#General

r0:b621309bd8d28cda43682baaeb45707f

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 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 of modern generative recommenders? We conduct a benchmark audit using an intentionally simple graph heuristic: starting from only the last one or two interacted items, it retrieves candidates from a few-hop item-transition graph and ranks them with item-feature similarity. Surprisingly, despite its simplicity, this heuristic matches or outperforms a broad set of modern baselines on a variety of popular sequential recommendation benchmarks. For example, it achieves relative NDCG@10 improvements of 38.10% and 44.18% over the best competing baseline on the widely used Amazon Review Sports and CDs datasets, respectively.

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R0:c4c3ffc882452ee50fea21d2c6b1486a-CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization

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 across levels of abstraction, our tool enables users to inspect the interplay between low-level mathematical operations and high-level model structures.

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R0:884a5f6297216a3c5e883ae639b13721-Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP

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 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 applications and that thinking seriously about tokenization remains important for many applications

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R0:62a6aa3b4882ad9b194a4ae5c97b4d58-Ethics-based auditing of automated decision-making systems: intervention points and policy implications

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, it is also coupled with ethical challenges. Unfortunately, the governance mechanisms currently used to oversee human decision-making often fail when applied to ADMS.

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R0:5e6fade87218b43e4b8d96158080cc85-A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning

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 the unique aspects that define the TOPML research area as a subfield of modern ML theory and outline interesting open questions that remain.

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R0:97532435b0393f0a6ae72973cc68382e-How to avoid machine learning pitfalls: a guide for academic researchers

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 primarily as a guide for research students, and focuses on issues that are of particular concern within academic research, such as the need to do rigorous comparisons and reach valid conclusions. It covers five stages of the machine learning process: what to do before model building, how to reliably build models, how to robustly evaluate models, how to compare models fairly, and how to report results

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R0:8477de576deaf0ef41a00ad9e17c7171-Partial Differential Equations is All You Need for Generating Neural Architectures -- A Theory for Physical Artificial Intelligence Systems

Partial Differential Equations is All You Need for Generating Neural Architectures — A Theory for Physical Artificial Intelligence Systems

In this work, we generalize the reaction-diffusion equation in statistical physics, Schrödinger equation in quantum mechanics, Helmholtz equation in paraxial optics into the neural partial differential equations (NPDE), which can be considered as the fundamental equations in the field of artificial intelligence research

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Probabilistic Machine Learning: An Introduction

Probabilistic Machine Learning: An Introduction

«In this book, we will cover the most common types of ML, but from a probabilistic perspective. Roughly speaking, this means that we treat all unknown quantities (e.g., predictions about the future value of some quantity of interest, such as tomorrow’s temperature, or the parameters of some model) as random variables, that are endowed with probability distributions which describe a weighted set of possible values the variable may have.[…].».

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