Search Results for author:
Found 9 papers, 5 papers with code
On the Safety Concerns of Deploying LLMs/VLMs in Robotics: Highlighting the Risks and Vulnerabilities
However, such integration can introduce significant vulnerabilities, in terms of their susceptibility to adversarial attacks due to the language models, potentially leading to catastrophic consequences.
On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces
Doing so incurs a persistent bias that appears in the attenuation rate of the expected policy gradient norm, which is inversely proportional to the radius of the action space.
Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases
To facilitate the development of KBQA models with stronger generalization, we construct and release a new large-scale, high-quality dataset with 64, 331 questions, GrailQA, and provide evaluation settings for all three levels of generalization.
Deep Multimodal Transfer-Learned Regression in Data-Poor Domains
In many real-world applications of deep learning, estimation of a target may rely on various types of input data modes, such as audio-video, image-text, etc.
TaxoGen: Unsupervised Topic Taxonomy Construction by Adaptive Term Embedding and Clustering
Our method, TaxoGen, uses term embeddings and hierarchical clustering to construct a topic taxonomy in a recursive fashion.
Databases
Mining Entity Synonyms with Efficient Neural Set Generation
Mining entity synonym sets (i. e., sets of terms referring to the same entity) is an important task for many entity-leveraging applications.
Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning
Given a text description, most existing semantic parsers synthesize a program in one shot.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
Learning flexible representations of stochastic processes on graphs
Graph convolutional networks adapt the architecture of convolutional neural networks to learn rich representations of data supported on arbitrary graphs by replacing the convolution operations of convolutional neural networks with graph-dependent linear operations.
