Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts 模型(a) 最为常见,两个任务直接共享模型的 bottom 部分,只在最后处理时做区分,图 (a) 中使用了 Tower A 和 Tower B,然后分别接损失函数。 模型(b) 是常见的多任务学习模型。将 input 分别输入给三个 Expert,但三个 ...
深度模型(十三):Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts,程序员大本营,技术文章内容聚合第一站。
通过调制和门控网络,我们的模型自动调整建模之间的参数化共享信息和建模任务特定信息。 第二,我们对合成数据进行控制实验。 我们报告如何任务相关性影响多任务学习中的训练动态和MMoE 如何提高模型的表现力和可训练性。 最后,我们对真实的基准数据和一个拥有数百个产品的大规模生产推荐系统数以百万计的用户和项目。 ...
In this paper, a new task modeling approach calledTask Interaction-Precedence Graph(TIPG) is introduced in which task precedence, dependency, and interaction are possible, subject to passing validity tests. The modeling starts with only tasks, and TIGs are automatically recognized by the developed ...
论文地址:https://github.com/imsheridan/DeepRec/blob/master/MTL/%5BMMoE%5D%5BKDD%2018%5D%5BGoogle%5D%20Modeling%20Task%20Relationships%20in%20Multi-task%20Learning%20with%20Multi-gate%20Mixture-of-Experts.pdf(这篇论文作者几乎全是华人啊) ...
In this compound-target association dataset, we could ask the question, is it possible to predict whether a compound is active, whereas regression modeling is focused on predicting numeric values. For example, we may be asked to predict the activity value in nanomolars for a particular compound...
It is therefore important to study the modeling tradeoffs between task-specific objectives and inter-task relationships. In this work, we propose a novel multi-task learning approach, Multi-gate Mixture-of-Experts (MMoE), which explicitly learns to model task relationships from data. We adapt ...
():fromtransformers.models.llama.configuration_llamaimportLlamaConfigfromtransformers.models.llama.modeling_llamaimportLlamaForCausalLMcfg=LlamaConfig(vocab_size=32000,hidden_size=32,intermediate_size=256,num_hidden_layers=4,num_attention_heads=4,torch_dtype=torch.float16)model=LlamaForCausalLM(cfg)###...
基于此,文中提出了任务驱动的语言建模(Task-driven Language Modeling,简称TLM),以求改进预训练->微调的训练范式,首先基于通用语料库用任务文本构造检索利用BM25检索文本构造小型语料库,然后将小型语料库预训练目标和任务目标同时优化,最后微调,发现计算量减少两个数量级的同时,效果不弱于甚至强于传统预训练->微调范式...
the prediction quality of commonly used multi task models is often sensitive to the relationships between tasks. It is therefore important to study the modeling tradeoffs between task-specific objectives and inter-task relationships. In this work, we propose a novel multi-task learning approach, Mult...