238 citations · 700 across the 14 of their papers we have counts for
14 papers
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…
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…
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…
Encoder-Decoder Gemma: Improving the Quality-Efficiency Trade-Off via Adaptation
Biao Zhang, Fedor Moiseev, Joshua Ainslie +7
While decoder-only large language models (LLMs) have shown impressive results, encoder-decoder models are still widely adopted in real-world applications for their inference effici…
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…
Gemma 2: Improving Open Language Models at a Practical Size
Gemma Team, Morgane Riviere, Shreya Pathak +195
In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In th…
Gemma: Open Models Based on Gemini Research and Technology
Gemma Team, Thomas Mesnard, Cassidy Hardin +105
This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models. Gemma models demonstrate stro…
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models
Aleksandar Botev, Soham De, Samuel L Smith +59
We introduce RecurrentGemma, a family of open language models which uses Google's novel Griffin architecture. Griffin combines linear recurrences with local attention to achieve ex…
Skill Rating for Generative Models
Catherine Olsson, Surya Bhupatiraju, Tom Brown +2
We explore a new way to evaluate generative models using insights from evaluation of competitive games between human players. We show experimentally that tournaments between genera…
Towards Mixed Optimization for Reinforcement Learning with Program Synthesis
Surya Bhupatiraju, Kumar Krishna Agrawal, Rishabh Singh
Deep reinforcement learning has led to several recent breakthroughs, though the learned policies are often based on black-box neural networks. This makes them difficult to interpre…
The Mirage of Action-Dependent Baselines in Reinforcement Learning
George Tucker, Surya Bhupatiraju, Shixiang Gu +3
Policy gradient methods are a widely used class of model-free reinforcement learning algorithms where a state-dependent baseline is used to reduce gradient estimator variance. Seve…
Deep API Programmer: Learning to Program with APIs
Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed +1
We present DAPIP, a Programming-By-Example system that learns to program with APIs to perform data transformation tasks. We design a domain-specific language (DSL) that allows for…
RobustFill: Neural Program Learning under Noisy I/O
Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju +3
The problem of automatically generating a computer program from some specification has been studied since the early days of AI. Recently, two competing approaches for automatic pro…
Lower central series of a free associative algebra over the integers and finite fields
Surya Bhupatiraju, Pavel Etingof, David Jordan +2
Consider the free algebra A_n generated over Q by n generators x_1, ..., x_n. Interesting objects attached to A = A_n are members of its lower central series, L_i = L_i(A), defined…