With the new data engine, Blue River Technology’s ML teams can now spend more of their time focusing on training, monitoring, and maintaining their computer vision models. Their data scientists can pull updated
JumpStart supports task-specific models across fifteen of the most popular problem types. Of the supported problem types, Vision and NLP-related types total thirteen. There are eight problem types that support incremental training and fine-tuning. For more information about incremental training and hyp...
Chinese AI company DeepSeek releases new Janus-Pro 7B model: multimodal AI models that are meant to outperform OpenAI's dominant DALL-E 3.
While foundation models primarily focus on segmentation and classification, task-specific models are integrated into nearly all medical image analyses. However, with further advancements, foundation models could be applied to other clinical scenarios. In conclusion, all indications suggest that task-...
Distilling Task-Specific Large Language Models Distillation [1] involves generating a large amount of data from a large/expensive LLM (called ateacher model), and training a smaller, more efficient, and performant LLM (called astudent model) on it to achieve quality comparable to the larger LLM...
We can use NLI models to evaluate the factual consistency of summaries too. The key insight is to treat the source document as the premise and the generated summary as the hypothesis. If the summary contradicts the source, then the summary is factually inconsistent aka a hallucination. Document...
5.2.3 Combining DTRN with Different Multi-Task Learning Models 在这部分研究中,我们关注于任务特定的底层表示,这与现有的多任务学习(MTL)模型是正交的。我们通过将 DTRN 与不同的 MTL 模型结合在推荐系统(RS)中,展示了其有效性。表 7 的结果表明,DTRN 输出的任务特定底层表示为每个任务提供了更强的能力,...
By inputting such task-specific output to the minimum stable balanced reduction method, the simplification application may generate minimum stable balanced reduced models which are better suited for the desired tasks.To control robot 100 movement, an observer, which may be designed using, e.g., ...
Finetuning foundation models for specific tasks is an emerging paradigm in modern machine learning. The efficacy of task-specific finetuning largely depends on the selection of appropriate training data. We present TSDS (Task-Specific Data Selection), a framework to select data for task-specific mod...
Model merging integrates the weights of multiple task-specific models into a single multi-task model. Despite recent interest in the problem, a significant performance gap between the combined and single-task models remains. In this paper, we investigate the key characteristics of task matrices --...