Machine Learning

Graph Learning Learning representations for graph-structured data. Probabilistic Graphical Models Probabilistic graphical modeling is a branch of machine learning that studies how to use probability distributions to model the world.

Topic	Replies	Views	Activity
About Machine Learning Machine Learning	0	727	December 25, 2019
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Incorporating Hierarchy into Text Encoder: a Contrastive Learning Approach Graph Learning	0	417	December 25, 2022
LAGr: Label Aligned Graphs for Better Systematic Generalization in Semantic Probabilistic Graphical Models	0	443	October 7, 2022
Rethinking the Inception Architecture for Computer Vision Machine Learning	0	535	April 14, 2022
We Need to Talk About train-dev-test Splits Machine Learning	0	603	December 2, 2021
简单有效的位置编码 Machine Learning	0	838	November 27, 2021
SELFEXPLAIN: A Self-Explaining Architecture for Neural Text Classifiers Machine Learning	0	548	November 20, 2021
MTAdam: Automatic Balancing of Multiple Training Loss Terms Machine Learning	0	514	November 19, 2021
What to Pre-Train on? Efficient Intermediate Task Selection Machine Learning	0	486	November 18, 2021
Effects of Parameter Norm Growth During Transformer Training: Inductive Bias from Gradient Descent Machine Learning	0	634	November 17, 2021
Conditional Poisson Stochastic Beams Machine Learning	0	545	November 17, 2021
Do Transformers Really Perform Bad for Graph Representation? Graph Learning	0	691	October 9, 2021
好奇，有没有那种工具，NLP任务数据读取、转化的python包 Machine Learning	2	824	August 22, 2021
pytorch版Loss实现苏剑林版【将“softmax+交叉熵”推广到多标签分类问题】 Graph Learning	0	1657	August 22, 2021
pytorch版Loss实现苏剑林版【通过互信息思想来缓解类别不平衡问题】 Graph Learning	0	1137	August 22, 2021
Convolutions and Self-Attention: Re-interpreting Relative Positions in... Machine Learning	0	518	August 16, 2021
Reservoir Transformers Machine Learning	0	526	August 15, 2021
Structural Knowledge Distillation: Tractably Distilling Information for... Machine Learning	0	911	August 10, 2021
Parameter-efficient Multi-task Fine-tuning for Transformers via Shared... Machine Learning	0	669	August 11, 2021
Self-Training with Weak Supervision Machine Learning	0	539	July 30, 2021
A Generative Model for Joint Natural Language Understanding and Generation Machine Learning	0	541	June 21, 2021
Self-Attention Guided Copy Mechanism for Abstractive Summarization Machine Learning	0	920	June 16, 2021
NBDT: Neural-Backed Decision Trees Machine Learning	0	1156	June 10, 2021
Active Imitation Learning with Noisy Guidance Machine Learning	0	605	May 27, 2021
Integrating Semantic and Structural Information with Graph Convolutional... Graph Learning	0	630	May 7, 2021
Intel的二代神经棒有人用过没 Machine Learning	0	638	April 12, 2021
TensorFlow 2.0 VS PyTorch？ Machine Learning	13	4788	April 3, 2021
Learning Sparse Neural Networks through L0 Regularization Machine Learning	0	881	February 13, 2021
Dice Loss for Data-imbalanced NLP Tasks Machine Learning	0	798	February 18, 2021