Create accountLogin

Recent searches

No recent searches

Search options

Only available when logged in.
sigmoid.social is one of the many independent Mastodon servers you can use to participate in the fediverse.
A social space for people researching, working with, or just interested in AI!

Administered by:

Server stats:

664
active users

sigmoid.social: AboutProfiles directoryPrivacy policy

Mastodon: AboutGet the appKeyboard shortcutsView source codev4.3.7

#machine

1 post1 participant0 posts today
Continued thread
Public
Nonilex

The #law also refers to other elements that can’t be attributed to a #machine, Millet said, when it requires an #author’s “signature” to transfer, talks about co-authors’ “intention,” & refers to an author’s “nationality or domicile.”

“Machines do not have property, traditional human lifespans, family members, domiciles, nationalities, mentes reae, or signatures,” Millet said, later adding that “the #Copyright Act makes no sense if an ‘author’ is not a human being.”

#AI#IntellectualProperty
Public
Samuel Hess
Rückblick | Review 07/03/2019
Wir sind auf dem Weg nach Süden auf Andøya an dieser eingeschneiten Maschine vorbeigefahren und ich dachte (wie öfters): "das wäre ein nettes Fotosujet". Aber gerade jedes Mal kann ich bei diesem Gedanken nicht anhalten und mit etwas Wehmut sind wir weiter gefahren - bis nach einigen Kilometern die Strasse gesperrt war. Meistens sind solche Überraschungen ärgerlich, hier hatte ich aber die Gelegenheit auf dem Rückweg die Szenerie doch noch abzulichten.

#Nikon D750 | 19mm | f/10 | 1/640s | 07/03/2019

#hess_photography #photography #fotografie #abandoned #blue #bluesky #decay #deserted #landscape #machine #Norway #rusty #snow #snowscape #landscapephotography #landschaftsfotografie
Fotografie einer eingeschneiten Fördermaschine.
#landschaftsfotografie
Public
Hacker News

Enhancing Frame Detection with Retrieval Augmented Generation — arxiv.org/abs/2502.12210
#HackerNews #Enhancing #Frame #Detection #Retrieval #Augmented #Generation #AI #Research #Machine #Learning

arXiv.orgEnhancing Frame Detection with Retrieval Augmented GenerationRecent advancements in Natural Language Processing have significantly improved the extraction of structured semantic representations from unstructured text, especially through Frame Semantic Role Labeling (FSRL). Despite this progress, the potential of Retrieval-Augmented Generation (RAG) models for frame detection remains under-explored. In this paper, we present the first RAG-based approach for frame detection called RCIF (Retrieve Candidates and Identify Frames). RCIF is also the first approach to operate without the need for explicit target span and comprises three main stages: (1) generation of frame embeddings from various representations ; (2) retrieval of candidate frames given an input text; and (3) identification of the most suitable frames. We conducted extensive experiments across multiple configurations, including zero-shot, few-shot, and fine-tuning settings. Our results show that our retrieval component significantly reduces the complexity of the task by narrowing the search space thus allowing the frame identifier to refine and complete the set of candidates. Our approach achieves state-of-the-art performance on FrameNet 1.5 and 1.7, demonstrating its robustness in scenarios where only raw text is provided. Furthermore, we leverage the structured representation obtained through this method as a proxy to enhance generalization across lexical variations in the task of translating natural language questions into SPARQL queries.
ExploreLive feeds
About