23.6k citations
- Carnegie Mellon UniversityUS49 papers
- Google (United States)US37 papers
- California Institute of TechnologyUS33 papers
- Amazon (Germany)DE32 papers
- University of Illinois Urbana-ChampaignUS22 papers
- Microsoft (United States)US20 papers
- Stanford UniversityUS20 papers
- The University of Texas at AustinUS20 papers
- University of California, Los AngelesUS20 papers
- University of California, Santa BarbaraUS19 papers
- University of WashingtonUS19 papers
- Georgia Institute of TechnologyUS17 papers
37 papers · 1 filter
LLMCache: Layer-Wise Caching Strategies for Accelerated Reuse in Transformer Inference
Harsh Vardhan Bansal
Transformer-based language models have achieved remarkable performance across a wide range of tasks, yet their high inference latency poses a significant challenge for real-timeand…
Zero-Shot Cross-Lingual Transfer using Prefix-Based Adaptation
Snegha A, Sayambhu Sen, Piyush Singh Pasi +2
With the release of new large language models (LLMs) like Llama and Mistral, zero-shot cross-lingual transfer has become increasingly feasible due to their multilingual pretraining…
Explicit Memory through Online 3D Gaussian Splatting Improves Class-Agnostic Video Segmentation
Anthony Opipari, Aravindhan K Krishnan, Shreekant Gayaka +4
Remembering where object segments were predicted in the past is useful for improving the accuracy and consistency of class-agnostic video segmentation algorithms. Existing video se…
Approximate maximum likelihood decoding with minimum weight matchings
Mao Lin
The minimum weight matching (MWM) and maximum likelihood decoding (MLD) are two widely used and distinct decoding strategies for quantum error correction. For a given syndrome, the…
An Expert-grounded benchmark of General Purpose LLMs in LCA
Artur Donaldson, Bharathan Balaji, Cajetan Oriekezie +2
Purpose: Artificial intelligence (AI), and in particular large language models (LLMs), are increasingly being explored as tools to support life cycle assessment (LCA). While demons…
DCP: Addressing Input Dynamism In Long-Context Training via Dynamic Context Parallelism
Chenyu Jiang, Zhenkun Cai, Ye Tian +3
Context parallelism has emerged as a key technique to support long-context training, a growing trend in generative AI for modern large models. However, existing context parallel me…