Zobrazeno 1 - 10
of 10 549
pro vyhledávání: '"A. Raff"'
Autor:
Mohammadi, Seyedali, Raff, Edward, Malekar, Jinendra, Palit, Vedant, Ferraro, Francis, Gaur, Manas
Language Models (LMs) are being proposed for mental health applications where the heightened risk of adverse outcomes means predictive performance may not be a sufficient litmus test of a model's utility in clinical practice. A model that can be trus
Externí odkaz:
http://arxiv.org/abs/2406.12058
While distributed training is often viewed as a solution to optimizing linear models on increasingly large datasets, inter-machine communication costs of popular distributed approaches can dominate as data dimensionality increases. Recent work on non
Externí odkaz:
http://arxiv.org/abs/2406.01753
Autor:
Liu, Chang, Saul, Rebecca, Sun, Yihao, Raff, Edward, Fuchs, Maya, Pantano, Townsend Southard, Holt, James, Micinski, Kristopher
Binary code is pervasive, and binary analysis is a key task in reverse engineering, malware classification, and vulnerability discovery. Unfortunately, while there exist large corpuses of malicious binaries, obtaining high-quality corpuses of benign
Externí odkaz:
http://arxiv.org/abs/2405.03991
Autor:
Tilwani, Deepa, Saxena, Yash, Mohammadi, Ali, Raff, Edward, Sheth, Amit, Parthasarathy, Srinivasan, Gaur, Manas
Automatic citation generation for sentences in a document or report is paramount for intelligence analysts, cybersecurity, news agencies, and education personnel. In this research, we investigate whether large language models (LLMs) are capable of ge
Externí odkaz:
http://arxiv.org/abs/2405.02228
Autor:
Sachtler, Arne, Calzolari, Davide, Raff, Maximilian, Schmidt, Annika, Wotte, Yannik P., Della Santina, Cosimo, Remy, C. David, Albu-Schäffer, Alin
We identify the nonlinear normal modes spawning from the stable equilibrium of a double pendulum under gravity, and we establish their connection to homoclinic orbits through the unstable upright position as energy increases. This result is exploited
Externí odkaz:
http://arxiv.org/abs/2404.08478
Linear models are ubiquitous in data science, but are particularly prone to overfitting and data memorization in high dimensions. To guarantee the privacy of training data, differential privacy can be used. Many papers have proposed optimization tech
Externí odkaz:
http://arxiv.org/abs/2404.01141
Malware detection is an interesting and valuable domain to work in because it has significant real-world impact and unique machine-learning challenges. We investigate existing long-range techniques and benchmarks and find that they're not very suitab
Externí odkaz:
http://arxiv.org/abs/2403.17978
Autor:
Raff, Maximilian, Remy, C. David
In autonomous differential equations where a single first integral is present, periodic orbits are well-known to belong to one-parameter families, parameterized by the first integral's values. This paper shows that this characteristic extends to a br
Externí odkaz:
http://arxiv.org/abs/2402.06502
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. However, academic re
Externí odkaz:
http://arxiv.org/abs/2312.15813
While deep learning has enjoyed significant success in computer vision tasks over the past decade, many shortcomings still exist from a Cognitive Science (CogSci) perspective. In particular, the ability to subitize, i.e., quickly and accurately ident
Externí odkaz:
http://arxiv.org/abs/2312.15310