This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
The paper will be presented at the 2025 Conference of the Nations of the Americas Chapter of the Association for ComputationalLinguistics (NAACL2025). The funding will support both computational resources for working with frontier AI models and personnel to assist with Rudners research.
Emergence and History of LLMs Artificial NeuralNetworks (ANNs) and Rule-based Models The foundation of these ComputationalLinguistics models (CL) dates back to the 1940s when Warren McCulloch and Walter Pitts laid the groundwork for AI. Both contain self-attention mechanisms and feed-forward neuralnetworks.
DeepL DeepL is a Cologne-based startup that utilises deep neuralnetworks to build state-of-the-art machine translation service. The company utilises algorithms for targeted data collection and semantic analysis to extract fine-grained information from various types of customer feedback and market opinions.
Computation Function We consider a neuralnetwork $f_theta$ as a composition of functions $f_{theta_1} odot f_{theta_2} odot ldots odot f_{theta_l}$, each with their own set of parameters $theta_i$. d) Hypernetwork: A small separate neuralnetwork generates modular parameters conditioned on metadata.
In Proceedings of the IEEE International Conference on ComputerVision, pp. Distributionally robust neuralnetworks for group shifts: On the importance of regularization for worst-case generalization. In Association for ComputationalLinguistics (ACL), pp. Selective classification for deep neuralnetworks.
In computervision, supervised pre-trained models such as Vision Transformer [2] have been scaled up [3] and self-supervised pre-trained models have started to match their performance [4]. Transactions of the Association for ComputationalLinguistics, 9, 978–994. link] ↩︎ Hendricks, L.
Classifiers based on neuralnetworks are known to be poorly calibrated outside of their training data [3]. 2019 Annual Conference of the North American Chapter of the Association for ComputationalLinguistics. [7] 57th Annual Meeting of the Association for ComputationalLinguistics [9] C. Weigreffe, Y.
We organize all of the trending information in your field so you don't have to. Join 15,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content