7.8k citations · 12.8k across the 52 of their papers we have counts for
55 papers
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…
ResidencyRL: Reinforcement Learning in Simulated Clinical Environments
Valentin Liévin, Samuel Schmidgall, Tim Strother +32
In medical education, physicians convert academic knowledge into clinical expertise through residency: years of training across thousands of encounters, with diverse sources of fee…
Gemma 4 Technical Report
Gemma Team, Sherif El Abd, Vaibhav Aggarwal +320
We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemm…
Efficiently Reconstructing Dynamic Scenes One D4RT at a Time
Chuhan Zhang, Guillaume Le Moing, Skanda Koppula +11
Understanding and reconstructing the complex geometry and motion of dynamic scenes from video remains a formidable challenge in computer vision. This paper introduces D4RT, a simpl…
SIMA 2: A Generalist Embodied Agent for Virtual Worlds
SIMA team, Adrian Bolton, Alexander Lerchner +63
We introduce SIMA 2, a generalist embodied agent that understands and acts in a wide variety of 3D virtual worlds. Built upon a Gemini foundation model, SIMA 2 represents a signifi…
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…
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…
RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation
Mel Vecerik, Carl Doersch, Yi Yang +6
For robots to be useful outside labs and specialized factories we need a way to teach them new useful behaviors quickly. Current approaches lack either the generality to onboard ne…
RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation
Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao +36
The ability to leverage heterogeneous robotic experience from different robots and tasks to quickly master novel skills and embodiments has the potential to transform robot learnin…
Barkour: Benchmarking Animal-level Agility with Quadruped Robots
Ken Caluwaerts, Atil Iscen, J. Chase Kew +41
Animals have evolved various agile locomotion strategies, such as sprinting, leaping, and jumping. There is a growing interest in developing legged robots that move like their biol…
Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning
Tuomas Haarnoja, Ben Moran, Guy Lever +25
We investigate whether Deep Reinforcement Learning (Deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be compo…
Hindering Adversarial Attacks with Implicit Neural Representations
Andrei A. Rusu, Dan A. Calian, Sven Gowal +1
We introduce the Lossy Implicit Network Activation Coding (LINAC) defence, an input transformation which successfully hinders several common adversarial attacks on CIFAR- class…
Probing Transfer in Deep Reinforcement Learning without Task Engineering
Andrei A. Rusu, Sebastian Flennerhag, Dushyant Rao +2
We evaluate the use of original game curricula supported by the Atari 2600 console as a heterogeneous transfer benchmark for deep reinforcement learning agents. Game designers crea…
MO2: Model-Based Offline Options
Sasha Salter, Markus Wulfmeier, Dhruva Tirumala +4
The ability to discover useful behaviours from past experience and transfer them to new tasks is considered a core component of natural embodied intelligence. Inspired by neuroscie…
The CLRS Algorithmic Reasoning Benchmark
Petar Veličković, Adrià Puigdomènech Badia, David Budden +5
Learning representations of algorithms is an emerging area of machine learning, seeking to bridge concepts from neural networks with classical algorithms. Several important works h…
A Generalist Agent
Scott Reed, Konrad Zolna, Emilio Parisotto +17
Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we…
Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors
Steven Bohez, Saran Tunyasuvunakool, Philemon Brakel +18
We investigate the use of prior knowledge of human and animal movement to learn reusable locomotion skills for real legged robots. Our approach builds upon previous work on imitati…
Few-Shot Keypoint Detection as Task Adaptation via Latent Embeddings
Mel Vecerik, Jackie Kay, Raia Hadsell +2
Dense object tracking, the ability to localize specific object points with pixel-level accuracy, is an important computer vision task with numerous downstream applications in robot…
Learning Transferable Motor Skills with Hierarchical Latent Mixture Policies
Dushyant Rao, Fereshteh Sadeghi, Leonard Hasenclever +8
For robots operating in the real world, it is desirable to learn reusable behaviours that can effectively be transferred and adapted to numerous tasks and scenarios. We propose an…
Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes
Alex X. Lee, Coline Devin, Yuxiang Zhou +18
We study the problem of robotic stacking with objects of complex geometry. We propose a challenging and diverse set of such objects that was carefully designed to require strategie…
Reasoning-Modulated Representations
Petar Veličković, Matko Bošnjak, Thomas Kipf +4
Neural networks leverage robust internal representations in order to generalise. Learning them is difficult, and often requires a large training set that covers the data distributi…
Tactile Sim-to-Real Policy Transfer via Real-to-Sim Image Translation
Alex Church, John Lloyd, Raia Hadsell +1
Simulation has recently become key for deep reinforcement learning to safely and efficiently acquire general and complex control policies from visual and proprioceptive inputs. Tac…
On Multi-objective Policy Optimization as a Tool for Reinforcement Learning: Case Studies in Offline RL and Finetuning
Abbas Abdolmaleki, Sandy H. Huang, Giulia Vezzani +11
Many advances that have improved the robustness and efficiency of deep reinforcement learning (RL) algorithms can, in one way or another, be understood as introducing additional ob…