A type of artificial intelligence that focuses on designing systems that can learn from and make decisions based on data.
The article introduces SLIM, a multi-critic learning approach for skill discovery, particularly focusing on robotic manipulation. It addresses the limitations of mutual information maximization in producing useful manipulation behaviors. The…
Continue readingThis academic paper addresses the concepts of identifiability and achievability in the context of intervention-based causal representation learning (CRL) under a general nonparametric latent causal model and an unknown transformation….
Continue readingThe article discusses the use of Raman Spectroscopy in biotechnology as a process analytical technology. It focuses on the use of Convolutional Neural Networks (CNNs) for handling non-Gaussian noise and…
Continue readingThe paper presents a new method called LTAU-FF for estimating the prediction uncertainty in atomistic force fields. The authors use model ensembles and cumulative distribution functions of per-sample errors to…
Continue readingThe paper introduces SymbolicAI, a framework that uses a logic-based approach for concept learning and flow management in generative processes. It integrates generative models with various solvers, treating large language…
Continue readingThe research introduces a statistical framework for early time classification algorithms, which aim to label a stream of features without processing the full input stream. The framework formulates a calibrated…
Continue readingThe article presents the results of fine-tuning large language models (LLMs) to detect vulnerabilities in source code. The authors used WizardCoder, an improvement of the state-of-the-art LLM StarCoder, and adapted…
Continue readingThe paper explores the increasing security risks associated with the use of AI and machine learning in healthcare systems. The authors demonstrate these risks through a case study of an…
Continue readingThis paper discusses unlearnable example attacks, which are data poisoning attacks that aim to degrade the accuracy of deep learning by adding imperceptible perturbations to training samples. The attack is…
Continue readingThe paper presents a new approach, PrivPGD, for differentially private data synthesis of protected tabular datasets, an important task in sensitive domains like healthcare and government. Existing methods typically use…
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