4 citations · 6 across the 12 of their papers we have counts for
12 papers
FlashRT: Agent Harness for Guiding Agents to Deploy Real-Time Multimodal Applications
Krish Agarwal, Zhuoming Chen, Yanyuan Qin +3
Real-time multimodal applications, including voice agents and interactive video generation, compose heterogeneous models into pipelines whose efficient deployment requires applicat…
MLPs are Hebbians: Constructing Efficient Fact-Storing MLPs for Transformers
Roberto Garcia, Jerry Liu, Ronny Junkins +3
Large language models (LLMs) store factual knowledge in their parameters. While recent work has shown that this knowledge resides in MLP layers, existing constructive and mechanist…
MonarchRT: Efficient Attention for Real-Time Video Generation
Krish Agarwal, Zhuoming Chen, Cheng Luo +5
Real-time video generation with Diffusion Transformers is bottlenecked by the quadratic cost of 3D self-attention, especially in real-time regimes that are both few-step and autore…
Constructing Efficient Fact-Storing MLPs for Transformers
Owen Dugan, Roberto Garcia, Ronny Junkins +5
The success of large language models (LLMs) can be attributed in part to their ability to efficiently store factual knowledge as key-value mappings within their MLP parameters. Rec…
Operationalizing Justice: Towards the Development of a Principle Based Design Framework for Human Services AI
Maria Y. Rodriguez, Seventy Hall, Pranav Sankhe +4
Scholars investigating ethical AI, especially in high stakes settings like child welfare, have arguably been seeking ways to embed notions of justice into the design of these criti…
Identifying Subgroup and Context Effects in Conjoint Experiments
Steven Wang, Isys Johnson, Jessica Grogan +4
Conjoint experiments have become central to survey research in political science and related fields because they allow researchers to study preferences across multiple attributes s…
BWLer: Barycentric Weight Layer Elucidates a Precision-Conditioning Tradeoff for PINNs
Jerry Liu, Yasa Baig, Denise Hui Jean Lee +3
Physics-informed neural networks (PINNs) offer a flexible way to solve partial differential equations (PDEs) with machine learning, yet they still fall well short of the machine-pr…
Cartridges: Lightweight and general-purpose long context representations via self-study
Sabri Eyuboglu, Ryan Ehrlich, Simran Arora +8
Large language models are often used to answer queries grounded in large text corpora (e.g. codebases, legal documents, or chat histories) by placing the entire corpus in the conte…
Towards Learning High-Precision Least Squares Algorithms with Sequence Models
Jerry Liu, Jessica Grogan, Owen Dugan +4
This paper investigates whether sequence models can learn to perform numerical algorithms, e.g. gradient descent, on the fundamental problem of least squares. Our goal is to inheri…
Just read twice: closing the recall gap for recurrent language models
Simran Arora, Aman Timalsina, Aaryan Singhal +6
Recurrent large language models that compete with Transformers in language modeling perplexity are emerging at a rapid rate (e.g., Mamba, RWKV). Excitingly, these architectures use…
Simple linear attention language models balance the recall-throughput tradeoff
Simran Arora, Sabri Eyuboglu, Michael Zhang +6
Recent work has shown that attention-based language models excel at recall, the ability to ground generations in tokens previously seen in context. However, the efficiency of atten…
Zoology: Measuring and Improving Recall in Efficient Language Models
Simran Arora, Sabri Eyuboglu, Aman Timalsina +5
Attention-free language models that combine gating and convolutions are growing in popularity due to their efficiency and increasingly competitive performance. To better understand…