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Found 16 papers, 2 papers with code
Dialogue with Robots: Proposals for Broadening Participation and Research in the SLIVAR Community
The ability to interact with machines using natural human language is becoming not just commonplace, but expected.
Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization
One of the challenges in constructing a KG from scientific literature is the extraction of ontology from unstructured text.
Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits!
Industry practitioners care about small improvements in malware detection accuracy because their models are deployed to hundreds of millions of machines, meaning a 0. 1\% change can cause an overwhelming number of false positives.
DDxT: Deep Generative Transformer Models for Differential Diagnosis
In the case of DDx, the proposed network has achieved a mean accuracy of 99. 82% and a mean F1 score of 0. 9472.
Measuring Equality in Machine Learning Security Defenses: A Case Study in Speech Recognition
Over the past decade, the machine learning security community has developed a myriad of defenses for evasion attacks.
Bridging the Gap: Using Deep Acoustic Representations to Learn Grounded Language from Percepts and Raw Speech
Learning to understand grounded language, which connects natural language to percepts, is a critical research area.
Adversarial Transfer Attacks With Unknown Data and Class Overlap
In this work we note that as studied, current transfer attack research has an unrealistic advantage for the attacker: the attacker has the exact same training data as the victim.
Neural Variational Learning for Grounded Language Acquisition
We propose a learning system in which language is grounded in visual percepts without specific pre-defined categories of terms.
Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery
The use of Machine Learning has become a significant part of malware detection efforts due to the influx of new malware, an ever changing threat landscape, and the ability of Machine Learning methods to discover meaningful distinctions between malicious and benign software.
Sampling Approach Matters: Active Learning for Robotic Language Acquisition
Ordering the selection of training data using active learning can lead to improvements in learning efficiently from smaller corpora.
Practical Cross-modal Manifold Alignment for Grounded Language
We propose a cross-modality manifold alignment procedure that leverages triplet loss to jointly learn consistent, multi-modal embeddings of language-based concepts of real-world items.
Presentation and Analysis of a Multimodal Dataset for Grounded Language Learning
Grounded language acquisition -- learning how language-based interactions refer to the world around them -- is amajor area of research in robotics, NLP, and HCI.
Planning with Abstract Learned Models While Learning Transferable Subtasks
We introduce an algorithm for model-based hierarchical reinforcement learning to acquire self-contained transition and reward models suitable for probabilistic planning at multiple levels of abstraction.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
?`Es un pl\'atano? Exploring the Application of a Physically Grounded Language Acquisition System to Spanish
In this paper we describe a multilingual grounded language learning system adapted from an English-only system.
A Joint Model of Language and Perception for Grounded Attribute Learning
As robots become more ubiquitous and capable, it becomes ever more important to enable untrained users to easily interact with them.
