Paul Liang

Reading list for research topics in multimodal machine learning

Collection of advice for prospective and current PhD students

[NeurIPS 2023, ICMI 2023] Quantifying & Modeling Multimodal Interactions

[NeurIPS 2021] Multiscale Benchmarks for Multimodal Representation Learning

[NeurIPS 2019 FL workshop] Federated Learning with Local and Global Representations

Holistic evaluation of multimodal foundation models

[TMLR 2022] High-Modality Multimodal Transformer

[AAAI 2018] Memory Fusion Network for Multi-view Sequential Learning

[ICLR 2023] MultiViz: Towards Visualizing and Understanding Multimodal Models

[NeurIPS 2023] Factorized Contrastive Learning: Going Beyond Multi-view Redundancy

[ICLR 2019] Learning Factorized Multimodal Representations

[ACL 2020] Towards Debiasing Sentence Representations

[ICML 2021] Towards Understanding and Mitigating Social Biases in Language Models

[AAMAS 2020] On Emergent Communication in Competitive Multi-Agent Teams

Course website for 11-777, Multimodal Machine Learning

[ICLR 2021] Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies

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Official style files for papers submitted to venues of the Association for Computational Linguistics

CMU MultimodalSDK provides tools to easily load well-known multimodal datasets such as CMU-MOSEI, CMU-MOSI, POM, and ICT-MMMO.

JMLR Cover Letter Template

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LaTeX style file for the Journal of Machine Learning Research

Beyond the Imitation Game collaborative benchmark for enormous language models

Course webpage for PGM, Spring 2019.