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Transparency in artificial intelligence has become a question of provenance. Provenance tells you where a piece of content came from; it does not tell you why what it asserts was decided.
A number with no recorded reason may end up deciding for a patient Read More »
Sequential recommendation is a central task in recommender systems, and recent research has increasingly shifted toward generative recommenders that leverage both sequential patterns and semantic item information. However, these methods are often evaluated on a small set of widely used benchmarks. This raises a natural question: do these benchmarks actually require the advanced modeling capabilities
Large language models (LLMs) are designed to generate answers to user prompts, which often drives them to respond even when uncertainty is high, information is incomplete, or a refusal would be more appropriate. In healthcare, this tendency can be dangerous: confidently stated but inaccurate medical advice can cause significant harm, making the ability to abstain
🔘 Paper page: nature.com/articles/s41586-025-10014-0 Abstract Deep learning models that predict functional genomic measurements from DNA sequences are powerful tools for deciphering the genetic regulatory code. Existing methods involve a trade-off between input sequence length and prediction resolution, thereby limiting their modality scope and performance1,2,3,4,5. We present AlphaGenome, a unified DNA sequence model, which takes as
Advancing regulatory variant effect prediction with AlphaGenome Read More »
Systems for diagnosis, prognosis and imaging have repeatedly been deployed or promoted on the strength of performance that proved fragile under independent testing. A model that performs well in the hospital where it was born has proven only one thing: that it works at home. Consider the most instructive failure in recent clinical AI. The
External validation is not a bureaucratic detail Read More »
In 2019, a research team led by the physician and economist Ziad Obermeyer reverse-engineered a commercial algorithm already running quietly across the United States health system. The tool, sold by Optum, helped decide which patients — out of a population of roughly 200 million a year — would be flagged for extra medical attention. To
A medical algorithm must not confuse cost with health Read More »
Malicious software (malware) causes much harm to our devices and life. We are eager to understand the malware behavior and the threat it made. Most of the record files of malware are variable length and text-based files with time stamps, such as event log data and dynamic analysis profiles. Using the time stamps, we can
Sequence Feature Extraction for Malware Family Analysis via Graph Neural Network Read More »
In this survey, we connect several lines of work from the pre-neural and neural era, by showing how hybrid approaches of words and characters as well as subword-based approaches based on learned segmentation have been proposed and evaluated. We conclude that there is and likely will never be a silver bullet singular solution for all
CNN Explainer tightly integrates a model overview that summarizes a CNN’s structure, and on-demand, dynamic visual explanation views that help users understand the underlying components of CNNs. Through smooth transitions across levels of abstraction, our tool enables users to inspect the interplay between low-level mathematical operations and high-level model structures.
CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization Read More »
In this presentation, the current issues to make federated learning flawlessly useful in the real world will be briefly overviewed. They are related to data/system heterogeneity, client management, traceability, and security. Also, we introduce the modularized federated learning framework, we currently develop, to experiment various techniques and protocols to find solutions for aforementioned issues. The
Federated Learning: Issues in Medical Application Read More »
