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Found 9 papers, 4 papers with code
Automatic Machine Learning Framework to Study Morphological Parameters of AGN Host Galaxies within $z < 1.4$ in the Hyper Supreme-Cam Wide Survey
We present a composite machine learning framework to estimate posterior probability distributions of bulge-to-total light ratio, half-light radius, and flux for Active Galactic Nucleus (AGN) host galaxies within $z<1. 4$ and $m<23$ in the Hyper Supreme-Cam Wide survey.
Robust Amortized Bayesian Inference with Self-Consistency Losses on Unlabeled Data
In this paper, we propose a semi-supervised approach that enables training not only on labeled simulated data generated from the model, but also on \textit{unlabeled} data originating from any source, including real data.
On the challenges of detecting MCI using EEG in the wild
Recent studies have shown promising results in the detection of Mild Cognitive Impairment (MCI) using easily accessible Electroencephalogram (EEG) data which would help administer early and effective treatment for dementia patients.
Source-Free and Image-Only Unsupervised Domain Adaptation for Category Level Object Pose Estimation
We focus on individual locally robust mesh vertex features and iteratively update them based on their proximity to corresponding features in the target domain even when the global pose is not correct.
Ranked #1 on
Unsupervised Domain Adaptation
on OOD-CV
Do pretrained Transformers Learn In-Context by Gradient Descent?
Furthermore, the theoretical hand-constructed weights used in these studies have properties that don't match those of real LLMs.
Stress Testing Chain-of-Thought Prompting for Large Language Models
This report examines the effectiveness of Chain-of-Thought (CoT) prompting in improving the multi-step reasoning abilities of large language models (LLMs).
DECODE: Data-driven Energy Consumption Prediction leveraging Historical Data and Environmental Factors in Buildings
In summary, our research contributes to energy prediction by offering a robust LSTM model that outperforms alternative methods and operates with remarkable efficiency, generalizability, and reliability.
Repeated Environment Inference for Invariant Learning
The EI step uses a reference model which focuses on spurious correlations to efficiently reach a good environment partition.
VStegNET: Video Steganography Networkusing Spatio-Temporal features andMicro-Bottleneck
Traditional image steganography techniques hide the secret image intohigh-frequency regions of the cover images.
