在第1个学习场景中,模型总是被告知需要执 行哪些任务,这也是最简单的连续学习场景,将其称 为任务增量学习(taskGincrementallearning,TaskG IL).近年来,提出的大部分连续学习方法在此场景都是适用的,且都具有较好的实验效果,例如正则化 方法和动态结构方法等. 在第2个学习场景中,通常将其称之为域增量 学习 (dom...
最近深度学习两巨头 Bengio 和 LeCun 在 ICLR 2020 上点名Self-Supervised Learning(SSL,自监督学习)是 AI 的未来,而其的代表的 Framework 便是Contrastive Learning(CL,对比学习)。 另一巨头 Hinton 和 Kaiming 两尊大神也在这问题上隔空过招,MoCo、SimCLR、MoCo V2 打得火热,这和BERT之后,各大公司出XL-Net...
In this paper, we introduce an intelligent system (CL-XAI) for cognitive learning supported by XAI, focusing on two key research objectives: (i) exploring how human learners comprehend the internal mechanisms of AI models using XAI tools; and (ii) evaluating the effectiveness of such tools ...
In order to simulate this effect we extended the Selective Attention for Identification model (SAIM [5, 7]) with a mechanism for contextual learning (CL-SAIM). The learning mechanism is based on a Hop field pattern memory with asymmetric weights. This memory module integrates two functions: On...
在DIN、DIEN等面向用户行为序列的模型提出后,在CTR预测上取得了非常显著的提升,后续许多工作都瞄准了序列行为的学习。相比以点击等目标直接训练模型,可以结合对比学习,加强对于序列知识的提取能力。CL4SRec通过数据增强、网络结构和损失设计实现了更好的基于序列信息的知识提取和任务应用。
1. CL Machine-Learning 1.1. Author(s): 1.1.1. Original 1.1.2. Current Branch Maintainer(s)/Authors(s): 1.1.3. Contributors: 1.2. Installation 1.3. Requirements 1.4. Installation Notes 1.4.1. Obtaining code 1.4.2. Installing 1.5. Documentation 1.5.1. User and API Documentation 1.6....
the personalized CLTV prediction problem from the two sub-tasks of churn prediction and payment prediction in a sequential gated multi-task learning fashion... S Zhao,R Wu,J Tao,... - 《Acm Transactions on Information Systems》 被引量: 0发表: 2023年 Language and socio-cultural model of le...
In this paper, we propose a federated contrastive learning method named FedCL for privacy-preserving recommendation, which can exploit high-quality negative samples for effective model training with privacy well protected. We first infer user embeddings from local user data through the local model on...
InfluenceCL Code for CVPR 2023 paperRegularizing Second-Order Influences for Continual Learning. In continual learning, earlier coreset selection exerts a profound influence on subsequent steps through the data flow. Our proposed scheme regularizes the future influence of each selection. In its absence...
CL Machine-Learning CL Machine-Learning is high performance and large scale statistical machine learning package written in Common Lisp developed atMSI. This repository contains is a authorized fork of the original CLML with the following goals in mind: ...