Papers with Code 아카이브

Multi-Task Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

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/shenweichen/DeepCTR-Torch

An Overview of Multi-Task Learning in Deep Neural Networks

https://github.com/shmsw25/factscore

GPT-4 Technical Report

https://github.com/shreyashankar/gpt3-sandbox

Language Models are Few-Shot Learners

https://github.com/shujams/CT-AI-Deep-Learning-Project

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

https://github.com/simon-benigeri/narrative-generation

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/sithu31296/EasyFace

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/sjn3303123/DL_Reproducibility

Self-Supervised Generalisation with Meta Auxiliary Learning

https://github.com/smarton-empower/smarton-ai

Language Models are Few-Shot Learners

https://github.com/smile-data/smile

Language Models are Few-Shot Learners

https://github.com/so-hyeun/BRET

Internal-transfer Weighting of Multi-task Learning for Lung Cancer Detection

https://github.com/soham97/MTL_Weakly_labelled_audio_data

Multi-Task Learning for Interpretable Weakly Labelled Sound Event Detection

https://github.com/sooftware/OpenSpeech

Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning

https://github.com/soyoung97/fairseq-gec-korean

Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data

https://github.com/squeezeailab/squeezellm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/sseung0703/KD_methods_with_TF

Graph-based Knowledge Distillation by Multi-head Attention Network

https://github.com/stanfordbdhg/llama.cpp

LLaMA: Open and Efficient Foundation Language Models

https://github.com/stepjam/PyRep

RLBench: The Robot Learning Benchmark & Learning Environment

https://github.com/sukibean163/RetinaFace_Paddle

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/sunxm2357/AdaShare

AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning

https://github.com/superli3/CYRMPR

Evaluating Large Language Models Trained on Code

https://github.com/superli3/codenavi

Evaluating Large Language Models Trained on Code

https://github.com/superlj666/Learning-Vector-valued-Functions-with-Local-Rademacher-Complexity

Semi-supervised Vector-valued Learning: Improved Bounds and Algorithms

https://github.com/takiyu/hyperface

HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition

https://github.com/talhaanwarch/Corona_Virus/tree/master/CT_scan

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

https://github.com/tantheta01/AGMIR

On the Automatic Generation of Medical Imaging Reports

https://github.com/tatsu-lab/stanford_alpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/teelinsan/camoscio

LLaMA: Open and Efficient Foundation Language Models

https://github.com/tensorflow/models

Semi-Supervised Sequence Modeling with Cross-View Training

https://github.com/tensorflow/models/tree/master/official/nlp/albert

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

https://github.com/tensorflow/models/tree/master/research/syntaxnet

DRAGNN: A Transition-based Framework for Dynamically Connected Neural Networks

https://github.com/theoremone/llm-benchmarker-suite

Scaling Instruction-Finetuned Language Models

https://github.com/thudm/chatglm

GLM-130B: An Open Bilingual Pre-trained Model

https://github.com/thudm/chatglm2-6b

GLM-130B: An Open Bilingual Pre-trained Model

https://github.com/thudm/chatglm3

GLM-130B: An Open Bilingual Pre-trained Model

https://github.com/thuiar/MMSA

Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis

https://github.com/thunlp/clueanchor

Atlas: Few-shot Learning with Retrieval Augmented Language Models

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