Apple is sponsoring the International Conference on Learning Representations (ICLR), which will be held as a hybrid virtual and in person conference from May 1 - 5 in Kigali, Rwanda. ICLR brings together professionals dedicated to the advancement of deep learning. Below is the schedule of Apple sponsored workshops and events at ICLR 2023.


Monday, May 1

Tuesday, May 2

Friday, May 5

Virtual Presentations

Accepted Papers

Conference Accepted Papers

Continuous Pseudo-Labeling from the Start

Dan Berrebbi, Ronan Collobert, Samy Bengio, Navdeep Jaitly, Tatiana Likhomanenko

Diffusion Probabilistic Fields

Peiye Zhuang, Samira Abnar, Jiatao Gu, Alexander Schwing, Josh M. Susskind, Miguel Angel Bautista

FastFill: Efficient Compatible Model Update

Florian Jaeckle, Fartash Faghri, Ali Farhadi, Oncel Tuzel, Hadi Pouransari

f-DM: A Multi-stage Diffusion Model via Progressive Signal Transformation

Jiatao Gu, Shuangfei Zhai, Yizhe Zhang, Miguel Angel Bautista, Josh M. Susskind

MAST: Masked Augmentation Subspace Training for Generalizable Self-Supervised Priors

Chen Huang, Hanlin Goh, Jiatao Gu, Josh M. Susskind

RGI: Robust GAN-inversion for Mask-free Image Inpainting and Unsupervised Pixel-wise Anomaly Detection

Shancong Mou, Xiaoyi Gu, Meng Cao, Haoping Bai, Ping Huang, Jiulong Shan, Jianjun Shi


Samy Bengio is a senior area chair for ICLR 2023.

Audra McMillan, Chen Huang, Barry Theobald, Hilal Asi, Luca Zappella, Miguel Angel Bautista, Pierre Ablin, Pau Rodriguez, Rin Susa, Samira Abnar, Tatiana Likhomanenko, Vaishaal Shankar, Vimal Thilak are reviewers for ICLR 2023.

Jon Shlens and Marco Cuturi are area chairs for ICLR 2023.

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Related readings and updates.

International Conference on Machine Learning (ICML) 2023

Apple is sponsoring the International Conference on Machine Learning (ICML), which will take place in person from July 23 to 29 in Honolulu, Hawai'i. ICML is a conference dedicated to advancing artificial intelligence, with over 6,000 attendees. Below is the schedule of Apple-sponsored workshops and events at ICML 2023.

See event details

f-DM: A Multi-stage Diffusion Model via Progressive Signal Transformation

Diffusion models (DMs) have recently emerged as SoTA tools for generative modeling in various domains. Standard DMs can be viewed as an instantiation of hierarchical variational autoencoders (VAEs) where the latent variables are inferred from input-centered Gaussian distributions with fixed scales and variances. Unlike VAEs, this formulation constrains DMs from changing the latent spaces and learning abstract representations. In this work, we…
See paper details