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/tiger-ai-lab/mmlu-pro

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

https://github.com/tighu20/Kaggle-Tweet-Sentiment-Extraction

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/tj-kim/pfeddef_v1

Federated Multi-Task Learning under a Mixture of Distributions

https://github.com/tomas-gajarsky/facetorch

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/tonyzhaozh/few-shot-learning

Language Models are Few-Shot Learners

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

DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction

https://github.com/totoadel/Minst

You Only Learn One Representation: Unified Network for Multiple Tasks

https://github.com/totoroKalic/pamtri-mindspore

PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data

https://github.com/traviscoan/cards

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/tshi04/ACCE

An Interpretable and Uncertainty Aware Multi-Task Framework for Multi-Aspect Sentiment Analysis

https://github.com/tslgit0/CT5

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

https://github.com/turkunlp/megatron-deepspeed

Language Models are Few-Shot Learners

https://github.com/turningpoint-ai/visualthinker-r1-zero

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

https://github.com/ubicomplab/rppg-toolbox

BigSmall: Efficient Multi-Task Learning for Disparate Spatial and Temporal Physiological Measurements

https://github.com/um-arm-lab/efficient-eng-2-ltl

Language Models are Few-Shot Learners

https://github.com/unispac/visual-adversarial-examples-jailbreak-large-language-models

GPT-4 Technical Report

https://github.com/uoe-agents/sami

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

https://github.com/usyd-fsalab/fp6_llm

Llama 2: Open Foundation and Fine-Tuned Chat Models

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

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/valdecy/pybibx

Gemini: A Family of Highly Capable Multimodal Models

https://github.com/vaseline555/Federated-Learning-in-PyTorch

LEAF: A Benchmark for Federated Settings

https://github.com/vcskaushik/LLMzip

LLaMA: Open and Efficient Foundation Language Models

https://github.com/vedantc6/mtl-dts

Deeper Task-Specificity Improves Joint Entity and Relation Extraction

https://github.com/vhellendoorn/code-lms

Evaluating Large Language Models Trained on Code

https://github.com/vico-uoe/universalrepresentations

Knowledge Distillation for Multi-task Learning

https://github.com/viethoang1512/kpa

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/vigsivan/NeurReg_Pytorch

NeurReg: Neural Registration and Its Application to Image Segmentation

https://github.com/vilm-ai/viet-llm-eval

Language Models are Few-Shot Learners

https://github.com/vios-s/multiscale-adversarial-attention-gates

Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates

https://github.com/vision-cair/minigpt-4

MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning

https://github.com/vivo-ai-lab/bluelm

Measuring Massive Multitask Language Understanding

https://github.com/vladimirwest/insightface_cinematic

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/vlm-rl/ocean-r1

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

https://github.com/volcengine/vegiantmodel

Language Models are Few-Shot Learners

https://github.com/voxmenthe/OmniNet_1

OmniNet: A unified architecture for multi-modal multi-task learning

https://github.com/vvvm23/albert

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

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