A type of artificial intelligence that focuses on designing systems that can learn from and make decisions based on data.
This article focuses on the problem of factually incorrect responses generated by Language Models (LMs). The proposed solution involves augmenting LMs with knowledge retrieved from an external source. However, this…
Continue readingThis academic article delves into the computational capabilities of language models based on recurrent neural networks (RNNs). It extends the Turing completeness, a concept showing the computational potential of a…
Continue readingThe article presents a new technique called ‘Emulated Fine-Tuning’ (EFT) for large language models. The authors explore the two-stage training pipeline of language models – pre-training and fine-tuning – and…
Continue readingThe article discusses the issue of privacy in graph embedding, a powerful tool for learning latent representations for nodes in a graph. The authors highlight the need for privacy-preserving graph…
Continue readingThe article discusses a comparative analysis of three Large Language Models (LLMs) adaptation techniques – Low-Rank Adaptation (LoRA), Soft Prompt Tuning (SPT), and In-Context Learning (ICL). These techniques help in…
Continue readingThe paper proposes a federated heterogeneous graph neural network (FedHGNN) for privacy-preserving recommendations. The authors suggest that the Heterogeneous Information Network (HIN) is partitioned into private HINs stored on the…
Continue readingThe article presents a study on keystroke authentication using Transformer-based networks. Two architectures, bi-encoder and cross-encoder, are compared for their effectiveness in keystroke authentication. Various loss functions and distance metrics…
Continue readingThe article revisits the transferable adversarial examples, which are a significant security concern in real-world, black-box attack scenarios. The authors identify two main problems in common evaluation practices: lack of…
Continue readingIn the world of privacy-preserving machine learning, empirical privacy defenses are often used to maintain training data privacy without significantly affecting model utility. These defenses usually require reference data, an…
Continue readingThe research proposes Adaptive Bayesian Domain Randomization via Strategic Fine-tuning (BayRnTune), a method to significantly speed up the learning processes in domain randomization by fine-tuning from previously learned policy. The…
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