activity
20182026
most citedGemma 3 Technical Report

65 citations · 65 across the 4 of their papers we have counts for

collaborators

10 papers

cs.CV2026

FoodSense: A Multisensory Food Dataset and Benchmark for Predicting Taste, Smell, Texture, and Sound from Images

Sabab Ishraq, Aarushi Aarushi, Juncai Jiang +1

Humans routinely infer taste, smell, texture, and even sound from food images a phenomenon well studied in cognitive science. However, prior vision language research on food has fo…

cs.LG2025

Assay2Mol: large language model-based drug design using BioAssay context

Yifan Deng, Spencer S. Ericksen, Anthony Gitter

Scientific databases aggregate vast amounts of quantitative data alongside descriptive text. In biochemistry, molecule screening assays evaluate candidate molecules' functional res…

cs.AI2025

MedGemma Technical Report

Andrew Sellergren, Sahar Kazemzadeh, Tiam Jaroensri +78

Artificial intelligence (AI) has significant potential in healthcare applications, but its training and deployment faces challenges due to healthcare's diverse data, complex tasks,…

cs.CL202565 cited

Gemma 3 Technical Report

Gemma Team, Aishwarya Kamath, Johan Ferret +209

We introduce Gemma 3, a multimodal addition to the Gemma family of lightweight open models, ranging in scale from 1 to 27 billion parameters. This version introduces vision underst…

cs.LG2024

A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs

Ankit Singh Rawat, Veeranjaneyulu Sadhanala, Afshin Rostamizadeh +12

A primary challenge in large language model (LLM) development is their onerous pre-training cost. Typically, such pre-training involves optimizing a self-supervised objective (such…

cs.CV2024

VISTA: A Visual and Textual Attention Dataset for Interpreting Multimodal Models

Harshit, Tolga Tasdizen

The recent developments in deep learning led to the integration of natural language processing (NLP) with computer vision, resulting in powerful integrated Vision and Language Mode…