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[Submitted on 24 Jul 2024] View a PDF of the paper titled A Large Encoder-Decoder Family of Foundation Models For Chemical Language, by Eduardo Soares and 5 other authors View PDF Abstract:Large-scale pre-training methodologies for chemical language models represent a breakthrough in cheminformatics. These methods excel in tasks such as property prediction and molecule generation by learning contextualized representations of input tokens through self-supervised learning on large unlabeled corpora. Typically, this involves pre-training on unlabeled data followed by fine-tuning on specific tasks, reducing dependence on annotated datasets and broadening chemical language representation understanding. This paper introduces a large encoder-decoder chemical…

arXiv:2407.19225v1 Announce Type: new Abstract: The requirement for 3D content is growing as AR/VR application emerges. At the same time, 3D modelling is only available for skillful experts, because traditional methods like Computer-Aided Design (CAD) are often too labor-intensive and skill-demanding, making it challenging for novice users. Our proposed method, Magic3DSketch, employs a novel technique that encodes sketches to predict a 3D mesh, guided by text descriptions and leveraging external prior knowledge obtained through text and language-image pre-training. The integration of language-image pre-trained neural networks complements the sparse and ambiguous nature of single-view sketch inputs. Our method is also more…

[Submitted on 29 Jul 2024] View a PDF of the paper titled What if Red Can Talk? Dynamic Dialogue Generation Using Large Language Models, by Navapat Nananukul and 1 other authors View PDF Abstract:Role-playing games (RPGs) provide players with a rich, interactive world to explore. Dialogue serves as the primary means of communication between developers and players, manifesting in various forms such as guides, NPC interactions, and storytelling. While most games rely on written scripts to define the main story and character personalities, player immersion can be significantly enhanced through casual interactions between characters. With the advent of large language…

[Submitted on 24 Jul 2024] View a PDF of the paper titled Accelerating the Low-Rank Decomposed Models, by Habib Hajimolahoseini and 3 other authors View PDF Abstract:Tensor decomposition is a mathematically supported technique for data compression. It consists of applying some kind of a Low Rank Decomposition technique on the tensors or matrices in order to reduce the redundancy of the data. However, it is not a popular technique for compressing the AI models duo to the high number of new layers added to the architecture after decomposition. Although the number of parameters could shrink significantly, it could result in…