Results 111 to 120 of about 7,883,935 (303)

Spatio-Temporal Crop Aggregation for Video Representation Learning [PDF]

, 2023
We propose Spatio-temporal Crop Aggregation for video representation LEarning (SCALE), a novel method that enjoys high scalability at both training and inference time.
Jenni, Simon, Favaro, Paolo, Sameni, Sepehr   +2 more
core   +1 more source

Few-Shot Fine-Grained Image Classification: A Comprehensive Review

AI
Few-shot fine-grained image classification (FSFGIC) methods refer to the classification of images (e.g., birds, flowers, and airplanes) belonging to different subclasses of the same species by a small number of labeled samples.
Jie Ren, Changmiao Li, Yaohui An, Weichuan Zhang, Changming Sun   +4 more
doaj   +1 more source

Accurate and noninvasive prostate cancer detection using plasma‐derived extracellular vesicle RNA

FEBS Open Bio, EarlyView.
Plasma extracellular vesicles were captured with WGA‐conjugated magnetic beads and profiled for RNA biomarkers. A three‐RNA panel (NM_024955, NR_047469, and NR_002564) distinguished prostate cancer from healthy controls and benign prostatic hyperplasia, supporting a simple, noninvasive approach to improve prostate cancer detection.
Hanping Wei, Haoran Wu, Wei Feng
wiley   +1 more source

Contrastive sentence representation learning with adaptive false negative cancellation [PDF]

Contrastive sentence representation learning has made great progress thanks to a range of text augmentation strategies and hard negative sampling techniques.
Lingling Xu, Fu Lee Wang, Xu, Lingling, Li, Qing, Weiming Wang, Tao, Xiaohui, Wang, Weiming, Xiaohui Tao, Xie, Haoran, Haoran Xie, Qing Li, Wang, Fu Lee   +11 more
core   +1 more source

Learning Representational Disparities

CoRR
We propose a fair machine learning algorithm to model interpretable differences between observed and desired human decision-making, with the latter aimed at reducing disparity in a downstream outcome impacted by the human decision. Prior work learns fair representations without considering the outcome in the decision-making process.
Pavan Ravishankar, Rushabh Shah, Daniel B. Neill   +2 more
openaire   +2 more sources

Learning Action Representations for Reinforcement Learning

CoRR, 2019
Most model-free reinforcement learning methods leverage state representations (embeddings) for generalization, but either ignore structure in the space of actions or assume the structure is provided a priori. We show how a policy can be decomposed into a component that acts in a low-dimensional space of action representations and a component that ...
Yash Chandak, Georgios Theocharous, James E. Kostas, Scott M. Jordan, Philip S. Thomas   +4 more
openaire   +3 more sources

From patient advocacy to patient‐driven research: Building active partnerships beginning at the bench to reach the bedside

FEBS Open Bio, EarlyView.
Research is strongest when conducted alongside patients, not just about them. Patient research organizations help integrate patient perspectives into research priorities, study design, and scientific meetings, leading to meaningful patient outcomes and development of relevant therapies.
Jenica H. Kakadia, Rachel Heilmann, Maximiliano Barrientos, Laura MacNeill, Ingrid Vallee, Sarah K. Schultz, Patrick O'Donoghue, Angelica A. Moresco, Victoria M. Siu, Ilka U. Heinemann   +9 more
wiley   +1 more source

Learning sparse representations in reinforcement learning

CoRR, 2019
Reinforcement learning (RL) algorithms allow artificial agents to improve their selection of actions to increase rewarding experiences in their environments. Temporal Difference (TD) Learning -- a model-free RL method -- is a leading account of the midbrain dopamine system and the basal ganglia in reinforcement learning.
Jacob Rafati, David C. Noelle
openaire   +3 more sources

Cell surface CD11c as a neutrophil aging marker molecule

FEBS Open Bio, EarlyView.
Cell surface CD11chi neutrophils were more aged and had better phagocytic function than CD11c−/lo neutrophils. Transcriptomic analysis of CD11chi neutrophils and CD11c−/lo neutrophils in pediatric population showed that the most difference was seen in infants.
Sophia Koutsogiannaki, Fahd Alhamdan, Erik Malm, Marie Bermudez, Samuel Y. Kim, Koichi Yuki   +5 more
wiley   +1 more source

A Deep Learning Based Induced GNSS Spoof Detection Framework

IEEE Transactions on Machine Learning in Communications and Networking
The Global Navigation Satellite System (GNSS) plays a crucial role in critical infrastructure by delivering precise timing and positional data. Nonetheless, the civilian segment of the GNSS remains susceptible to various spoofing attacks, necessitating ...
Asif Iqbal, Muhammad Naveed Aman, Biplab Sikdar   +2 more
doaj   +1 more source