Apple is sponsoring the International Conference on Learning Representations (ICLR), which is taking place in person from May 7 to 11 in Vienna Austria. ICLR brings together professionals dedicated to the advancement of deep learning.
Schedule
Below is the schedule of Apple sponsored workshops and events at ICLR 2024. Stop by the Apple booth from May 7 to 9 from 9:00am – 5:00pm CEST and May 10 from 9:00am – 4:00pm CEST at Halle/Hall A, Booth #3.
Tuesday, May 7
- Efficient ConvBN Blocks for Transfer Learning and Beyond
- 10:45am – 12:45pm CEST, Halle/Hall B, #176
- Kaichao You (Tsinghua University), Qin Guo (Tsinghua University), Anchang Bao (Tsinghua University), Meng Cao, Ping Huang, Jiulong Shan, Mingsheng Long (Tsinghua University)
- Poly-View Contrastive Learning
- 4:30pm – 6:30pm CEST, Halle/Hall B, #284
- Amitis Shidani (Oxford University), Dan Busbridge, Devon Hjelm, Jason Ramapuram, Eeshan Gunesh Dhekane, Russ Webb
Wednesday, May 8
- Women in Machine Learning
- 12:00pm – 2:00pm CEST, Halle/Hall B 6
- Dhruti Shah and Maria Cervera will be participating in the WIML social.
- FERRET: Refer and Ground Anything Anywhere at Any Granularity
- 4:30pm – 6:30pm CEST, Halle/Hall B, #81
- Haoxuan You, Haotian (AIML) Zhang, Liangliang Cao, Zhe Gan, Bowen Zhang, Zirui Wang, Xianzhi Du, Shih-Fu Chang (Columbia University), Yinfei Yang
Thursday, May 9
- Generative Modeling with Phase Stochastic Bridge
- 10:30am – 10:45am CEST, Halle/Hall A 8-9
- Tianrong Chen (Georgia Institute of Technology), Jiatao Gu, Josh Susskind, Shuangfei Zhai, Laurent Dinh, Evangelos Theodorou (Georgia Tech)
- Generative Modeling with Phase Stochastic Bridge
- 10:45am – 12:45am CEST, Halle/Hall B, #77
- Tianrong Chen (Georgia Institute of Technology), Jiatao Gu, Josh Susskind, Shuangfei Zhai, Laurent Dinh, Evangelos Theodorou (Georgia Tech)
- MOFI: Learning Image Representation from Noisy Entity Annotated Images
- 10:45am – 12:45am CEST, Halle/Hall B, #190
- Wentao Wu, Aleksei Timofeev, Chen (SII) Chen, Bowen Zhang, Kun Duan, Shuangning Liu, Yantao Zheng, Jonathon Shlens (Google (contributions while at Apple)), Xianzhi Du, Zhe Gan, Yinfei Yang
- Manifold Diffusion Fields
- 4:30pm – 6:30pm CEST, Halle/Hall B, #38
- Ahmed Elhag (AIMS Senegal), Yuyang Wang, Josh Susskind, Miguel Angel Bautista Martin
- Matryoshka Diffusion Models
- 4:30pm – 6:30pm CEST, Halle/Hall B, #246
- Jiatao Gu, Shuangfei Zhai, Yizhe Zhang, Josh Susskind, Navdeep Jaitly
Friday, May 10
- Large Language Models for Generalizable Reinforcement Learning of Embodied Tasks
- 10:45am – 12:45am CEST, Halle/Hall B, #264
- Andrew Szot (Georgia Institute of Technology), Max Schwarzer (Université de Montréal), Harsh Agrawal, Bogdan Mazoure, Rin Metcalf Susa, Natalie Mackraz, Walter Talbott, Devon Hjelm, Alexander Toshev
- TiC-CLIP: Continual Training of CLIP Models
- 4:30pm – 6:30pm CEST, Halle/Hall B, #139
- Saurabh Garg (Carnegie Mellon University), Hadi Pour Ansari, Mehrdad Farajtabar, Sachin Mehta, Raviteja Vemulapalli, Oncel Tuzel, Vaishaal Shankar, Fartash Faghri
Saturday, May 11
