Multi-Task Learning
Multi-task learning aims to learn multiple different tasks simultaneously while maximizing performance on one or all of the tasks.
State-of-the-art leaderboards
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Greatest papers with code
DRAGNN: A Transition-based Framework for Dynamically Connected Neural Networks
In this work, we present a compact, modular framework for constructing novel recurrent neural architectures.
Semi-Supervised Sequence Modeling with Cross-View Training
EMNLP 2018
• tensorflow/models
• 
We therefore propose Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data.
SOTA for CCG Supertagging on CCGBank
CCG SUPERTAGGING DEPENDENCY PARSING MACHINE TRANSLATION MULTI-TASK LEARNING NAMED ENTITY RECOGNITION (NER) UNSUPERVISED REPRESENTATION LEARNING
One Model To Learn Them All
We present a single model that yields good results on a number of problems spanning multiple domains.
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning
ICLR 2018
• facebookresearch/InferSent
• 
In this work, we present a simple, effective multi-task learning framework for sentence representations that combines the inductive biases of diverse training objectives in a single model.
MULTI-TASK LEARNING NATURAL LANGUAGE INFERENCE PARAPHRASE IDENTIFICATION SEMANTIC TEXTUAL SIMILARITY
A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks
• huggingface/hmtl
•
The model is trained in a hierarchical fashion to introduce an inductive bias by supervising a set of low level tasks at the bottom layers of the model and more complex tasks at the top layers of the model.
MULTI-TASK LEARNING NAMED ENTITY RECOGNITION (NER) RELATION EXTRACTION
Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding
• namisan/mt-dnn
•
This paper explores the use of knowledge distillation to improve a Multi-Task Deep Neural Network (MT-DNN) (Liu et al., 2019) for learning text representations across multiple natural language understanding tasks.
An Overview of Multi-Task Learning in Deep Neural Networks
Multi-task learning (MTL) has led to successes in many applications of machine learning, from natural language processing and speech recognition to computer vision and drug discovery.
Agnostic Lane Detection
• cardwing/Codes-for-Lane-Detection
•
Lane detection is an important yet challenging task in autonomous driving, which is affected by many factors, e. g., light conditions, occlusions caused by other vehicles, irrelevant markings on the road and the inherent long and thin property of lanes.
#2 best model for
Lane Detection
on CULane
AUTONOMOUS DRIVING INSTANCE SEGMENTATION LANE DETECTION MULTI-TASK LEARNING SEMANTIC SEGMENTATION
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
CVPR 2018 • yaringal/multi-task-learning-example
Numerous deep learning applications benefit from multi-task learning with multiple regression and classification objectives.
INSTANCE SEGMENTATION MULTI-TASK LEARNING SEMANTIC SEGMENTATION