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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/flab-facial-exp/FG2020-ABAW-VA-EXPR

A Multi-term and Multi-task Analyzing Framework for Affective Analysis in-the-wild

https://github.com/flagalpha/llama2-chinese

LLaMA: Open and Efficient Foundation Language Models

https://github.com/flexible-fl/flex-nlp

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/flowritecom/flow-merge

Editing Models with Task Arithmetic

https://github.com/fmpr/DeepJMQR

Beyond expectation: Deep joint mean and quantile regression for spatio-temporal problems

https://github.com/formulamonks/llm-benchmarker-suite

Scaling Instruction-Finetuned Language Models

https://github.com/foundation-model-stack/fms-fsdp

PaLM: Scaling Language Modeling with Pathways

https://github.com/francismontalbo/covid19_diagnosis_and_analysis_of_truncated_dcnns

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

https://github.com/frb-yousefi/multitask-gp

Multi-task Learning for Aggregated Data using Gaussian Processes

https://github.com/freedomintelligence/huatuogpt

LLaMA: Open and Efficient Foundation Language Models

https://github.com/fskong/FT-Classifier-for-Model-Merging

Representation Surgery for Multi-Task Model Merging

https://github.com/fsoft-ai4code/codecapybara

Evaluating Large Language Models Trained on Code

https://github.com/fvilmos/multi_task_learning_shape_color_position

An Overview of Multi-Task Learning in Deep Neural Networks

https://github.com/fywalter/label-bias

Language Models are Few-Shot Learners

https://github.com/gair-nlp/abel

Solving Quantitative Reasoning Problems with Language Models

https://github.com/ganjinzero/rrhf

LLaMA: Open and Efficient Foundation Language Models

https://github.com/generation21/generation6011

Self-Supervised Generalisation with Meta Auxiliary Learning

https://github.com/ggerganov/llama.cpp

Language Models are Few-Shot Learners

https://github.com/ggml-org/llama.cpp

Language Models are Few-Shot Learners

https://github.com/glb400/Toy-RecLM

Llama 2: Open Foundation and Fine-Tuned Chat Models

https://github.com/glouppe/info8010-deep-learning

Evaluating Large Language Models Trained on Code

https://github.com/gmum/dl-mo-2021

Language Models are Few-Shot Learners

https://github.com/google-research/flan

Scaling Instruction-Finetuned Language Models

https://github.com/google/paxml

PaLM: Scaling Language Modeling with Pathways

https://github.com/gpt4life/alpagasus

GPT-4 Technical Report

https://github.com/grantslatton/llama.cpp

Language Models are Few-Shot Learners

https://github.com/graph-star-team/graph_star

Graph Star Net for Generalized Multi-Task Learning

https://github.com/graykode/ALBERT-Pytorch

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

https://github.com/greenbitai/low_bit_llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/gsan2/FairMOT

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

https://github.com/gstrezoski/TaskRouting

Many Task Learning with Task Routing

https://github.com/guinmoon/llmfarm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/guluguluhhhh/contrib/tree/37-2/application/multi-task-learning-using-uncertainty-to

Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

https://github.com/gvalvano/wss-multiscale-adversarial-attention-gates

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

https://github.com/haisongzhang/roberta-tiny-cased

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/haiyang-w/git

Language Models are Few-Shot Learners

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