Accepted Papers
Compressing LLMs: The Truth is Rarely Pure and Never Simple
Ajay Jaiswal (The University of Texas at Austin), Zhe Gan, Xianzhi Du, Bowen Zhang, Zhangyang Wang (The University of Texas at Austin), Yinfei Yang
Data Filtering Functions: Algorithmic Curation of Billion Scale Datasets
Alex Fang (University of Washington), Albin Madappally Jose, Amit Jain, Ludwig Schmidt (University of Washington), Alexander Toshev, Vaishaal Shankar
Efficient ConvBN Blocks for Transfer Learning and Beyond
Kaichao You (Tsinghua University), Qin Guo (Tsinghua University), Anchang Bao (Tsinghua University), Meng Cao, Ping Huang, Jiulong Shan, Mingsheng Long (Tsinghua University)
Efficient-3Dim: Learning a Generalizable Single Image Novel View Synthesizer in One Day
Yifan Jiang, Hao Tang, Rick Chang, Liangchen Song, Zhangyang Wang (University of Texas at Austin), Liangliang Cao
FedHyper: Adaptive Step Sizes for Efficient Federated Learning with Hypergradient Descent
Ziyao Wang (University of Maryland College Park), Jianyu Wang, Ang Li (University of Maryland College Park)
FERRET: Refer and Ground Anything Anywhere at Any Granularity
Haoxuan You, Haotian (AIML) Zhang, Liangliang Cao, Zhe Gan, Bowen Zhang, Zirui Wang, Xianzhi Du, Shih-Fu Chang (Columbia University), Yinfei Yang
Generative Modeling with Phase Stochastic Bridge
Tianrong Chen (Georgia Tech), Jiatao Gu, Josh Susskind, Shuangfei Zhai, Laurent Dinh, Evangelos Theodorou (Georgia Tech)
Guiding Instruction-based Image Editing via Multimodal Large Language Models
Tsu-Jui Fu (University of California, Santa Barbara), Wenze Hu, Xianzhi Du, William Wang (University of California, Santa Barbara), Yinfei Yang, Zhe Gan
Hindsight PRIORs for Reward Learning from Human Preferences
Mudit Verma (Apple Intern / Arizona State University), Rin Metcalf Susa
JointNet: Extending Text-to-Image Diffusion for Dense Distribution Modeling
Jingyang Zhang, Shiwei Li, Yuanxun Lu (Nanjing University), Tian Fang, David McKinnon, Yanghai Tsin, Long Quan (The University of Science and Technology), Yao Yao (Nanjing University)
Large Language Models for Generalizable Reinforcement Learning of Embodied Tasks
Andrew Szot (Georgia Institute of Technology), Max Schwarzer (Université de Montréal), Harsh Agrawal, Bogdan Mazoure, Rin Metcalf Susa, Natalie Mackraz, Walter Talbott, Devon Hjelm, Alexander Toshev
Large-scale Training of Foundation Models for Wearable Biosignals
Salar Abbaspourazad, Oussama Elachqar, Andy Miller, Saba Emrani, Udhay Nallasamy, Ian Shapiro
LiDAR: Sensing Linear Probing Performance in Joint Embedding SSL Architectures
Vimal Thilak, Omid Saremi, Preetum Nakkiran, Josh Susskind, Chen Huang, Hanlin Goh, Laurent Dinh, Etai Littwin
Ahmed Elhag (AIMS Senegal), Yuyang Wang, Josh Susskind, Miguel Angel Bautista Martin
Jiatao Gu, Shuangfei Zhai, Yizhe Zhang, Josh Susskind, Navdeep Jaitly
MOFI: Learning Image Representation from Noisy Entity Annotated Images
Wentao Wu, Aleksei Timofeev, Chen (SII) Chen, Bowen Zhang, Kun Duan, Shuangning Liu, Yantao Zheng, Jonathon Shlens (Google (contributions while at Apple)), Xianzhi Du, Zhe Gan, Yinfei Yang
