#MultiTaskLearning

Paper: Improving Ads Ranking Models with Auxiliary Supervision: Techniques and Insights #AdsRanking #AuxiliarySupervision #MultiTaskLearning #MachineLearning #RecommenderSystems #AdTech #MetaAI #SplitArchitecture #LinearizedLabels #DeepLearning #RecommendationSystems #AIResearch

Our paper on Multi-Task Structural Learning (MTSL) accepted at #ICML2023! MTSL simultaneously learns the multitask architecture and its parameters, mimicking the structural changes that occur in the brain during learning. 1/4 #MultiTaskLearning #deeplearning #neuroscience

In speech, we employ linguistics hierarchies to decide which words to use over others. Can we teach computers the same? | by @omarsar0 #NLP #Naturallanguageprocessing #multitasklearning

Equation: Tasks×SharedReps → generalization↑ 🧠 Cut compute; share features; curb overfit; tune loss weights; separate heads; audit conflicts 🛠️ - Explore → #AI #MultiTaskLearning #DeepLearning

Our paper on "Joint Entity Extraction and Assertion Detection for Clinical Text" got accepted at ACL. #multitasklearning #acl #clinical #nlp #comprehend #medical #comprehendmedical #detection #extraction #text

SUBARU Labではクルマ・人・走行領域・道路標示・信号などを同時に認識させるために、DetectionやSemantic Segmentationといった複数のHeadsが1つのBackboneを共有したMulti-task Networkを開発しています。SUBARU ASURA Netと呼んでいます。 #画像認識 #MultitaskLearning

Our new paper where we show the effect of #multitasklearning on #sentimentanalysis #naturallanguageprocessing #senticnet

A new paper was published by Erik Cambria et. al., Nanyang Technological University, Singapore. Polarity and Subjectivity Detection with Multitask Learning and BERT Embedding #multitasklearning #polaritydetection #deeplearning #marketintelligence

🔥 Read our Paper 📚 Chinese Named Entity Recognition Model Based on Multi-Task Learning 🔗 👨‍🔬 by Qin Fang et al. #multitasklearning #Chinesenamedentityrecognition

I wrote an article on #multitasklearning demonstrating my project "faceless". I am glad to share that it got published on @wandb. Here's the link: Thanks @RisingSayak for referring me. Thanks @lavanyaai with edits. Feedbacks really appreciated.❣️

3 IAB students successfully defended Master's Thesis Himanshu - #MultitaskLearning Divyanshu - #ActiveLearning @geekysethi - Small Sample Size Problem Congratulations! #proudadvisor #deeplearning #articialintelligence #aiforsocialgood #computervision #ArtificialIntelligence

Keynote of @RichardSocher of @salesforce on #MultiTaskLearning at #DLIndaba2019 #SautiYetu @AiSenegal. A step toward #GeneralAI?

We were pleased to present 6 papers from our group @CoLLAs_Conf. Our advanced research lab @NavInfoEurope carries out fascinating research at the intersection of neuroscience and AI. We develop neuro-inspired AI models that can learn efficiently from multiple tasks.#CoLLAs2022

(5/6) Consistency is the key to further mitigating catastrophic forgetting in continual learning Paper: Video: #ContinualLearning #DeepLearning

(6/6) Curbing Task Interference using Representation Similarity-Guided Multi-Task Feature Sharing Video: #MultiTaskLearning #DeepLearning

Please join us virtually at #ICML2023 to check out our papers: 1) Multi-Task Structural Learning using Local Task Similarity induced Neuron Creation and Removal 2) BiRT: Bio-inspired Replay in Vision Transformers for Continual Learning #ContinualLearning #MultiTaskLearning #AI

Let's dive into #MultiTaskLearning! 📚🚀We covered the basics in the last post, and now it's time to explore the pros and cons of this approach, along with tips for maximizing its effectiveness. Check it out for more insights: #MachineLearning #AI

For #MultiTaskLearning in @ContinualAI how does a robot learn domain knowledge eg. tasks relationships & complexities? Intrinsic motivation & active imitation enable #TransferLearning & hierarchical #RL 🔖 #ArtificialIntelligence #IA

Multi-task learning is a type of machine learning where machines can learn to perform multiple tasks simultaneously, making it a powerful tool for efficiency and performance. #MultiTaskLearning #AI

Want to share some great works at #BMVC2020. EigenSDF ( uses a linear decoder, computed by PCA on ground truth 3D models. This approach allows the model to scale to high res 3D models. Great work from @MILAMontreal, UniOfQueensland and UniOfMontreal. (1/4)

ViewSynth integrates a view synthesis module in their learning pipeline and learn to associate correspondences between a pair of depth images ( They show the model captures the geometry info and, hence, achieves better matching. Great work, Jisan Mahmud!

Mish proposes a new activation function. Compared to ReLU and Swish, Mish offers a smooth loss func landscape ( The optimum region is wider, allowing better generalisation and good for #TransferLearning and #MultitaskLearning. Great work, @DigantaMisra1!

Multi-Task Architectures: A Comprehensive Guide | @TDataScience #MultiTaskLearning #DeepLearning #MachineLearningArchitecture #AI #DataScience #TowardsDataScience

By letting tasks converge on their own, and then switching to a knowledge distillation loss, Amazon scientists' method outperforms existing multitask learning approaches by up to 1.5%. Learn more about their @NAACLmeeting paper. #NAACL2022 #MultitaskLearning

🤗Excited to share that we will be at NeurIPS 2023 in New Orleans to present our spotlight paper “Episodic Multi-Task Learning with Heterogeneous Neural Processes ”. Paper: Code: #NeurIPS2023 #Bayesian #multitasklearning