The fourteenth International Conference on Learning Representation (ICLR) is wrapping up today in Rio de Janeiro, closing out nearly a week of presentations, debates, and research showcases from leading AI scientists across academia and the tech industry, including Apple. Here’s what the company presented.
While ICLR may not be widely known to the general public, it has been regarded for more than a decade as one of the most renowned and prestigious conferences in machine learning.
This year, it ran from April 23 through 27 and occupied the four pavilions and the conference center of the Riocentro Convention Center in Rio de Janeiro. ICLR 2026 drew machine learning and artificial intelligence specialists and researchers (including AMI Labs’ Yann LeCun) from all over the world, from China to India to the United States and across Europe.
It also brought together major technology companies as sponsors and exhibitors, including Amazon, Tencent, Google, Microsoft, Ant Group, ByteDance, Huawei, Meta, Salesforce, Shopify, and Apple, as well as firms from Wall Street and the broader financial industry, such as Capital One, Jane Street, Citadel, and others.
As Apple announced a few days ago, the company had a booth at the event, where it showcased apple sharp 9to5mac, an impressive open-source model that turns 2D images into 3D spaces in just a few seconds, as well as LLM inference on MLX, Apple’s open-source framework for machine learning tasks running on Apple Silicon.
Apple’s booth also doubled as a recruiting hub, with iPads set up for attendees to scan QR codes and apply for machine learning roles on the spot. That was by no means exclusive to Apple, as most companies on the show floor were also using the event as a hiring pipeline for AI talent.
ICLR also featured gigantic poster areas, where researchers presented their work and answered questions about their studies. During the event, Apple showcased dozens of papers, all of which you can find here.
Apple also delivered presentations and workshops on some of the studies accepted for the conference. That included ParaRNN: Unlocking Parallel Training of Nonlinear RNNs for Large Language Models, presented by Federico Danieli, and Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts, presented by Vitaly Feldman.
To learn more about the studies Apple presented at ICLR 2026, follow this link.
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