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	378	December 25, 2019
Rethinking the Inception Architecture for Computer Vision Machine Learning	0	71	April 14, 2022
We Need to Talk About train-dev-test Splits Machine Learning	0	105	December 2, 2021
简单有效的位置编码 Machine Learning	0	169	November 27, 2021
SELFEXPLAIN: A Self-Explaining Architecture for Neural Text Classifiers Machine Learning	0	120	November 20, 2021
MTAdam: Automatic Balancing of Multiple Training Loss Terms Machine Learning	0	125	November 19, 2021
What to Pre-Train on? Efficient Intermediate Task Selection Machine Learning	0	103	November 18, 2021
Effects of Parameter Norm Growth During Transformer Training: Inductive Bias from Gradient Descent Machine Learning	0	126	November 17, 2021
Conditional Poisson Stochastic Beams Machine Learning	0	100	November 17, 2021
Do Transformers Really Perform Bad for Graph Representation? Graph Learning	0	170	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	216	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	419	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	225	August 22, 2021
Convolutions and Self-Attention: Re-interpreting Relative Positions in... Machine Learning	0	155	August 16, 2021
Reservoir Transformers Machine Learning	0	150	August 15, 2021
Structural Knowledge Distillation: Tractably Distilling Information for... Machine Learning	0	369	August 10, 2021
Parameter-efficient Multi-task Fine-tuning for Transformers via Shared... Machine Learning	0	196	August 11, 2021
Self-Training with Weak Supervision Machine Learning	0	136	July 30, 2021
A Generative Model for Joint Natural Language Understanding and Generation Machine Learning	0	152	June 21, 2021
Self-Attention Guided Copy Mechanism for Abstractive Summarization Machine Learning	0	316	June 16, 2021
NBDT: Neural-Backed Decision Trees Machine Learning	0	305	June 10, 2021
Active Imitation Learning with Noisy Guidance Machine Learning	0	202	May 27, 2021
Integrating Semantic and Structural Information with Graph Convolutional... Graph Learning	0	172	May 7, 2021
Intel的二代神经棒有人用过没 Machine Learning	0	251	April 12, 2021
TensorFlow 2.0 VS PyTorch？ Machine Learning	13	3150	April 3, 2021
Learning Sparse Neural Networks through L0 Regularization Machine Learning	0	276	February 13, 2021
Dice Loss for Data-imbalanced NLP Tasks Machine Learning	0	305	February 18, 2021
TensorFlow 2.x 有哪些坑？ Machine Learning	6	1213	January 20, 2021
关于PyTorch的Dataset、DataLoader和Sampler的设计看法 Machine Learning	6	2497	January 11, 2021
Learning to Contextually Aggregate Multi-Source Supervision for Sequence... Machine Learning	0	303	September 30, 2020