Papers with Code 아카이브

Multi-Task Learning

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하위 태스크: 2
평가표: 8
보존된 평가 기록: 16

Multi-task learning aims to learn multiple different tasks simultaneously while maximizing performance on one or all of the tasks.

<span style="color:grey; opacity: 0.6">( Image credit: Cross-stitch Networks for Multi-task Learning )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/desaisrkr/https-github.com-UCSD-AI4H-COVID-CT

COVID-CT-Dataset: A CT Scan Dataset about COVID-19

https://github.com/devhemza/BERTweet_sentiment_analysis

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/dhu-mmct/dhu-mmct

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/dig-team/hanna-benchmark-asg

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/dingwoai/FairMOT-BDD100K

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/discipleofhamilton/RetinaFace

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/dreamh1gh/awesome-srl

What do you MEME? Generating Explanations for Visual Semantic Role Labelling in Memes

https://github.com/dscv-lab/KMF2JMTtracking

Robust Visual Tracking using Multi-Frame Multi-Feature Joint Modeling

https://github.com/duanchi1230/NLP_Project_AI2_Reasoning_Challenge

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/duguodong7/model-evolution

Localizing Task Information for Improved Model Merging and Compression

https://github.com/duxiaoman-di/xuanyuan

Measuring Massive Multitask Language Understanding

https://github.com/ebagdasa/backdoors101

Multi-Task Learning as Multi-Objective Optimization

https://github.com/ecnu-icalk/educhat

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ecolab-postech/owq

LLaMA: Open and Efficient Foundation Language Models

https://github.com/elliottzheng/face-detection

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/emiresenov/jaxpi

Gradient Alignment in Physics-informed Neural Networks: A Second-Order Optimization Perspective

https://github.com/emorynlp/friendsqa

Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering

https://github.com/emrgnt-cmplxty/zero-shot-replication

GPT-4 Technical Report

https://github.com/epfml/collaborative-attention

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/eternityyw/tram-benchmark

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/ethanjperez/true_few_shot

Language Models are Few-Shot Learners

https://github.com/ethz-privsec/superhuman-ai-consistency

GPT-4 Technical Report

https://github.com/ethz-spylab/superhuman-ai-consistency

GPT-4 Technical Report

https://github.com/expertailab/spaceqa

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/facebookresearch/InferSent

Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning

https://github.com/facebookresearch/anli

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/facebookresearch/chai

LLaMA: Open and Efficient Foundation Language Models

https://github.com/facebookresearch/data2vec_vision

UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data

https://github.com/facebookresearch/fairseq2

https://github.com/facico/chinese-vicuna

LLaMA: Open and Efficient Foundation Language Models

https://github.com/farama-foundation/metaworld

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

https://github.com/fatyin88/MCQA_project

MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension

https://github.com/fcakyon/sahi-benchmark

Varifocal-Net: A Chromosome Classification Approach using Deep Convolutional Networks

https://github.com/felisat/clustered-federated-learning

Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints

https://github.com/few-shot-NER-benchmark/BaselineCode

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/fewshotcdcs/cdcs

CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.