Papers with Code 아카이브

Multi-Task Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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/yangheng95/DaNuoYi

공식 코드

Evolutionary Multi-Task Injection Testing on Web Application Firewalls

https://github.com/yangheng95/LCF-ATEPC

공식 코드

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

https://github.com/yangsenius/PoseNFS

공식 코드

Pose Neural Fabrics Search

https://github.com/yangyangxu0/demt

공식 코드

DeMT: Deformable Mixer Transformer for Multi-Task Learning of Dense Prediction

https://github.com/yangyangxu0/mqtransformer

공식 코드

Multi-Task Learning with Multi-Query Transformer for Dense Prediction

https://github.com/yankd22/calm

공식 코드

CALM: Consensus-Aware Localized Merging for Multi-Task Learning

https://github.com/yanqidai/igb4mtl

공식 코드

Improvable Gap Balancing for Multi-Task Learning

https://github.com/yaoching0/gac

공식 코드

Breaking the Ceiling of the LLM Community by Treating Token Generation as a Classification for Ensembling

https://github.com/yeongseoj/conkd

공식 코드

Towards a Unified Conversational Recommendation System: Multi-task Learning via Contextualized Knowledge Distillation

https://github.com/yeonsw/BLANC

공식 코드

Context-Aware Answer Extraction in Question Answering

https://github.com/yewen1486/emtal

공식 코드

Transforming Vision Transformer: Towards Efficient Multi-Task Asynchronous Learning

https://github.com/yibowang214/multiner

공식 코드

Named Entity Recognition via Machine Reading Comprehension: A Multi-Task Learning Approach

https://github.com/yichaolu/traffic4cast2021

공식 코드

Learning to Transfer for Traffic Forecasting via Multi-task Learning

https://github.com/yifanycc/loretta

공식 코드

LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language Models

https://github.com/yifei-he/excessmtl

공식 코드

Robust Multi-Task Learning with Excess Risks

https://github.com/yifei-he/localize-and-stitch

공식 코드

Localize-and-Stitch: Efficient Model Merging via Sparse Task Arithmetic

https://github.com/yilunzhu/coref-mtl

공식 코드

Incorporating Singletons and Mention-based Features in Coreference Resolution via Multi-task Learning for Better Generalization

https://github.com/yiming-zh/DPL

공식 코드

Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis

https://github.com/yingtaomj/Reasoning-in-Dialog

공식 코드

Reasoning in Dialog: Improving Response Generation by Context Reading Comprehension

https://github.com/yinjunbo/UMA-MOT

공식 코드

A Unified Object Motion and Affinity Model for Online Multi-Object Tracking

https://github.com/yjiangcm/golf_for_idrr

공식 코드

Global and Local Hierarchy-aware Contrastive Framework for Implicit Discourse Relation Recognition

https://github.com/ylkuo/ltl-zero-shot

공식 코드

Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas

https://github.com/ylsung/vl-merging

공식 코드

An Empirical Study of Multimodal Model Merging

https://github.com/ymhe12/GS-Phong

공식 코드

GS-Phong: Meta-Learned 3D Gaussians for Relightable Novel View Synthesis

https://github.com/yobibyte/unitary-scalarization-dmtl

공식 코드

In Defense of the Unitary Scalarization for Deep Multi-Task Learning

https://github.com/yongcaoplus/cgr_damo

공식 코드

Geo-Encoder: A Chunk-Argument Bi-Encoder Framework for Chinese Geographic Re-Ranking

https://github.com/yongchanghao/multi-task-nat

공식 코드

Multi-Task Learning with Shared Encoder for Non-Autoregressive Machine Translation

https://github.com/youngfish42/FMTL-Benchmark

공식 코드

Federated Multi-Task Learning on Non-IID Data Silos: An Experimental Study

https://github.com/youssefkhalil320/deepchest-mtl

공식 코드

DeepChest: Dynamic Gradient-Free Task Weighting for Effective Multi-Task Learning in Chest X-ray Classification

https://github.com/ytstat/rl-mtl-tl

공식 코드

Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness

https://github.com/yuanmu97/mlink

공식 코드

MLink: Linking Black-Box Models from Multiple Domains for Collaborative Inference

https://github.com/yuanwei1989/landmark-detection

공식 코드

Fast Multiple Landmark Localisation Using a Patch-based Iterative Network

https://github.com/yuanwei1989/plane-detection

공식 코드

Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network

https://github.com/yuchen005/gradient-remedy

공식 코드

Gradient Remedy for Multi-Task Learning in End-to-End Noise-Robust Speech Recognition

https://github.com/yuchen005/nase

공식 코드

Noise-aware Speech Enhancement using Diffusion Probabilistic Model

https://github.com/yuchen005/unified-enhance-separation

공식 코드

Unifying Speech Enhancement and Separation with Gradient Modulation for End-to-End Noise-Robust Speech Separation

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