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Multi-Task Learning

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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/achernodub/targer

Neural End-to-End Learning for Computational Argumentation Mining

https://github.com/acnazarejr/fdet

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/adtalos/AITM-torch

Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising

https://github.com/aethercortex/llama-x

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ai21labs/lm-evaluation

Language Models are Few-Shot Learners

https://github.com/aioz-ai/multigraphfl

LEAF: A Benchmark for Federated Settings

https://github.com/aistairc/kirt_bert_on_abci

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/aj9011/Car-Speed-Prediction

Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks

https://github.com/akanyaani/miniLLAMA

LLaMA: Open and Efficient Foundation Language Models

https://github.com/alexeyab/yolor

You Only Learn One Representation: Unified Network for Multiple Tasks

https://github.com/alibaba-damo-academy/FunASR

A New Benchmark of Aphasia Speech Recognition and Detection Based on E-Branchformer and Multi-task Learning

https://github.com/alibaba-damo-academy/funcodec

LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT

https://github.com/alililia/ms_extend/tree/main/gpu_retinaface

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/allenai/beacon

BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing

https://github.com/allenai/dolma

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

https://github.com/allenai/macaw

Language Models are Few-Shot Learners

https://github.com/alon-albalak/online-data-mixing

GPT-NeoX-20B: An Open-Source Autoregressive Language Model

https://github.com/aluscher/torchbeastpopart

Multi-task Deep Reinforcement Learning with PopArt

https://github.com/amazon-science/tanl

Structured Prediction as Translation between Augmented Natural Languages

https://github.com/ambujojha/SemiSupervisedLearning

Strong Baselines for Neural Semi-supervised Learning under Domain Shift

https://github.com/andris955/diplomaterv

Learning to Multi-Task by Active Sampling

https://github.com/animeshprasad/citation_func_n_prov

Neural Multi-Task Learning for Citation Function and Provenance

https://github.com/ankitsinghsuraj/mot20

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

https://github.com/anlausch/LIBERT

Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity

https://github.com/anouck96/parsingfrisian

Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP

https://github.com/anthonysicilia/leather-aacl2022

Beyond task success: A closer look at jointly learning to see, ask, and GuessWhat

https://github.com/anton-jeran/AV-RIR

AV-RIR: Audio-Visual Room Impulse Response Estimation

https://github.com/anton-jeran/Speech2RIR

AV-RIR: Audio-Visual Room Impulse Response Estimation

https://github.com/anusfoil/llaqo

Acoustic Prompt Tuning: Empowering Large Language Models with Audition Capabilities

https://github.com/aozhongzhang/magr

LLaMA: Open and Efficient Foundation Language Models

https://github.com/appcoreopc/berty

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

https://github.com/aryopg/mmlu-redux

Measuring Massive Multitask Language Understanding

https://github.com/asahi417/lmppl

Language Models are Few-Shot Learners

https://github.com/asahi417/relbert

Language Models are Few-Shot Learners

https://github.com/ashokdahal/FrameFieldLearning_Anaconda_Windows

Polygonal Building Segmentation by Frame Field Learning

https://github.com/autumn9999/go4align

Multi-Task Learning as a Bargaining Game

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