CybAI news

arXiv:2406.17837v1 Announce Type: new Abstract: Recent works have shown that transformers can solve contextual reasoning tasks by internally executing computational graphs called circuits. Circuits often use attention to logically match information from subspaces of the representation, e.g. using position-in-sequence to identify the previous token. In this work, we consider a semantic subspace to be any independent subspace of the latent representation that can fully determine an attention distribution. We show that Pre-Norm, the placement of normalisation layer used by state-of-the-art transformers, violates this ability unless the model learns a strict representation structure of orthogonal spheres. This is because it causes…

arXiv:2406.17988v1 Announce Type: new Abstract: Reconstructing 3D hand-face interactions with deformations from a single image is a challenging yet crucial task with broad applications in AR, VR, and gaming. The challenges stem from self-occlusions during single-view hand-face interactions, diverse spatial relationships between hands and face, complex deformations, and the ambiguity of the single-view setting. The first and only method for hand-face interaction recovery, Decaf, introduces a global fitting optimization guided by contact and deformation estimation networks trained on studio-collected data with 3D annotations. However, Decaf suffers from a time-consuming optimization process and limited generalization capability due to its reliance on…

arXiv:2406.17901v1 Announce Type: new Abstract: Language identification is used as the first step in many data collection and crawling efforts because it allows us to sort online text into language-specific buckets. However, many modern languages, such as Konkani, Kashmiri, Punjabi etc., are synchronically written in several scripts. Moreover, languages with different writing systems do not share significant lexical, semantic, and syntactic properties in neural representation spaces, which is a disadvantage for closely related languages and low-resource languages, especially those from the Indian Subcontinent. To counter this, we propose learning script-agnostic representations using several different experimental strategies (upscaling, flattening, and script…

(YIUCHEUNG/Shutterstock) At this year’s Datadog annual conference, DASH 2024, the company made a string of key announcements, including new platforms and product updates designed to enable teams to observe their environment, secure the workloads and infrastructure, and fix issues before they affect customers.  The conference took place on June 25–26th, 2024, at the North Javits Center in New York City.  One of the key announcements is the general availability of the Datadog LLM Observability platform, which is designed to allow AI application developers and machine learning (ML) engineers to monitor, improve, and secure LLM applications.  LLM agents have helped organizations to…