Paul Liang

awesome-multimodal-ml. Reading list for research topics in multimodal machine learning

awesome-phd-advice. Collection of advice for prospective and current PhD students

MultiBench. [NeurIPS 2021] Multiscale Benchmarks for Multimodal Representation Learning

LG-FedAvg. [NeurIPS 2019 FL workshop] Federated Learning with Local and Global Representations

HighMMT. [TMLR 2022] High-Modality Multimodal Transformer

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

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

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

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

factorized. [ICLR 2019] Learning Factorized Multimodal Representations

sent_debias. [ACL 2020] Towards Debiasing Sentence Representations

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

HEMM. Holistic evaluation of multimodal foundation models

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

mmml-course. Course website for 11-777, Multimodal Machine Learning

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

workspace.

cmu-multibench.github.io. HTML

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

lm-social-bias.

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

jmlr-coverletter. JMLR Cover Letter Template