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Found 9 papers, 1 papers with code
Accelerating Exploration with Unlabeled Prior Data
Learning to solve tasks from a sparse reward signal is a major challenge for standard reinforcement learning (RL) algorithms.
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation
In our evaluation, we compared this improved model to two benchmarks: the pretrained SAM and the widely used model, nnUNetv2.
A 5' UTR Language Model for Decoding Untranslated Regions of mRNA and Function Predictions
The model outperformed the best-known benchmark by up to 42% for predicting the Mean Ribosome Loading, and by up to 60% for predicting the Translation Efficiency and the mRNA Expression Level.
Domain Knowledge Distillation from Large Language Model: An Empirical Study in the Autonomous Driving Domain
Engineering knowledge-based (or expert) systems require extensive manual effort and domain knowledge.
Using Deep Mixture-of-Experts to Detect Word Meaning Shift for TempoWiC
This paper mainly describes the dma submission to the TempoWiC task, which achieves a macro-F1 score of 77. 05% and attains the first place in this task.
Peek-a-Boo: What (More) is Disguised in a Randomly Weighted Neural Network, and How to Find It Efficiently
In this work, we define an extended class of subnetworks in randomly initialized NNs called disguised subnetworks, which are not only "hidden" in the random networks but also "disguised" -- hence can only be "unmasked" with certain transformations on weights.
Towards Real-time Drowsiness Detection for Elderly Care
The primary focus of this paper is to produce a proof of concept for extracting drowsiness information from videos to help elderly living on their own.
Machine Learning With Feature Selection Using Principal Component Analysis for Malware Detection: A Case Study
In this paper, we have proposed a novel approach by extending our recently suggested artificial neural network (ANN) based model with feature selection using the principal component analysis (PCA) technique for malware detection.
MLPdf: An Effective Machine Learning Based Approach for PDF Malware Detection
Due to the popularity of portable document format (PDF) and increasing number of vulnerabilities in major PDF viewer applications, malware writers continue to use it to deliver malware via web downloads, email attachments and other methods in both targeted and non-targeted attacks.
Cryptography and Security
