297 citations · 383 across the 5 of their papers we have counts for
5 papers
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.…
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Gemini Team, Petko Georgiev, Ving Ian Lei +1132
In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over…
Large-scale graph representation learning with very deep GNNs and self-supervision
Ravichandra Addanki, Peter W. Battaglia, David Budden +8
Effectively and efficiently deploying graph neural networks (GNNs) at scale remains one of the most challenging aspects of graph representation learning. Many powerful solutions ha…
Solving Mixed Integer Programs Using Neural Networks
Vinod Nair, Sergey Bartunov, Felix Gimeno +16
Mixed Integer Programming (MIP) solvers rely on an array of sophisticated heuristics developed with decades of research to solve large-scale MIP instances encountered in practice.…
Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learning
Ravichandra Addanki, Shaileshh Bojja Venkatakrishnan, Shreyan Gupta +2
We present Placeto, a reinforcement learning (RL) approach to efficiently find device placements for distributed neural network training. Unlike prior approaches that only find a d…