Large Margin Multi-Task Metric Learning 来自 掌桥科研 喜欢 0 阅读量: 220 作者:S Parameswaran,KQ Weinberger 摘要: Multi-task learning (MTL) improves the prediction performance on multiple, different but related, learning problems through shared parameters or representations. One of the most prominent...
Large Margin Multi-Task Metric Learning Multi-task learning (MTL) improves the prediction performance on multiple, different but related, learning problems through shared parameters or representations. One of the most prominent multi-task learning algorithms is an extension to... S Parameswaran,KQ Wei...
Ideally this loss function is tai-lored to the task and accurately represents the aims for the model's output. Yet learning in these models typically does not optimize performance on the true task loss, due to com-putational complexity, instead resorting to surrogate simple decomposable loss ...
we focus on how those models perform when they are fine-tuned—that is, the weights are updated—on some task-specific dataset. Note that this task-specific fine-tuning makes the models less dependent on the prompt structure than in-context learning21,22. ...
The clinical knowledge and professional medicine subset of MMLU are two specialized components of a broad multitask benchmarking dataset evaluating a model’s understanding of clinical and medical concepts and scenarios. We used accuracy as the primary evaluation metric and byte-length normalized ...
Multi-task Learning in Computer Vision and AVs Multi-task learning (MTL) is an area that has seen substantial research focus, often described as a major step towards human-like reasoning in artificial intelligence (AI). As outlined in Michael Crawshaw’s comprehensive survey on the subject, ...
TaskWeaver: A code-first agent framework which can convert natural language user requests into executable code, with additional support for rich data structures, dynamic plugin selection, and domain-adapted planning process. [Sep 2023] JARVIS: an interface for LLMs to connect numerous AI models for...
But this level of margin is NOT SIGNIFICANT Also remember that gpt-3.5-turbo is 10 times cheaper than text-davinci-003 Also be careful that GPT-4/ 3.5's performance on GSM8K is not true few-shot -- in GPT-4 report they said that they mixed a portion of GSM8K training set to ...
N. A training algorithm for optimal margin classifiers. in Proceedings of the Fifth Annual Workshop on Computational Learning theory 144–152. https://doi.org/10.1145/130385.130401(ACM, 1992). Luo, X. et al. Machine learning-based genetic diagnosis models for hereditary hearing loss by the GJB...
A larger DeBERTa model with 1.5 billion parameters surpasses human performance on the SuperGLUE benchmark, and an ensemble DeBERTa model leads the SuperGLUE leaderboard with a significant margin over the human baseline.[47] 2020 June 30 GShard 600,000,000,000[38] 1,000,000,000,000 tokens[38...