activity
20162025
most citedGemini 1.5: Unlocking multimodal understanding across millions of tokens of context

297 citations · 519 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CL2025★ 49 cited

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Gheorghe Comanici, Eric Bieber, Mike Schaekermann +3431

In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our…

cs.CL2024★ 297 cited

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…

cs.LG2018★ 27 cited

Stochastic Negative Mining for Learning with Large Output Spaces

Sashank J. Reddi, Satyen Kale, Felix Yu +3

We consider the problem of retrieving the most relevant labels for a given input when the size of the output space is very large. Retrieval methods are modeled as set-valued classi…

stat.ML2018★ 40 cited

Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling

Shanshan Wu, Alexandros G. Dimakis, Sujay Sanghavi +5

Linear encoding of sparse vectors is widely popular, but is commonly data-independent -- missing any possible extra (but a priori unknown) structure beyond sparsity. In this paper…

stat.ML2017★ 1 cited

Lattice Rescoring Strategies for Long Short Term Memory Language Models in Speech Recognition

Shankar Kumar, Michael Nirschl, Daniel Holtmann-Rice +3

Recurrent neural network (RNN) language models (LMs) and Long Short Term Memory (LSTM) LMs, a variant of RNN LMs, have been shown to outperform traditional N-gram LMs on speech rec…

cs.CV2017★ 2 cited

What's In A Patch, I: Tensors, Differential Geometry and Statistical Shading Analysis

Daniel Niels Holtmann-Rice, Benjamin S. Kunsberg, Steven W. Zucker

We develop a linear algebraic framework for the shape-from-shading problem, because tensors arise when scalar (e.g. image) and vector (e.g. surface normal) fields are differentiate…