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Multi-Task Learning

Multi-task learning aims to learn multiple different tasks simultaneously while maximizing performance on one or all of the tasks.

( Image credit: Cross-stitch Networks for Multi-task Learning )

Papers

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Showing 2101–2150 of 3687 papers

TitleStatusHype
Tensor-based Cooperative Control for Large Scale Multi-intersection Traffic Signal Using Deep Reinforcement Learning and Imitation Learning—0
TENSOR RING NETS ADAPTED DEEP MULTI-TASK LEARNING—0
TEST_POSITIVE at W-NUT 2020 Shared Task-3: Joint Event Multi-task Learning for Slot Filling in Noisy Text—0
TEST_POSITIVE at W-NUT 2020 Shared Task-3: Cross-task modeling—0
Text-Attentional Convolutional Neural Networks for Scene Text Detection—0
Text-attentional convolutional neural network for scene text detection—0
Text-Based Joint Prediction of Numeric and Categorical Attributes of Entities in Knowledge Bases—0
Text-dependent Speaker Verification (TdSV) Challenge 2024: Challenge Evaluation Plan—0
Text Synopsis Generation for Egocentric Videos—0
TFormer: 3D Tooth Segmentation in Mesh Scans with Geometry Guided Transformer—0
The Database and Benchmark for the Source Speaker Tracing Challenge 2024—0
The Discussion Tracker Corpus of Collaborative Argumentation—0
The Effective Methods for Intrusion Detection With Limited Network Attack Data: Multi-Task Learning and Oversampling—0
The intrinsic motivation of reinforcement and imitation learning for sequential tasks—0
The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding—0
The NiuTrans System for the WMT20 Quality Estimation Shared Task—0
The NTNU System at SemEval-2017 Task 10: Extracting Keyphrases and Relations from Scientific Publications Using Multiple Conditional Random Fields—0
The NTT DCASE2020 Challenge Task 6 system: Automated Audio Captioning with Keywords and Sentence Length Estimation—0
The Perfect Blend: Redefining RLHF with Mixture of Judges—0
The Power of Active Multi-Task Learning in Reinforcement Learning from Human Feedback—0
The Reality of Multi-Lingual Machine Translation—0
The Sample Complexity of Meta Sparse Regression—0
The Sparse Reverse of Principal Component Analysis for Fast Low-Rank Matrix Completion—0
The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddings—0
The University of Helsinki and Aalto University submissions to the WMT 2020 news and low-resource translation tasks—0
Thompson Sampling with a Mixture Prior—0
Three Birds with One Stone: Multi-Task Temporal Action Detection via Recycling Temporal Annotations—0
Three Strategies to Improve One-to-Many Multilingual Translation—0
TIMIT Speaker Profiling: A Comparison of Multi-task learning and Single-task learning Approaches—0
TITAN: A Spatiotemporal Feature Learning Framework for Traffic Incident Duration Prediction—0
TMT: A Transformer-based Modal Translator for Improving Multimodal Sequence Representations in Audio Visual Scene-aware Dialog—0
To complete or to estimate, that is the question: A Multi-Task Approach to Depth Completion and Monocular Depth Estimation—0
Topic-Guided Abstractive Multi-Document Summarization—0
Topic Segmentation Model Focusing on Local Context—0
To Reverse the Gradient or Not: An Empirical Comparison of Adversarial and Multi-task Learning in Speech Recognition—0
Geometry-aware Two-scale PIFu Representation for Human Reconstruction—0
Towards Adversarially Robust Object Detection—0
Towards a Multi-modal, Multi-task Learning based Pre-training Framework for Document Representation Learning—0
Towards a One-stop Solution to Both Aspect Extraction and Sentiment Analysis Tasks with Neural Multi-task Learning—0
Towards a Real-Time Simulation of Elastoplastic Deformation Using Multi-Task Neural Networks—0
Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution—0
Towards automatic construction of multi-network models for heterogeneous multi-task learning—0
Towards Better Multi-task Learning: A Framework for Optimizing Dataset Combinations in Large Language Models—0
Reconsidering Learning Objectives in Unbiased Recommendation with Unobserved Confounders—0
Towards Consistent Multi-Task Learning: Unlocking the Potential of Task-Specific Parameters—0
Towards Emotion-aided Multi-modal Dialogue Act Classification—0
Towards Enhancing Faithfulness for Neural Machine Translation—0
Towards Generalized Models for Task-oriented Dialogue Modeling on Spoken Conversations—0
Towards Improving Abstractive Summarization via Entailment Generation—0
Towards Interpretable Multi-Task Learning Using Bilevel Programming—0
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All datasetsQM9Cityscapes testNYUv2OMNIGLOTCelebAUTKFacewireframe dataset

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PCGrad∆m%125.7—Unverified
2CAGrad∆m%112.8—Unverified
3IMTL-G∆m%77.2—Unverified
4Nash-MTL∆m%62—Unverified
5BayesAgg-MTL∆m%53.7—Unverified
#ModelMetricClaimedVerifiedStatus
1SwinMTLmIoU76.41—Unverified
2Nash-MTLmIoU75.41—Unverified
3MultiObjectiveOptimizationmIoU66.63—Unverified
#ModelMetricClaimedVerifiedStatus
1SwinMTLMean IoU58.14—Unverified
2Nash-MTLMean IoU40.13—Unverified
#ModelMetricClaimedVerifiedStatus
1Gumbel-Matrix RoutingAverage Accuracy93.52—Unverified
2Mixture-of-ExpertsAverage Accuracy92.19—Unverified
#ModelMetricClaimedVerifiedStatus
1MGDA-UBError8.25—Unverified
#ModelMetricClaimedVerifiedStatus
1BayesAgg-MTLdelta_m-2.23—Unverified
#ModelMetricClaimedVerifiedStatus
1LETRFH83.3—Unverified