enough? Training Frequency Required to Maintain Microsurgical Anastomosis Skills: A Longitudinal Analysis of Self-Practice Records. [PDF]
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Training language models to follow instructions with human feedback
Neural Information Processing Systems, 2022Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user.
Long Ouyang +19 more
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Sigmoid Loss for Language Image Pre-Training
IEEE International Conference on Computer Vision, 2023We propose a simple pairwise sigmoid loss for imagetext pre-training. Unlike standard contrastive learning with softmax normalization, the sigmoid loss operates solely on image-text pairs and does not require a global view of the pairwise similarities ...
Xiaohua Zhai +3 more
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Training Compute-Optimal Large Language Models
Advances in Neural Information Processing Systems 35, 2022We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly undertrained, a consequence of the recent focus on scaling ...
Jordan Hoffmann +21 more
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2018 IEEE 14th International Conference on Control and Automation (ICCA), 2018
Deep learning has proven to be an effective method for classifying images. Over the past years various network topologies have been trained using millions of images only to illustrate that capabilities of deep learning is to be expected only to grow in time.
Ahmet Bugra Koku +3 more
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Deep learning has proven to be an effective method for classifying images. Over the past years various network topologies have been trained using millions of images only to illustrate that capabilities of deep learning is to be expected only to grow in time.
Ahmet Bugra Koku +3 more
openaire +1 more source
Domain-Adversarial Training of Neural Networks
Journal of machine learning research, 2015We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions.
Yaroslav Ganin +7 more
semanticscholar +1 more source