401 citations · 510 across the 9 of their papers we have counts for
10 papers
Hand-Written PTX Tensor-Core GEMM Kernels: A Multi-Precision Study on NVIDIA L4
Matt J. Borowski, Blazej Osinski
High-performance Tensor Core kernels rely on a low-level PTX pipeline built from asynchronous data movement with cp.async, warp-level matrix loads with ldmatrix, and matrix multipl…
Shieldstral
Antonia Calvi, Avinash Sooriyarachchi, Giada Pistilli +273
We introduce Shieldstral, a 3B-parameter policy-adaptive multimodal safety classifier that matches or outperforms models nearly 7 its size on text safety benchmarks and set…
Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning
Dhruv Shah, Michael Equi, Blazej Osinski +3
Navigation in unfamiliar environments presents a major challenge for robots: while mapping and planning techniques can be used to build up a representation of the world, quickly di…
LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action
Dhruv Shah, Blazej Osinski, Brian Ichter +1
Goal-conditioned policies for robotic navigation can be trained on large, unannotated datasets, providing for good generalization to real-world settings. However, particularly in v…
What data do we need for training an AV motion planner?
Long Chen, Lukas Platinsky, Stefanie Speichert +6
We investigate what grade of sensor data is required for training an imitation-learning-based AV planner on human expert demonstration. Machine-learned planners are very hungry for…
SimNet: Learning Reactive Self-driving Simulations from Real-world Observations
Luca Bergamini, Yawei Ye, Oliver Scheel +6
In this work, we present a simple end-to-end trainable machine learning system capable of realistically simulating driving experiences. This can be used for the verification of sel…
CARLA Real Traffic Scenarios -- novel training ground and benchmark for autonomous driving
Błażej Osiński, Piotr Miłoś, Adam Jakubowski +6
This work introduces interactive traffic scenarios in the CARLA simulator, which are based on real-world traffic. We concentrate on tactical tasks lasting several seconds, which ar…
Simulation-based reinforcement learning for real-world autonomous driving
Błażej Osiński, Adam Jakubowski, Piotr Miłoś +4
We use reinforcement learning in simulation to obtain a driving system controlling a full-size real-world vehicle. The driving policy takes RGB images from a single camera and thei…
Model-Based Reinforcement Learning for Atari
Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos +11
Model-free reinforcement learning (RL) can be used to learn effective policies for complex tasks, such as Atari games, even from image observations. However, this typically require…
Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments
Łukasz Kidziński, Sharada Prasanna Mohanty, Carmichael Ong +26
In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle c…