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Found 11 papers, 2 papers with code
Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence
We envision a pipeline to utilize large neuroimaging datasets, including maps of the brain which capture neuron and synapse connectivity, to improve machine learning approaches.
A Risk-Sensitive Approach to Policy Optimization
Without cost constraints, we find that pessimistic risk profiles can be used to reduce cost while improving total reward accumulation.
A Risk-Sensitive Policy Gradient Method
Standard deep reinforcement learning (DRL) agents aim to maximize expected reward, considering collected experiences equally in formulating a policy.
Learning a Group-Aware Policy for Robot Navigation
Human-aware robot navigation promises a range of applications in which mobile robots bring versatile assistance to people in common human environments.
Jacks of All Trades, Masters Of None: Addressing Distributional Shift and Obtrusiveness via Transparent Patch Attacks
We focus on the development of effective adversarial patch attacks and -- for the first time -- jointly address the antagonistic objectives of attack success and obtrusiveness via the design of novel semi-transparent patches.
Unsupervised Discovery, Control, and Disentanglement of Semantic Attributes with Applications to Anomaly Detection
Our work focuses on unsupervised and generative methods that address the following goals: (a) learning unsupervised generative representations that discover latent factors controlling image semantic attributes, (b) studying how this ability to control attributes formally relates to the issue of latent factor disentanglement, clarifying related but dissimilar concepts that had been confounded in the past, and (c) developing anomaly detection methods that leverage representations learned in (a).
TanksWorld: A Multi-Agent Environment for AI Safety Research
In this work, we introduce the AI safety TanksWorld as an environment for AI safety research with three essential aspects: competing performance objectives, human-machine teaming, and multi-agent competition.
Adversarial Examples in Remote Sensing
This paper considers attacks against machine learning algorithms used in remote sensing applications, a domain that presents a suite of challenges that are not fully addressed by current research focused on natural image data such as ImageNet.
Combining Deep Universal Features, Semantic Attributes, and Hierarchical Classification for Zero-Shot Learning
We address zero-shot (ZS) learning, building upon prior work in hierarchical classification by combining it with approaches based on semantic attribute estimation.
Automatic Dimension Selection for a Non-negative Factorization Approach to Clustering Multiple Random Graphs
We consider a problem of grouping multiple graphs into several clusters using singular value thesholding and non-negative factorization.
Techniques for clustering interaction data as a collection of graphs
A natural approach to analyze interaction data of form "what-connects-to-what-when" is to create a time-series (or rather a sequence) of graphs through temporal discretization (bandwidth selection) and spatial discretization (vertex contraction).
