activity
20142026
most citedStarCraft II: A New Challenge for Reinforcement Learning

682 citations · 1.2k across the 15 of their papers we have counts for

collaborators

15 papers

cs.CL2026

CT: Counterfactual Causal Reasoning for Sentiment Shifts in Social-Media Conversation Trees

S M Rafiuddin, Atriya Sen

Sentiment in social-media threads does not only vary across posts; it shifts as users react to claims, corrections, evidence, and hostility within a branching reply tree. We study…

cs.CL2024

Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai

Parinthapat Pengpun, Can Udomcharoenchaikit, Weerayut Buaphet +1

We present a synthetic data approach for instruction-tuning large language models (LLMs) for low-resource languages in a data-efficient manner, specifically focusing on Thai. We id…

cs.CL2024★ 145 cited

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…

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.LG2023★ 5 cited

AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning

Michaël Mathieu, Sherjil Ozair, Srivatsan Srinivasan +21

StarCraft II is one of the most challenging simulated reinforcement learning environments; it is partially observable, stochastic, multi-agent, and mastering StarCraft II requires…

cs.LG2022★ 5 cited

Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback

Josh Abramson, Arun Ahuja, Federico Carnevale +16

An important goal in artificial intelligence is to create agents that can both interact naturally with humans and learn from their feedback. Here we demonstrate how to use reinforc…

cs.LG2022★ 3 cited

Intra-agent speech permits zero-shot task acquisition

Chen Yan, Federico Carnevale, Petko Georgiev +7

Human language learners are exposed to a trickle of informative, context-sensitive language, but a flood of raw sensory data. Through both social language use and internal processe…

cs.LG2022★ 2 cited

Evaluating Multimodal Interactive Agents

Josh Abramson, Arun Ahuja, Federico Carnevale +12

Creating agents that can interact naturally with humans is a common goal in artificial intelligence (AI) research. However, evaluating these interactions is challenging: collecting…

cs.LG2022★ 7 cited

A data-driven approach for learning to control computers

Peter C Humphreys, David Raposo, Toby Pohlen +8

It would be useful for machines to use computers as humans do so that they can aid us in everyday tasks. This is a setting in which there is also the potential to leverage large-sc…

cond-mat.stat-mech2021★ 24 cited

Dynamical crossover behavior in the relaxation of quenched quantum many-body systems

Aamir Ahmad Makki, Souvik Bandyopadhyay, Somnath Maity +1

A crossover between different power-law relaxation behaviors of many-body periodically driven integrable systems has come to light in recent years. We demonstrate using integrable…

cs.LG2021★ 43 cited

Imitating Interactive Intelligence

Josh Abramson, Arun Ahuja, Iain Barr +26

A common vision from science fiction is that robots will one day inhabit our physical spaces, sense the world as we do, assist our physical labours, and communicate with us through…

cs.LG2017★ 682 cited

StarCraft II: A New Challenge for Reinforcement Learning

Oriol Vinyals, Timo Ewalds, Sergey Bartunov +22

This paper introduces SC2LE (StarCraft II Learning Environment), a reinforcement learning environment based on the StarCraft II game. This domain poses a new grand challenge for re…

cs.SD2014★ 5 cited

DSP.Ear: Leveraging Co-Processor Support for Continuous Audio Sensing on Smartphones

Petko Georgiev, Nicholas D. Lane, Kiran K. Rachuri +1

The rapidly growing adoption of sensor-enabled smartphones has greatly fueled the proliferation of applications that use phone sensors to monitor user behavior. A central sensor am…

cs.SI2014

The Call of the Crowd: Event Participation in Location-based Social Services

Petko Georgiev, Anastasios Noulas, Cecilia Mascolo

Understanding the social and behavioral forces behind event participation is not only interesting from the viewpoint of social science, but also has important applications in the d…

cs.SI2014

Where Businesses Thrive: Predicting the Impact of the Olympic Games on Local Retailers through Location-based Services Data

Petko Georgiev, Anastasios Noulas, Cecilia Mascolo

The Olympic Games are an important sporting event with notable consequences for the general economic landscape of the host city. Traditional economic assessments focus on the aggre…