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pro vyhledávání: '"Dickens P"'
Given that AI systems are set to play a pivotal role in future decision-making processes, their trustworthiness and reliability are of critical concern. Due to their scale and complexity, modern AI systems resist direct interpretation, and alternativ
Externí odkaz:
http://arxiv.org/abs/2409.13919
A spring in parallel with an effort source (e.g., electric motor or human muscle) can reduce its energy consumption and effort (i.e., torque or force) depending on the spring stiffness, spring preload, and actuation task. However, selecting the sprin
Externí odkaz:
http://arxiv.org/abs/2409.08889
The $k$-Minimum Values (\kmv) data sketch algorithm stores the $k$ least hash keys generated by hashing the items in a dataset. We show that compression based on ordering the keys and encoding successive differences can offer $O(\log n)$ bits per key
Externí odkaz:
http://arxiv.org/abs/2409.02852
Autor:
Dickens, Charles, Pryor, Connor, Gao, Changyu, Albalak, Alon, Augustine, Eriq, Wang, William, Wright, Stephen, Getoor, Lise
The field of Neural-Symbolic (NeSy) systems is growing rapidly. Proposed approaches show great promise in achieving symbiotic unions of neural and symbolic methods. However, each NeSy system differs in fundamental ways. There is a pressing need for a
Externí odkaz:
http://arxiv.org/abs/2407.09693
Autor:
Tayal, Anuja, Di Eugenio, Barbara, Salunke, Devika, Boyd, Andrew D., Dickens, Carolyn A, Abril, Eulalia P, Garcia-Bedoya, Olga, Allen-Meares, Paula G
We propose a dialogue system that enables heart failure patients to inquire about salt content in foods and help them monitor and reduce salt intake. Addressing the lack of specific datasets for food-based salt content inquiries, we develop a templat
Externí odkaz:
http://arxiv.org/abs/2404.01182
Cooking recipes are one of the most readily available kinds of procedural text. They consist of natural language instructions that can be challenging to interpret. In this paper, we propose a model to identify relevant information from recipes and ge
Externí odkaz:
http://arxiv.org/abs/2401.12088
We leverage convex and bilevel optimization techniques to develop a general gradient-based parameter learning framework for neural-symbolic (NeSy) systems. We demonstrate our framework with NeuPSL, a state-of-the-art NeSy architecture. To achieve thi
Externí odkaz:
http://arxiv.org/abs/2401.09651
Understanding procedural texts, such as cooking recipes, is essential for enabling machines to follow instructions and reason about tasks, a key aspect of intelligent reasoning. In cooking, these instructions can be interpreted as a series of modific
Externí odkaz:
http://arxiv.org/abs/2401.06930
Autor:
Dickens, Charles, Huang, Eddie, Reganti, Aishwarya, Zhu, Jiong, Subbian, Karthik, Koutra, Danai
Graph summarization as a preprocessing step is an effective and complementary technique for scalable graph neural network (GNN) training. In this work, we propose the Coarsening Via Convolution Matching (CONVMATCH) algorithm and a highly scalable var
Externí odkaz:
http://arxiv.org/abs/2312.15520
Autor:
Dickens, Charlie, Bax, Eric
Data sketching has emerged as a key infrastructure for large-scale data analysis on streaming and distributed data. Merging sketches enables efficient estimation of cardinalities and frequency histograms over distributed data. However, merging sketch
Externí odkaz:
http://arxiv.org/abs/2312.08981