Statistics and much of deep learning share the feature that, to understand high-dimensional data, we need an intermediate low-dimensional representation. LLMs’ architecture seemed not to conform to this. But this paper came to my attention. It appears that a few weights have a

Imagine an AI that not only learns quickly but adapts to your daily tasks autonomously. Meet Premier-TACO 🌮, a multitask feature representation learning designed for few-shot adaptation in sequential decision-making. Project 🔗: #FoundationModel4RL.A 🧵

DreamTeacher: Pretraining Image Backbones with Deep Generative Models. Presents a self-supervised feature representation learning framework that utilizes generative networks for pre-training downstream image backbones. proj: abs:

🔬 Excited to share the publication "Applying Self-Supervised Learning to Image Quality Assessment in Chest CT Imaging" 👉 #self_supervised_learning #feature_representation_learning #convolutional_denoising_autoencoder #task_based_approach #chest #CT

DreamTeacher: Pretraining Image Backbones with Deep Generative Models. paper page: introduce a self-supervised feature representation learning framework DreamTeacher that utilizes generative networks for pre-training downstream image backbones. We propose

Led by Ritabrata, happy to present our recent work on single-cell data integration. scDecorr - Feature decorrelation representation learning with domain adaptation

Protein Representation Learning with Sequence Information Embedding: Does it Always Lead to a Better Performance?.- Introduces PROTLOCA, a local geometry alignment method based solely on amino acid structure representation (by GVP).- Using 3Di tokens as node feature yields the

- Subgraph Extraction .- Feature Extraction .- Contrastive Learning .- Graph Neural Network .- Vector Representation. all rolled into one after a long week of intense coding. 😫 . #GNN #neuralnetworks #graph #Python #TensorFlow #pythonprogramming #vector #coding

Congratulations to Dr. Bulat Khaertdinov, and his supervisory team, for the successful defence of his PhD thesis entitled "Feature Representation Learning for Human Activity Recognition"!

ال Representation Learning من المفاهيم المهمة في تعلم الآلة. وله صور كثيرة مثلا لو عند بيانات فيها ٢٠ الف خاصية feature ممكن تمثلها بطريقة تقلل البعدية high-dimensional. أيضا تمثيل النصوص والصور وتحويلها إلى vectors

A Spectral Condition for Feature Learning. Maximal update parametrization and representation evolution in wide networks follow from scaling spectral norms by √(fan-out/fan-in), rather than using heuristic Frobenius or entry-wise norms. This scaling enables feature learning

My recent talks about feature representation learning discussed connections between different formulations (information, computation, feature space), including geometric interpretation in feature spaces and operational meanings in feature learning/adaptations.

🔰feature learning or representation learning -. is a set of techniques that allow a system to automatically discover representations needed for feature detection or classification from raw data. replaces manual FE & allows machine to both learn features & use to perform a task

MFDSMC: Accurate Identification of Cancer-Driver Synonymous Mutations Using Multiperspective Feature Representation Learning #machinelearning #compchem

Manual feature engineering is extremely expensive. That is why feature learning was created. Facts about feature learning:.1) = representation learning.2) looks to features about a dataset.3) allows building a more effective ML model. 1/2

Video-LLaVA: Learning United Visual Representation by Alignment Before Projection. - Unifies visual representation into the language feature space to advance the foundational LLM towards a unified LVLM.- Outperforms Video-ChatGPT.

Can we boost MAE performance without extra computational costs? 🤔. 💡 Introducing #ColorMAE: a data-independent masking strategy for MAE that improves feature representation learning with no added computational overhead. 📰Paper accepted at #ECCV2024:

New Research: DSCnet: detection of drug and alcohol addiction mechanisms based on multi-angle feature learning from the hybrid representation of EEG #FrontiersIn #Neuroscience.

Congratulations to Dr. Akhil Kumar (Assistant Professor #scsetbennett) for acceptance of the research article “A Novel Multi-Layer Feature Fusion Based BERT-CNN for Sentence Representation Learning and Classification” for #publication in Robotic Intelligence and Automation.

This #EHUikerketa introduces an information-processing framework to improve credit scoring models by blending several methods of graph representation learning: feature engineering, graph embeddings, and graph neural networks.