Zobrazeno 1 - 10
of 79
pro vyhledávání: '"Huang, H. Howie"'
Autor:
Kim, Yejin, Rome, Scott, Foley, Kevin, Nankani, Mayur, Melamed, Rimon, Morales, Javier, Yadav, Abhay, Peifer, Maria, Hamidian, Sardar, Huang, H. Howie
Addressing the challenges related to data sparsity, cold-start problems, and diversity in recommendation systems is both crucial and demanding. Many current solutions leverage knowledge graphs to tackle these issues by combining both item-based and u
Externí odkaz:
http://arxiv.org/abs/2403.18667
Achieving high performance for Sparse MatrixMatrix Multiplication (SpMM) has received increasing research attention, especially on multi-core CPUs, due to the large input data size in applications such as graph neural networks (GNNs). Most existing s
Externí odkaz:
http://arxiv.org/abs/2312.05639
Autor:
Melamed, Rimon, McCabe, Lucas H., Wakhare, Tanay, Kim, Yejin, Huang, H. Howie, Boix-Adsera, Enric
We discover that many natural-language prompts can be replaced by corresponding prompts that are unintelligible to humans but that provably elicit similar behavior in language models. We call these prompts "evil twins" because they are obfuscated and
Externí odkaz:
http://arxiv.org/abs/2311.07064
Autor:
Bhattarai, Bibek, Huang, H. Howie
Modern cyber attackers use advanced zero-day exploits, highly targeted spear phishing, and other social engineering techniques to gain access and also use evasion techniques to maintain a prolonged presence within the victim network while working gra
Externí odkaz:
http://arxiv.org/abs/2310.00843
Graph neural networks (GNNs) have been utilized to create multi-layer graph models for a number of cybersecurity applications from fraud detection to software vulnerability analysis. Unfortunately, like traditional neural networks, GNNs also suffer f
Externí odkaz:
http://arxiv.org/abs/2303.14836
Recent pre-trained language models (PLMs) achieved great success on many natural language processing tasks through learning linguistic features and contextualized sentence representation. Since attributes captured in stacked layers of PLMs are not cl
Externí odkaz:
http://arxiv.org/abs/2209.05972
Autor:
Ding, Ding, Huang, H. Howie
This work investigates the problem of multi-agents trajectory prediction. Prior approaches lack of capability of capturing fine-grained dependencies among coordinated agents. In this paper, we propose a spatial-temporal trajectory prediction approach
Externí odkaz:
http://arxiv.org/abs/2012.10531
Analysis of cyber relevant data has become an area of increasing focus. As larger percentages of businesses and governments begin to understand the implications of cyberattacks, the impetus for better cybersecurity solutions has increased. Unfortunat
Externí odkaz:
http://arxiv.org/abs/2008.09192
Autor:
Liu, Hang, Huang, H. Howie
With high computation power and memory bandwidth, graphics processing units (GPUs) lend themselves to accelerate data-intensive analytics, especially when such applications fit the single instruction multiple data (SIMD) model. However, graph algorit
Externí odkaz:
http://arxiv.org/abs/1812.04070
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