Overcoming the Pitfalls of Vision-Language Model Finetuning for OOD Generalization
Yuhang Zang (Nanyang Technological University), Hanlin Goh, Josh Susskind, Chen Huang
Poly-View Contrastive Learning
Amitis Shidani (Oxford University), Dan Busbridge, Devon Hjelm, Jason Ramapuram, Eeshan Gunesh Dhekane, Russ Webb
ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models
Iman Mirzadeh, Keivan Alizadeh Vahid, Sachin Mehta, Carlo C Del Mundo, Oncel Tuzel, Golnoosh Samei, Mohammad Rastegari, Mehrdad Farajtabar
TiC-CLIP: Continual Training of CLIP Models
Saurabh Garg (Carnegie Mellon University), Hadi Pour Ansari, Mehrdad Farajtabar, Sachin Mehta, Raviteja Vemulapalli, Oncel Tuzel, Vaishaal Shankar, Fartash Faghri
Vanishing gradients in reinforcement learning based fine-tuning of language models
Noam Razin (Tel Aviv University), Hattie Zhou (Université de Montréal), Preetum Nakkiran, Josh Susskind, Omid Saremi, Arwen Bradley, Vimal Thilak, Etai Littwin
What Algorithms can Transformers Learn? A Study in Length Generalization
Hattie Zhou (Université de Montréal), Omid Saremi, Etai Littwin, Arwen Bradley, Noam Razin (Tel Aviv University), Josh Susskind, Samy Bengio, Preetum Nakkiran
Conformal Prediction via Regression-as-Classification
Etash Guha (Riken AIP), Shlok Natarajan (Salesforce), Thomas Mollenhoff (Riken AIP), Emtiyaz Khan (Riken AIP), Eugene Ndiaye
How to compute efficiently Hessian-vector products?
Mathieu Dagreou (Inria), Thomas Moreau (Inria), Samuel Vaiter (CNRS), Pierre Ablin
Only Pay for What is Uncertain: Variance-Adaptive Thompson Sampling
Aadi Saha, Branislav Kveton (Amazon)
Pseudo-Generalized Dynamic View Synthesis from a Video
Xiaoming Zhao (UIUC), Fangchang Ma, Josh Susskind, Miguel Angel Bautista Martin, Alex Colburn, Alex Schwing
When Can Transformers Reason With Abstract Symbols?
Enric Boix (MIT), Josh Susskind, Omid Saremi, Emmanuel Abbe, Etai Littwin, Samy Bengio
Workshop Accepted Papers
Frequency-Aware Masked Autoencoders for Multimodal Pretraining on Biosignals
Ran Liu (Georgia Institute of Technology), Ellen Zippi, Hadi Pour Ansari, Chris Sandino, Jingping Nie (Columbia University), Hanlin Goh, Erdrin Azemi, Ali Moin
How Far Are We from Intelligent Visual Deductive Reasoning?
Yizhe Zhang, Richard Bai, Ruixiang Zhang, Jiatao Gu, Shuangfei Zhai, Josh Susskind, Navdeep Jaitly
Large-scale Training of Foundation Models for Wearable Biosignals
Salar Abbaspourazad, Oussama Elachqar, Andy Miller, Saba Emrani, Udhay Nallasamy, Ian Shapiro
Rephrase not Repeat: Beating Scaling Laws in Data Constrained Language Modeling
Pratyush Maini (CMU), Skyler Seto, David Grangier, Richard Bai, Yizhe Zhang, Navdeep Jaitly
Acknowledgements
Samy Bengio is a member of the ICLR 2024 Organizing Committee.
Samy Bengio, Miguel Angel Bautista Martin, Eugene Ndiaye and Yizhe Zhang are ICLR 2024 area chairs.
Fartash Faghri, Enrico Fini, Devon Hjelm, Bogdan Mazoure, Wenze Hu, Rin Metcalf Susa, Vimal Thilak and Luca Zappella are reviewers for ICLR 2024.