180 citations
- Massachusetts Institute of TechnologyUS38 papers
- University of BonnDE37 papers
- University of OxfordGB36 papers
- Centre National de la Recherche ScientifiqueFR35 papers
- Centro Brasileiro de Pesquisas FísicasBR35 papers
- École Polytechnique Fédérale de LausanneCH35 papers
- Lamarr Institute for Machine Learning and Artificial Intelligence35 papers
- Ruhr University BochumDE35 papers
- Sorbonne UniversitéFR35 papers
- University of BolognaIT35 papers
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- University of SienaIT35 papers
6 papers · 1 filter
MiniFool -- Physics-Constraint-Aware Minimizer-Based Adversarial Attacks in Deep Neural Networks
Lucie Flek, Oliver Janik, Philipp Alexander Jung +8
In this paper, we present a new algorithm, MiniFool, that implements physics-inspired adversarial attacks for testing neural network-based classification tasks in particle and astr…
Maximum Reward Formulation In Reinforcement Learning
Sai Krishna Gottipati, Yashaswi Pathak, Rohan Nuttall +6
Reinforcement learning (RL) algorithms typically deal with maximizing the expected cumulative return (discounted or undiscounted, finite or infinite horizon). However, several cruc…
Predictive Coding for Locally-Linear Control
Rui Shu, Tung Nguyen, Yinlam Chow +5
High-dimensional observations and unknown dynamics are major challenges when applying optimal control to many real-world decision making tasks. The Learning Controllable Embedding…
Improving the Generalization of Adversarial Training with Domain Adaptation
Chuanbiao Song, Kun He, Liwei Wang +1
By injecting adversarial examples into training data, adversarial training is promising for improving the robustness of deep learning models. However, most existing adversarial tra…
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
Kurtland Chua, Roberto Calandra, Rowan McAllister +1
Model-based reinforcement learning (RL) algorithms can attain excellent sample efficiency, but often lag behind the best model-free algorithms in terms of asymptotic performance. T…
Asymptotic Logical Uncertainty and The Benford Test
Scott Garrabrant, Siddharth Bhaskar, Abram Demski +3
We give an algorithm A which assigns probabilities to logical sentences. For any simple infinite sequence of sentences whose truth-values appear indistinguishable from a biased coi…