A type of artificial intelligence that focuses on designing systems that can learn from and make decisions based on data.
The research introduces a novel framework, FedPEAT, which converges Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning. The framework is designed to address the challenges in deploying and fine-tuning…
Continue readingThe paper introduces a certified backdoor detector (CBD) for deep neural networks. The CBD is based on a conformal prediction scheme and uses a statistic called local dominant probability. The…
Continue readingThis research paper delves into the theoretical aspects of Low-Rank Adaptation (LoRA), a prevalent technique for fine-tuning pre-trained models like large language models and diffusion models. Despite its practical success,…
Continue readingThe article, ‘Little Exploration is All You Need’, presents a novel modification of the standard UCB (Upper Confidence Bound) algorithm in the multi-armed bandit problem. The authors propose an adjusted…
Continue readingThis study presents a new ensemble approach for feature selection in time series forecasting, particularly in non-stationary and limited sample scenarios. The approach utilizes a hierarchical structure to exploit feature…
Continue readingThe article discusses a method of training neural networks to generate a variety of discrete representations, controlled by the complexity of the representations. This method is inspired by the human…
Continue readingThis academic article introduces a new algorithm for efficiently solving the damped Fisher matrix in large-scale scenarios where the number of parameters significantly exceeds the number of available samples. This…
Continue readingThe study explores the role of bifurcations in recurrent neural networks (RNNs) training. Bifurcations refer to changes in a system’s dynamical behavior as its parameters are varied. The authors mathematically…
Continue readingThe article introduces BLIS-Net, a novel Graph Neural Network (GNN) designed for signal classification. Unlike traditional GNNs, BLIS-Net can capture both local and global signal structures, as well as low…
Continue readingThe paper presents PAC-tuning, a method for fine-tuning pretrained language models (PLMs) to enhance their generalization performance. The method uses PAC-Bayes training to directly minimize the PAC-Bayes generalization bound, resulting…
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