163 citations · 165 across the 6 of their papers we have counts for
6 papers
LongVU-TTT: Causal Test-Time Training for Visual Resampling in Long Video Understanding
Mahmoud Ahmed, Sameh Abdulah, Olatunji Ruwase +3
Long-video MLLMs must model temporal change before a limited visual-token budget removes most frame evidence. We introduce LongVU-TTT, which inserts a convolutional Test-Time Train…
Cross-Layer Energy Analysis of Multimodal Training on Grace Hopper Superchips
Mahmoud Ahmed, Sameh Abdulah, Olatunji Ruwase +4
Multimodal deep learning models enable joint learning across heterogeneous data sources, including text, images, and video, but their rapid scaling introduces significant memory an…
The Bias is in the Details: An Assessment of Cognitive Bias in LLMs
R. Alexander Knipper, Charles S. Knipper, Kaiqi Zhang +3
As Large Language Models (LLMs) are increasingly embedded in real-world decision-making processes, it becomes crucial to examine the extent to which they exhibit cognitive biases.…
Training Ultra Long Context Language Model with Fully Pipelined Distributed Transformer
Jinghan Yao, Sam Ade Jacobs, Masahiro Tanaka +3
Large Language Models (LLMs) with long context capabilities are integral to complex tasks in natural language processing and computational biology, such as text generation and prot…
Universal Checkpointing: A Flexible and Efficient Distributed Checkpointing System for Large-Scale DNN Training with Reconfigurable Parallelis
Xinyu Lian, Sam Ade Jacobs, Lev Kurilenko +4
Deep neural network (DNN) training continues to scale rapidly in terms of model size, data volume, and sequence length, to the point where multiple machines are required to fit lar…
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
Marah Abdin, Jyoti Aneja, Hany Awadalla +126
We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal test…