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Found 14 papers, 5 papers with code
Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes
As data collection is often significantly cheaper than labeling in this domain, the decision of which subset of examples to label can have a profound impact on model performance.
Adversarial Attacks On Multi-Agent Communication
Growing at a fast pace, modern autonomous systems will soon be deployed at scale, opening up the possibility for cooperative multi-agent systems.
Cost-Efficient Online Hyperparameter Optimization
Recent work on hyperparameters optimization (HPO) has shown the possibility of training certain hyperparameters together with regular parameters.
AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles
Importantly, by simulating directly from sensor data, we obtain adversarial scenarios that are safety-critical for the full autonomy stack.
Learning to Communicate and Correct Pose Errors
Learned communication makes multi-agent systems more effective by aggregating distributed information.
Policy Learning Using Weak Supervision
Most existing policy learning solutions require the learning agents to receive high-quality supervision signals such as well-designed rewards in reinforcement learning (RL) or high-quality expert demonstrations in behavioral cloning (BC).
BabyAI++: Towards Grounded-Language Learning beyond Memorization
Although recent works have shown the benefits of instructive texts in goal-conditioned RL, few have studied whether descriptive texts help agents to generalize across dynamic environments.
Towards A Unified Min-Max Framework for Adversarial Exploration and Robustness
The worst-case training principle that minimizes the maximal adversarial loss, also known as adversarial training (AT), has shown to be a state-of-the-art approach for enhancing adversarial robustness against norm-ball bounded input perturbations.
Adversarial Attack Generation Empowered by Min-Max Optimization
In this paper, we show how a general framework of min-max optimization over multiple domains can be leveraged to advance the design of different types of adversarial attacks.
One Bit Matters: Understanding Adversarial Examples as the Abuse of Redundancy
Despite the great success achieved in machine learning (ML), adversarial examples have caused concerns with regards to its trustworthiness: A small perturbation of an input results in an arbitrary failure of an otherwise seemingly well-trained ML model.
Reinforcement Learning with Perturbed Rewards
For instance, the state-of-the-art PPO algorithm is able to obtain 84. 6% and 80. 8% improvements on average score for five Atari games, with error rates as 10% and 30% respectively.
Multiple Character Embeddings for Chinese Word Segmentation
Chinese word segmentation (CWS) is often regarded as a character-based sequence labeling task in most current works which have achieved great success with the help of powerful neural networks.
The Helmholtz Method: Using Perceptual Compression to Reduce Machine Learning Complexity
This paper proposes a fundamental answer to a frequently asked question in multimedia computing and machine learning: Do artifacts from perceptual compression contribute to error in the machine learning process and if so, how much?
LiDAR-Video Driving Dataset: Learning Driving Policies Effectively
Learning autonomous-driving policies is one of the most challenging but promising tasks for computer vision.
