Publications (10)
ARMOR: High-Performance Semi-Structured Pruning via Adaptive Matrix Factorization
Lawrence Liu, Alexander Liu, Mengdi Wang +2
Large language models (LLMs) present significant deployment challenges due to their immense computational and memory requirements. While semi-structured pruning, particularly 2:4 s…
Monarch: Expressive Structured Matrices for Efficient and Accurate Training
Tri Dao, Beidi Chen, Nimit Sohoni +7
Large neural networks excel in many domains, but they are expensive to train and fine-tune. A popular approach to reduce their compute or memory requirements is to replace dense we…
Bridging Creative Intent and Visual Quality: Creator-Driven Recurrent Video Generation with Agentic Feedback Loops
Denis Savytski, Aiden Lei, Heding Liu +4
Generative AI has made content creation increasingly accessible, but many AI-generated videos lack narrative coherence and creative direction, issues that become more substantial a…
REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets
Angelina Wang, Alexander Liu, Ryan Zhang +6
Machine learning models are known to perpetuate and even amplify the biases present in the data. However, these data biases frequently do not become apparent until after the models…
Masked Autoencoders As The Unified Learners For Pre-Trained Sentence Representation
Alexander Liu, Samuel Yang
Despite the progresses on pre-trained language models, there is a lack of unified frameworks for pre-trained sentence representation. As such, it calls for different pre-training m…
Spoken Moments: Learning Joint Audio-Visual Representations from Video Descriptions
Mathew Monfort, SouYoung Jin, Alexander Liu +4
When people observe events, they are able to abstract key information and build concise summaries of what is happening. These summaries include contextual and semantic information…