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/junwei-pan/recscope

STEM: Unleashing the Power of Embeddings for Multi-task Recommendation

https://github.com/kabongosalomon/listra

Synchronous Speech Recognition and Speech-to-Text Translation with Interactive Decoding

https://github.com/kamalkraj/ALBERT-TF2.0

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

https://github.com/kamanphoebe/look-into-moes

Mixtral of Experts

https://github.com/kapllan/zeroshot_lexglue

Scaling Instruction-Finetuned Language Models

https://github.com/karpathy/build-nanogpt

Language Models are Few-Shot Learners

https://github.com/karpathy/llama2.c

Training Compute-Optimal Large Language Models

https://github.com/karpathy/llm.c

Language Models are Few-Shot Learners

https://github.com/kathrynchapman/QA_project

Attentive History Selection for Conversational Question Answering

https://github.com/kaushaltrivedi/fast-bert

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/kayvr/token-hawk

LLaMA: Open and Efficient Foundation Language Models

https://github.com/kbressem/medalpaca

LLaMA: Open and Efficient Foundation Language Models

https://github.com/kevinduh/san_mrc

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

https://github.com/khyeyoon/face_recognition

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/kjybinp/SCGAN

Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal

https://github.com/knowlab/bi-weekly-paper-presentation

Textbooks Are All You Need II: phi-1.5 technical report

https://github.com/knuddj1/op_text

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/knuddy/op_text

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/kpe/bert-for-tf2

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

https://github.com/krafton-ai/korani

LLaMA: Open and Efficient Foundation Language Models

https://github.com/ku2482/discor.pytorch

DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction

https://github.com/ku2482/rljax

DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction

https://github.com/kun-g/Scraping-Github-trending

YOLOP: You Only Look Once for Panoptic Driving Perception

https://github.com/kun0906/brifca

An Efficient Framework for Clustered Federated Learning

https://github.com/kyegomez/GPT3

Language Models are Few-Shot Learners

https://github.com/l294265421/OTE-MTL-ASOTE

A Multi-task Learning Framework for Opinion Triplet Extraction

https://github.com/labmlai/annotated_deep_learning_paper_implementations

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

https://github.com/labmlai/neox

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

https://github.com/lambert-x/prolab

Language Models are Few-Shot Learners

https://github.com/lashoun/hanna-benchmark-asg

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/yolor

You Only Learn One Representation: Unified Network for Multiple Tasks

https://github.com/lflage/openfactscore

GPT-4 Technical Report

https://github.com/liano3/RAG-fairness

REPLUG: Retrieval-Augmented Black-Box Language Models

https://github.com/lifuguan/UPDETR-mmdet

UP-DETR: Unsupervised Pre-training for Object Detection with Transformers

https://github.com/lilin19890401/FairMOT

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

https://github.com/lilygeorgescu/AED-SSMTL

Anomaly Detection in Video via Self-Supervised and Multi-Task Learning

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