The 14th International Conference on Learning Representations (ICLR) concludes today in Rio de Janeiro, wrapping up nearly a week of presentations, discussions, and research shows by leading AI scientists from academia and the technology industry, including Apple. Here’s what the company presented.
Apple presents dozens of studies at ICLR 2026
ICLR may not be well known to the general public, but it has been considered one of the most prestigious and prestigious conferences in machine learning for over a decade.
This year it was held from April 23rd to 27th, occupying four pavilions and a conference center at the Rio Centro Convention Center in Rio de Janeiro. ICLR 2026 brought together machine learning and artificial intelligence experts and researchers from around the world, from China to India, the United States and Europe, including Yann LeCun from the AMI Institute.
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 Wall Street and broader financial industry companies such as Capital One, Jane Street, and Citadel.
As Apple announced a few days ago, the company had a booth at the event showcasing the Apple Sharp 9to5mac, an impressive open-source model that converts 2D images into 3D space in just seconds, and LLM inference in MLX, Apple’s open-source framework for machine learning tasks running on Apple Silicon.
Apple’s booth also served as a recruiting hub, with iPads set up so attendees could scan QR codes and apply for machine learning roles on the spot. This was by no means unique to Apple; most companies on the show floor also used the event as a recruitment pipeline for AI talent.
ICLR also had a huge poster area where researchers presented their research and answered questions about their research. During the event, Apple displayed dozens of papers. All of which can be found here.
Apple also conducted presentations and workshops on some of the research accepted at the conference. These include “ParaRNN: Unlocking Parallel Training of Nonlinear RNNs for Large Language Models” by Federico Danieli and “Cram Less to Fit More: Training Data Pruning Improvements Memorization of Facts” by Kunal Talwar.
Click this link to learn more about the research Apple presented at ICLR 2026.
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