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/weirdlabuw/strap

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning

https://github.com/wenet-e2e/west

The Llama 3 Herd of Models

https://github.com/wenet-e2e/wetts

Unified Mandarin TTS Front-end Based on Distilled BERT Model

https://github.com/wenlai-lavine/jola

MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

https://github.com/wgchang/PCGrad-pytorch-example

Gradient Surgery for Multi-Task Learning

https://github.com/willemsenbram/mention-detection-vgd

The Llama 3 Herd of Models

https://github.com/wongkinyiu/yolov9

YOLOR-Based Multi-Task Learning

https://github.com/woodfrog/ActionRecognition

Two-Stream Convolutional Networks for Action Recognition in Videos

https://github.com/wuzhiyuan2000/FedCache/blob/main/FedCache.py

FedICT: Federated Multi-task Distillation for Multi-access Edge Computing

https://github.com/wzzzd/LM_NER

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/x-lance/neusym-rag

Language Models are Few-Shot Learners

https://github.com/xiangzhang1015/EEG_Shape_Reconstruction

Multi-task Generative Adversarial Learning on Geometrical Shape Reconstruction from EEG Brain Signals

https://github.com/xiaoman-zhang/PMC-VQA

LLaMA: Open and Efficient Foundation Language Models

https://github.com/xiaoqian19940510/text-classification-

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/xiaoqian19940510/text-classification-surveys

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/xiaoxuegao499/LA-DNN-for-COVID-19-diagnosis

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

https://github.com/xingyuansun/pix3d

Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling

https://github.com/xinyooo/ALBERT4Rec

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

https://github.com/xiuyu0000/papers_with_examples/tree/main/vogue

VOGUE: Answer Verbalization through Multi-Task Learning

https://github.com/xuetianci/pacit

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/xusenlinzy/api-for-open-llm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/xverse-ai/xverse-13b

Measuring Massive Multitask Language Understanding

https://github.com/xverse-ai/xverse-7b

Measuring Massive Multitask Language Understanding

https://github.com/xvyaward/owq

LLaMA: Open and Efficient Foundation Language Models

https://github.com/xycforgithub/Airbnb-Comparison

Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension

https://github.com/xzhang97666/alpacare

LLaMA: Open and Efficient Foundation Language Models

https://github.com/yahah100/text_summarization

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

https://github.com/yakhyo/retinaface-pytorch

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/yangheng95/LC-ABSA

A Multi-task Learning Model for Chinese-oriented Aspect Polarity Classification and Aspect Term Extraction

https://github.com/yangheng95/pyabsa

A Multi-task Learning Model for Chinese-oriented Aspect Polarity Classification and Aspect Term Extraction

https://github.com/yangletliu/finlora

BloombergGPT: A Large Language Model for Finance

https://github.com/yangsenius/Pose-Neural-Fabrics-Search

Pose Neural Fabrics Search

https://github.com/yangyi02/densebox

DenseBox: Unifying Landmark Localization with End to End Object Detection

https://github.com/yangyucheng000/-insightface

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/yangyucheng000/RetinaFace

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/yangyucheng000/University/tree/main/model-2/gpt_neox

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

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