Artificial Intelligence in Society, Medicine, Security & Automotive
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LLM-Based Agents for Cybersecurity: A Systematic Review of Architectures, Applications, and Open Challenges
From penetration testing to incident response, this review maps 59 studies of language-model agents in cybersecurity, comparing architectures and applications while examining the evidence needed for reliable deployment...
Large language models detect methodological errors that human peer review misses in clinical trials
Fifteen language models detected deliberately inserted errors in clinical-trial reports, exceeding a historical human benchmark. The study examines how AI could help reviewers identify methodological weaknesses in published research...
Contemporary AI lacks the imagination to diverge or negate in science
Bold projections that artificial intelligence will accelerate scientific discovery have raced ahead of evidence from working scientists, and the field still lacks large-scale, scientist-in-the-loop tests of these claims. Here we...
An Embarrassingly Simple Graph Heuristic Reveals Shortcut-Solvable Benchmarks for Sequential Recommendation
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...
When silence is safer: a review and decision-theoretic framework for LLM abstention in healthcare
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...
Advancing regulatory variant effect prediction with AlphaGenome
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...
Sequence Feature Extraction for Malware Family Analysis via Graph Neural Network
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...
Auto Quantum Circuits
«AutoQML, self-assembling circuits, hyper-parameterized Quantum ML platform, using cirq, tensorflow and tfq. Trillions of possible qubit registries, gate combinations and moment sequences, ready to be adapted into your ML flow. Here I demonstrate climatechange...
Federated Learning: Issues in Medical Application
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...
CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization
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...
Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP
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...
Scientific Visualization: Python + Matplotlib
The Python scientific visualisation landscape is huge. It is composed of a myriad of tools, ranging from the most versatile and widely used down to the more specialised and confidential...
Introduction to Datascience: Learn Julia Programming, Math & Datascience from Scratch
I was emboldened to write this book after my video series called Data Science With Julia got some traction. That too after a tweet about Decision Tree was liked by...
Ethics-based auditing of automated decision-making systems: intervention points and policy implications
Organisations increasingly use automated decision-making systems (ADMS) to inform decisions that affect humans and their environment. While the use of ADMS can improve the accuracy and efficiency of decision-making processes...
AI and the Future of Skills, Volume 1
The OECD launched the Artificial Intelligence and the Future of Skills project to develop a programme that could assess the capabilities of AI and robotics and their impact on education...
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU. Both physics simulation and the neural network policy training reside...
Do Vision Transformers See Like Convolutional Neural Networks?
Convolutional neural networks (CNNs) have so far been the de-facto model for visual data. Recent work has shown that (Vision) Transformer models (ViT) can achieve comparable or even superior performance...
Human Learn
Machine learning covers a lot of ground but it is also capable of making bad decision. We've also reached a stage of hype that folks forget that many classification problems...
The Word is Mightier than the Label: Learning without Pointillistic Labels using Data Programming
We analyze the math fundamentals behind DP and demonstrate the power of it by applying it on two real-world text classification tasks. Furthermore, we compare DP with pointillistic active and...
Highly accurate protein structure prediction with AlphaFold
Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep...
Model-based Decision Making with Imagination for Autonomous Parking
Autonomous parking technology is a key concept within autonomous driving research. This paper will propose an imaginative autonomous parking algorithm to solve issues concerned with parking...
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
CARLA (Counterfactual And Recourse LibrAry), a python library for benchmarking counterfactual explanation methods across both different data sets and different machine learning models. In summary, our work provides the following...
A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning
This paper provides a succinct overview of this emerging theory of overparameterized ML (henceforth abbreviated as TOPML) that explains these recent findings through a statistical signal processing perspective. We emphasize...
How to avoid machine learning pitfalls: a guide for academic researchers
This document gives a concise outline of some of the common mistakes that occur when using machine learning techniques, and what can be done to avoid them. It is intended...
YOLOP: You Only Look Once for Panoptic Driving Perception
A panoptic driving perception system is an essential part of autonomous driving. A high-precision and real-time perception system can assist the vehicle in making the reasonable decision while driving. We...
When silence is safer: a review and decision-theoretic framework for LLM abstention in healthcare
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...
The Super Duper NLP Repo & The Big Bad NLP Database
A database housing more than 100 Colab notebooks running ML code for various NLP tasks. Colab is an excellent destination to experiment with the latest models as it comes with...
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